---
_id: '12566'
abstract:
- lang: eng
text: "Approximate agreement is one of the few variants of consensus that can be
solved in a wait-free manner in asynchronous systems where processes communicate
by reading and writing to shared memory. In this work, we consider a natural generalisation
of approximate agreement on arbitrary undirected connected graphs. Each process
is given a node of the graph as input and, if non-faulty, must output a node such
that\r\n– all the outputs are within distance 1 of one another, and\r\n– each
output value lies on a shortest path between two input values.\r\nFrom prior work,
it is known that there is no wait-free algorithm among processes for this problem
on any cycle of length , by reduction from 2-set agreement (Castañeda et al.,
2018).\r\n\r\nIn this work, we investigate the solvability of this task on general
graphs. We give a new, direct proof of the impossibility of approximate agreement
on cycles of length , via a generalisation of Sperner's Lemma to convex polygons.
We also extend the reduction from 2-set agreement to a larger class of graphs,
showing that approximate agreement on these graphs is unsolvable. On the positive
side, we present a wait-free algorithm for a different class of graphs, which
properly contains the class of chordal graphs."
acknowledgement: This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No. 805223 ScaleML) and under the Marie Skłodowska-Curie grant
agreement No. 840605 and from the Natural Sciences and Engineering Research Council
of Canada grant RGPIN-2020-04178. Part of this work was done while Faith Ellen was
visiting IST Austria.
article_number: '113733'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Faith
full_name: Ellen, Faith
last_name: Ellen
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
citation:
ama: Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs.
Theoretical Computer Science. 2023;948(2). doi:10.1016/j.tcs.2023.113733
apa: Alistarh, D.-A., Ellen, F., & Rybicki, J. (2023). Wait-free approximate
agreement on graphs. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2023.113733
chicago: Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate
Agreement on Graphs.” Theoretical Computer Science. Elsevier, 2023. https://doi.org/10.1016/j.tcs.2023.113733.
ieee: D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement
on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
ista: Alistarh D-A, Ellen F, Rybicki J. 2023. Wait-free approximate agreement on
graphs. Theoretical Computer Science. 948(2), 113733.
mla: Alistarh, Dan-Adrian, et al. “Wait-Free Approximate Agreement on Graphs.” Theoretical
Computer Science, vol. 948, no. 2, 113733, Elsevier, 2023, doi:10.1016/j.tcs.2023.113733.
short: D.-A. Alistarh, F. Ellen, J. Rybicki, Theoretical Computer Science 948 (2023).
date_created: 2023-02-19T23:00:55Z
date_published: 2023-02-28T00:00:00Z
date_updated: 2023-08-01T13:17:20Z
day: '28'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1016/j.tcs.2023.113733
ec_funded: 1
external_id:
isi:
- '000934262700001'
file:
- access_level: open_access
checksum: b27c5290f2f1500c403494364ee39c9f
content_type: application/pdf
creator: dernst
date_created: 2023-02-20T07:30:20Z
date_updated: 2023-02-20T07:30:20Z
file_id: '12570'
file_name: 2023_TheoreticalCompScience_Alistarh.pdf
file_size: 602333
relation: main_file
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has_accepted_license: '1'
intvolume: ' 948'
isi: 1
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
- _id: 26A5D39A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '840605'
name: Coordination in constrained and natural distributed systems
publication: Theoretical Computer Science
publication_identifier:
issn:
- 0304-3975
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Wait-free approximate agreement on graphs
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 948
year: '2023'
...
---
_id: '13053'
abstract:
- lang: eng
text: 'Deep neural networks (DNNs) often have to be compressed, via pruning and/or
quantization, before they can be deployed in practical settings. In this work
we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization
step in a principled way, in order to produce models whose local loss behavior
is stable under compression operations such as pruning. Thus, dense models trained
via CrAM should be compressible post-training, in a single step, without significant
accuracy loss. Experimental results on standard benchmarks, such as residual networks
for ImageNet classification and BERT models for language modelling, show that
CrAM produces dense models that can be more accurate than the standard SGD/Adam-based
baselines, but which are stable under weight pruning: specifically, we can prune
models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90%
with reasonable (∼1%) accuracy loss, which is competitive with gradual compression
methods. Additionally, CrAM can produce sparse models which perform well for transfer
learning, and it also works for semi-structured 2:4 pruning patterns supported
by GPU hardware. The code for reproducing the results is available at this https
URL .'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "AP, EK, DA received funding from the European Research Council (ERC)
under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant
agreement No 805223 ScaleML). AV acknowledges the support of the French Agence Nationale
de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT). We further
acknowledge the support from the Scientific Service Units (SSU) of ISTA through
resources provided by Scientific Computing (SciComp)-"
article_processing_charge: No
arxiv: 1
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. In: 11th International Conference on Learning Representations .'
apa: 'Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.).
CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning
Representations . Kigali, Rwanda .'
chicago: 'Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert,
and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International
Conference on Learning Representations , n.d.'
ieee: 'E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM:
A Compression-Aware Minimizer,” in 11th International Conference on Learning
Representations , Kigali, Rwanda .'
ista: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. 11th International Conference on Learning Representations . ICLR: International
Conference on Learning Representations.'
mla: 'Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th
International Conference on Learning Representations .'
short: E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International
Conference on Learning Representations , n.d.
conference:
end_date: 2023-05-05
location: 'Kigali, Rwanda '
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-05-23T11:36:18Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-06-01T12:54:45Z
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '2207.14200'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/pdf?id=_eTZBs-yedr
month: '05'
oa: 1
oa_version: Preprint
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: '11th International Conference on Learning Representations '
publication_status: accepted
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
status: public
title: 'CrAM: A Compression-Aware Minimizer'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '13074'
abstract:
- lang: eng
text: "Deep learning has become an integral part of a large number of important
applications, and many of the recent breakthroughs have been enabled by the ability
to train very large models, capable to capture complex patterns and relationships
from the data. At the same time, the massive sizes of modern deep learning models
have made their deployment to smaller devices more challenging; this is particularly
important, as in many applications the users rely on accurate deep learning predictions,
but they only have access to devices with limited memory and compute power. One
solution to this problem is to prune neural networks, by setting as many of their
parameters as possible to zero, to obtain accurate sparse models with lower memory
footprint. Despite the great research progress in obtaining sparse models that
preserve accuracy, while satisfying memory and computational constraints, there
are still many challenges associated with efficiently training sparse models,
as well as understanding their generalization properties.\r\n\r\nThe focus of
this thesis is to investigate how the training process of sparse models can be
made more efficient, and to understand the differences between sparse and dense
models in terms of how well they can generalize to changes in the data distribution.
We first study a method for co-training sparse and dense models, at a lower cost
compared to regular training. With our method we can obtain very accurate sparse
networks, and dense models that can recover the baseline accuracy. Furthermore,
we are able to more easily analyze the differences, at prediction level, between
the sparse-dense model pairs. Next, we investigate the generalization properties
of sparse neural networks in more detail, by studying how well different sparse
models trained on a larger task can adapt to smaller, more specialized tasks,
in a transfer learning scenario. Our analysis across multiple pruning methods
and sparsity levels reveals that sparse models provide features that can transfer
similarly to or better than the dense baseline. However, the choice of the pruning
method plays an important role, and can influence the results when the features
are fixed (linear finetuning), or when they are allowed to adapt to the new task
(full finetuning). Using sparse models with fixed masks for finetuning on new
tasks has an important practical advantage, as it enables training neural networks
on smaller devices. However, one drawback of current pruning methods is that the
entire training cycle has to be repeated to obtain the initial sparse model, for
every sparsity target; in consequence, the entire training process is costly and
also multiple models need to be stored. In the last part of the thesis we propose
a method that can train accurate dense models that are compressible in a single
step, to multiple sparsity levels, without additional finetuning. Our method results
in sparse models that can be competitive with existing pruning methods, and which
can also successfully generalize to new tasks."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
citation:
ama: Peste E-A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074
apa: Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
chicago: Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural
Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074.
ieee: E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute
of Science and Technology Austria, 2023.
ista: Peste E-A. 2023. Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria.
mla: Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
short: E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute
of Science and Technology Austria, 2023.
date_created: 2023-05-23T17:07:53Z
date_published: 2023-05-23T00:00:00Z
date_updated: 2023-08-04T10:33:27Z
day: '23'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
doi: 10.15479/at:ista:13074
ec_funded: 1
file:
- access_level: open_access
checksum: 6b3354968403cb9d48cc5a83611fb571
content_type: application/pdf
creator: epeste
date_created: 2023-05-24T16:11:16Z
date_updated: 2023-05-24T16:11:16Z
file_id: '13087'
file_name: PhD_Thesis_Alexandra_Peste_final.pdf
file_size: 2152072
relation: main_file
success: 1
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checksum: 8d0df94bbcf4db72c991f22503b3fd60
content_type: application/zip
creator: epeste
date_created: 2023-05-24T16:12:59Z
date_updated: 2023-05-24T16:12:59Z
file_id: '13088'
file_name: PhD_Thesis_APeste.zip
file_size: 1658293
relation: source_file
file_date_updated: 2023-05-24T16:12:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '147'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11458'
relation: part_of_dissertation
status: public
- id: '13053'
relation: part_of_dissertation
status: public
- id: '12299'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
title: Efficiency and generalization of sparse neural networks
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '14364'
abstract:
- lang: eng
text: We introduce extension-based proofs, a class of impossibility proofs that
includes valency arguments. They are modelled as an interaction between a prover
and a protocol. Using proofs based on combinatorial topology, it has been shown
that it is impossible to deterministically solve -set agreement among processes
or approximate agreement on a cycle of length 4 among processes in a wait-free
manner in asynchronous models where processes communicate using objects that can
be constructed from shared registers. However, it was unknown whether proofs based
on simpler techniques were possible. We show that these impossibility results
cannot be obtained by extension-based proofs in the iterated snapshot model and,
hence, extension-based proofs are limited in power.
acknowledgement: "We would like to thank Valerie King, Toniann Pitassi, and Michael
Saks for helpful discussions and Shi Hao Liu for his useful feedback.\r\nThis research
was supported by the Natural Science and Engineering Research Council of Canada
under grants RGPIN-2015-05080 and RGPIN-2020-04178, a postgraduate scholarship,
and a postdoctoral fellowship; a University of Toronto postdoctoral fellowship;
the National Science Foundation under grants CCF-1217921, CCF-1301926, CCF-1637385,
CCF-1650596, and IIS-1447786; the U.S. Department of Energy under grant ER26116/DE-SC0008923;
the European Research Council (ERC) under the European Union’s Horizon 2020 research
and innovation programme grant agreement 805223 ScaleML; and the Oracle and Intel
corporations. Some of the work on this paper was done while Faith Ellen was visiting
IST Austria."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: James
full_name: Aspnes, James
last_name: Aspnes
- first_name: Faith
full_name: Ellen, Faith
last_name: Ellen
- first_name: Rati
full_name: Gelashvili, Rati
last_name: Gelashvili
- first_name: Leqi
full_name: Zhu, Leqi
id: a2117c59-cee4-11ed-b9d0-874ecf0f8ac5
last_name: Zhu
citation:
ama: Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs
fail. SIAM Journal on Computing. 2023;52(4):913-944. doi:10.1137/20M1375851
apa: Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2023).
Why extension-based proofs fail. SIAM Journal on Computing. Society for
Industrial and Applied Mathematics. https://doi.org/10.1137/20M1375851
chicago: Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi
Zhu. “Why Extension-Based Proofs Fail.” SIAM Journal on Computing. Society
for Industrial and Applied Mathematics, 2023. https://doi.org/10.1137/20M1375851.
ieee: D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based
proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial
and Applied Mathematics, pp. 913–944, 2023.
ista: Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2023. Why extension-based
proofs fail. SIAM Journal on Computing. 52(4), 913–944.
mla: Alistarh, Dan-Adrian, et al. “Why Extension-Based Proofs Fail.” SIAM Journal
on Computing, vol. 52, no. 4, Society for Industrial and Applied Mathematics,
2023, pp. 913–44, doi:10.1137/20M1375851.
short: D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, SIAM Journal
on Computing 52 (2023) 913–944.
date_created: 2023-09-24T22:01:11Z
date_published: 2023-07-25T00:00:00Z
date_updated: 2023-12-13T12:28:29Z
day: '25'
department:
- _id: DaAl
doi: 10.1137/20M1375851
ec_funded: 1
external_id:
arxiv:
- '1811.01421'
isi:
- '001082972300004'
intvolume: ' 52'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1811.01421
month: '07'
oa: 1
oa_version: Preprint
page: 913-944
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: SIAM Journal on Computing
publication_identifier:
eissn:
- 1095-7111
issn:
- 0097-5397
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
related_material:
record:
- id: '6676'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Why extension-based proofs fail
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 52
year: '2023'
...
---
_id: '14458'
abstract:
- lang: eng
text: 'We show for the first time that large-scale generative pretrained transformer
(GPT) family models can be pruned to at least 50% sparsity in one-shot, without
any retraining, at minimal loss of accuracy. This is achieved via a new pruning
method called SparseGPT, specifically designed to work efficiently and accurately
on massive GPT-family models. We can execute SparseGPT on the largest available
open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach
60% unstructured sparsity with negligible increase in perplexity: remarkably,
more than 100 billion weights from these models can be ignored at inference time.
SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible
with weight quantization approaches. The code is available at: https://github.com/IST-DASLab/sparsegpt.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: The authors gratefully acknowledge funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 programme (grant agreement
No. 805223 ScaleML), as well as experimental support from Eldar Kurtic, and from
the IST Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and
Alois Schloegl.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately
pruned in one-shot. In: Proceedings of the 40th International Conference on
Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.'
apa: 'Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models
can be accurately pruned in one-shot. In Proceedings of the 40th International
Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii,
HI, United States: ML Research Press.'
chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language
Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International
Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.'
ieee: 'E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be
accurately pruned in one-shot,” in Proceedings of the 40th International Conference
on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202,
pp. 10323–10337.'
ista: 'Frantar E, Alistarh D-A. 2023. SparseGPT: Massive language models can be
accurately pruned in one-shot. Proceedings of the 40th International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 202, 10323–10337.'
mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models
Can Be Accurately Pruned in One-Shot.” Proceedings of the 40th International
Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.'
short: E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference
on Machine Learning, ML Research Press, 2023, pp. 10323–10337.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, HI, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2023-07-23
date_created: 2023-10-29T23:01:16Z
date_published: 2023-07-30T00:00:00Z
date_updated: 2023-10-31T09:59:42Z
day: '30'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2301.00774'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.00774
month: '07'
oa: 1
oa_version: Preprint
page: 10323-10337
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 40th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'SparseGPT: Massive language models can be accurately pruned in one-shot'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14460'
abstract:
- lang: eng
text: We provide an efficient implementation of the backpropagation algorithm, specialized
to the case where the weights of the neural network being trained are sparse.
Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and
common layer types (e.g., convolutional or linear). We provide a fast vectorized
implementation on commodity CPUs, and show that it can yield speedups in end-to-end
runtime experiments, both in transfer learning using already-sparsified networks,
and in training sparse networks from scratch. Thus, our results provide the first
support for sparse training on commodity hardware.
acknowledgement: 'We would like to thank Elias Frantar for his valuable assistance
and support at the outset of this project, and the anonymous ICML and SNN reviewers
for very constructive feedback. EI was supported in part by the FWF DK VGSCO, grant
agreement number W1260-N35. DA acknowledges generous ERC support, via Starting Grant
805223 ScaleML. '
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Mahdi
full_name: Nikdan, Mahdi
id: 66374281-f394-11eb-9cf6-869147deecc0
last_name: Nikdan
- first_name: Tommaso
full_name: Pegolotti, Tommaso
last_name: Pegolotti
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient
sparse backpropagation for faster training of neural networks at the edge. In:
Proceedings of the 40th International Conference on Machine Learning. Vol
202. ML Research Press; 2023:26215-26227.'
apa: 'Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A.
(2023). SparseProp: Efficient sparse backpropagation for faster training of neural
networks at the edge. In Proceedings of the 40th International Conference on
Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United
States: ML Research Press.'
chicago: 'Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and
Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster
Training of Neural Networks at the Edge.” In Proceedings of the 40th International
Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.'
ieee: 'M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp:
Efficient sparse backpropagation for faster training of neural networks at the
edge,” in Proceedings of the 40th International Conference on Machine Learning,
Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.'
ista: 'Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp:
Efficient sparse backpropagation for faster training of neural networks at the
edge. Proceedings of the 40th International Conference on Machine Learning. ICML:
International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.'
mla: 'Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster
Training of Neural Networks at the Edge.” Proceedings of the 40th International
Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.'
short: M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings
of the 40th International Conference on Machine Learning, ML Research Press, 2023,
pp. 26215–26227.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, HI, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2023-07-23
date_created: 2023-10-29T23:01:17Z
date_published: 2023-07-30T00:00:00Z
date_updated: 2023-10-31T09:33:51Z
day: '30'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2302.04852'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2302.04852
month: '07'
oa: 1
oa_version: Preprint
page: 26215-26227
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 40th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'SparseProp: Efficient sparse backpropagation for faster training of neural
networks at the edge'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14461'
abstract:
- lang: eng
text: 'Communication-reduction techniques are a popular way to improve scalability
in data-parallel training of deep neural networks (DNNs). The recent emergence
of large language models such as GPT has created the need for new approaches to
exploit data-parallelism. Among these, fully-sharded data parallel (FSDP) training
is highly popular, yet it still encounters scalability bottlenecks. One reason
is that applying compression techniques to FSDP is challenging: as the vast majority
of the communication involves the model’s weights, direct compression alters convergence
and leads to accuracy loss. We present QSDP, a variant of FSDP which supports
both gradient and weight quantization with theoretical guarantees, is simple to
implement and has essentially no overheads. To derive QSDP we prove that a natural
modification of SGD achieves convergence even when we only maintain quantized
weights, and thus the domain over which we train consists of quantized points
and is, therefore, highly non-convex. We validate this approach by training GPT-family
models with up to 1.3 billion parameters on a multi-node cluster. Experiments
show that QSDP preserves model accuracy, while completely removing the communication
bottlenecks of FSDP, providing end-to-end speedups of up to 2.2x.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: The authors gratefully acknowledge funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML), as well as experimental support from the IST
Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and Alois
Schloegl. AV acknowledges the support of the French Agence Nationale de la Recherche
(ANR), under grant ANR-21-CE48-0016 (project COMCOPT), the support of Fondation
Hadamard with a PRMO grant, and the support of CNRS with a CoopIntEER IEA grant
(project ALFRED).
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- first_name: Qi
full_name: Guo, Qi
last_name: Guo
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of
large models with convergence guarantees. In: Proceedings of the 40th International
Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.'
apa: 'Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed
training of large models with convergence guarantees. In Proceedings of the
40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044).
Honolulu, Hawaii, HI, United States: ML Research Press.'
chicago: Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized
Distributed Training of Large Models with Convergence Guarantees.” In Proceedings
of the 40th International Conference on Machine Learning, 202:24020–44. ML
Research Press, 2023.
ieee: I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training
of large models with convergence guarantees,” in Proceedings of the 40th International
Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023,
vol. 202, pp. 24020–24044.
ista: 'Markov I, Vladu A, Guo Q, Alistarh D-A. 2023. Quantized distributed training
of large models with convergence guarantees. Proceedings of the 40th International
Conference on Machine Learning. ICML: International Conference on Machine Learning,
PMLR, vol. 202, 24020–24044.'
mla: Markov, Ilia, et al. “Quantized Distributed Training of Large Models with Convergence
Guarantees.” Proceedings of the 40th International Conference on Machine Learning,
vol. 202, ML Research Press, 2023, pp. 24020–44.
short: I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th
International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, HI, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2023-07-23
date_created: 2023-10-29T23:01:17Z
date_published: 2023-07-30T00:00:00Z
date_updated: 2023-10-31T09:40:45Z
day: '30'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2302.02390'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2302.02390
month: '07'
oa: 1
oa_version: Preprint
page: 24020-24044
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 40th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantized distributed training of large models with convergence guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14771'
abstract:
- lang: eng
text: Pruning—that is, setting a significant subset of the parameters of a neural
network to zero—is one of the most popular methods of model compression. Yet,
several recent works have raised the issue that pruning may induce or exacerbate
bias in the output of the compressed model. Despite existing evidence for this
phenomenon, the relationship between neural network pruning and induced bias is
not well-understood. In this work, we systematically investigate and characterize
this phenomenon in Convolutional Neural Networks for computer vision. First, we
show that it is in fact possible to obtain highly-sparse models, e.g. with less
than 10% remaining weights, which do not decrease in accuracy nor substantially
increase in bias when compared to dense models. At the same time, we also find
that, at higher sparsities, pruned models exhibit higher uncertainty in their
outputs, as well as increased correlations, which we directly link to increased
bias. We propose easy-to-use criteria which, based only on the uncompressed model,
establish whether bias will increase with pruning, and identify the samples most
susceptible to biased predictions post-compression. Our code can be found at https://github.com/IST-DASLab/pruned-vision-model-bias.
acknowledgement: The authors would like to sincerely thank Sara Hooker for her feedback
during the development of this work. EI was supported in part by the FWF DK VGSCO,
grant agreement number W1260-N35. AP and DA acknowledge generous ERC support, via
Starting Grant 805223 ScaleML.
article_processing_charge: No
arxiv: 1
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Iofinova EB, Peste E-A, Alistarh D-A. Bias in pruned vision models: In-depth
analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2023:24364-24373. doi:10.1109/cvpr52729.2023.02334'
apa: 'Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned
vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC,
Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334'
chicago: 'Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias
in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.
https://doi.org/10.1109/cvpr52729.2023.02334.'
ieee: 'E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models:
In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.'
ista: 'Iofinova EB, Peste E-A, Alistarh D-A. 2023. Bias in pruned vision models:
In-depth analysis and countermeasures. 2023 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
24364–24373.'
mla: 'Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis
and Countermeasures.” 2023 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, IEEE, 2023, pp. 24364–73, doi:10.1109/cvpr52729.2023.02334.'
short: E.B. Iofinova, E.-A. Peste, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.
conference:
end_date: 2023-06-24
location: Vancouver, BC, Canada
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2023-06-17
date_created: 2024-01-10T08:42:40Z
date_published: 2023-08-22T00:00:00Z
date_updated: 2024-01-10T08:59:26Z
day: '22'
department:
- _id: DaAl
- _id: ChLa
doi: 10.1109/cvpr52729.2023.02334
ec_funded: 1
external_id:
arxiv:
- '2304.12622'
isi:
- '001062531308068'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.12622
month: '08'
oa: 1
oa_version: Preprint
page: 24364-24373
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eisbn:
- '9798350301298'
eissn:
- 2575-7075
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/pruned-vision-model-bias
status: public
title: 'Bias in pruned vision models: In-depth analysis and countermeasures'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12299'
abstract:
- lang: eng
text: 'Transfer learning is a classic paradigm by which models pretrained on large
“upstream” datasets are adapted to yield good results on “downstream” specialized
datasets. Generally, more accurate models on the “upstream” dataset tend to provide
better transfer accuracy “downstream”. In this work, we perform an in-depth investigation
of this phenomenon in the context of convolutional neural networks (CNNs) trained
on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying
their connections. We consider transfer using unstructured pruned models obtained
by applying several state-of-the-art pruning methods, including magnitude-based,
second-order, regrowth, lottery-ticket, and regularization approaches, in the
context of twelve standard transfer tasks. In a nutshell, our study shows that
sparse models can match or even outperform the transfer performance of dense models,
even at high sparsities, and, while doing so, can lead to significant inference
and even training speedups. At the same time, we observe and analyze significant
differences in the behaviour of different pruning methods. The code is available
at: https://github.com/IST-DASLab/sparse-imagenet-transfer.'
acknowledgement: he authors would like to sincerely thank Christoph Lampert and Nir
Shavit for fruitful discussions during the development of this work, and Eldar Kurtic
for experimental support. EI was supported in part by the FWF DK VGSCO, grant agreement
number W1260-N35, while AP and DA acknowledge generous support by the ERC, via Starting
Grant 805223 ScaleML.
article_processing_charge: No
arxiv: 1
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Iofinova EB, Peste E-A, Kurtz M, Alistarh D-A. How well do sparse ImageNet
models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. Institute of Electrical and Electronics Engineers; 2022:12256-12266.
doi:10.1109/cvpr52688.2022.01195'
apa: 'Iofinova, E. B., Peste, E.-A., Kurtz, M., & Alistarh, D.-A. (2022). How
well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195'
chicago: Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian
Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical
and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
ieee: E. B. Iofinova, E.-A. Peste, M. Kurtz, and D.-A. Alistarh, “How well do sparse
ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision
and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
ista: 'Iofinova EB, Peste E-A, Kurtz M, Alistarh D-A. 2022. How well do sparse ImageNet
models transfer? 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
CVPR: Computer Vision and Pattern Recognition, 12256–12266.'
mla: Iofinova, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?”
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute
of Electrical and Electronics Engineers, 2022, pp. 12256–66, doi:10.1109/cvpr52688.2022.01195.
short: E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Institute of Electrical
and Electronics Engineers, 2022, pp. 12256–12266.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-01-16T10:06:00Z
date_published: 2022-09-27T00:00:00Z
date_updated: 2023-08-04T10:33:28Z
day: '27'
department:
- _id: DaAl
- _id: ChLa
doi: 10.1109/cvpr52688.2022.01195
ec_funded: 1
external_id:
arxiv:
- '2111.13445'
isi:
- '000870759105034'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13445
month: '09'
oa: 1
oa_version: Preprint
page: 12256-12266
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: ' W1260-N35'
name: Vienna Graduate School on Computational Optimization
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: How well do sparse ImageNet models transfer?
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2022'
...
---
_id: '11180'
abstract:
- lang: eng
text: "Designing and implementing efficient parallel priority schedulers is an active
research area. An intriguing proposed design is the Multi-Queue: given n threads
and m ≥ n distinct priority queues, task insertions are performed uniformly at
random, while, to delete, a thread picks two queues uniformly at random, and removes
the observed task of higher priority. This approach scales well, and has probabilistic
rank guarantees: roughly, the rank of each task removed, relative to remaining
tasks in all other queues, is O (m) in expectation. Yet, the performance of this
pattern is below that of well-engineered schedulers, which eschew theoretical
guarantees for practical efficiency.\r\n\r\nWe investigate whether it is possible
to design and implement a Multi-Queue-based task scheduler that is both highly-efficient
and has analytical guarantees. We propose a new variant called the Stealing Multi-Queue
(SMQ), a cache-efficient variant of the Multi-Queue, which leverages both queue
affinity---each thread has a local queue, from which tasks are usually removed;
but, with some probability, threads also attempt to steal higher-priority tasks
from the other queues---and task batching, that is, the processing of several
tasks in a single insert / remove step. These ideas are well-known for task scheduling
without priorities; our theoretical contribution is showing that, despite relaxations,
this design can still provide rank guarantees, which in turn implies bounds on
total work performed. We provide a general SMQ implementation which can surpass
state-of-the-art schedulers such as OBIM and PMOD in terms of performance on popular
graph-processing benchmarks. Notably, the performance improvement comes mainly
from the superior rank guarantees provided by our scheduler, confirming that analytically-reasoned
approaches can still provide performance improvements for priority task scheduling."
acknowledgement: We would like to thank the anonymous reviewers for their useful comments.
This project has received funding from the European Research Council (ERC) under
the European Union’s Horizon 2020 research and innovation programme (grant agreement
No 805223 ScaleML).
article_processing_charge: No
arxiv: 1
author:
- first_name: Anastasiia
full_name: Postnikova, Anastasiia
last_name: Postnikova
- first_name: Nikita
full_name: Koval, Nikita
id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87
last_name: Koval
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art
priority schedulers. In: Proceedings of the 27th ACM SIGPLAN Symposium on Principles
and Practice of Parallel Programming. Association for Computing Machinery;
2022:353-367. doi:10.1145/3503221.3508432'
apa: 'Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues
can be state-of-the-art priority schedulers. In Proceedings of the 27th ACM
SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp.
353–367). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508432'
chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian
Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” In Proceedings
of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming,
353–67. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508432.
ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can
be state-of-the-art priority schedulers,” in Proceedings of the 27th ACM SIGPLAN
Symposium on Principles and Practice of Parallel Programming, Seoul, Republic
of Korea, 2022, pp. 353–367.
ista: 'Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can
be state-of-the-art priority schedulers. Proceedings of the 27th ACM SIGPLAN Symposium
on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles
and Practice of Parallel Programming, 353–367.'
mla: Postnikova, Anastasiia, et al. “Multi-Queues Can Be State-of-the-Art Priority
Schedulers.” Proceedings of the 27th ACM SIGPLAN Symposium on Principles and
Practice of Parallel Programming, Association for Computing Machinery, 2022,
pp. 353–67, doi:10.1145/3503221.3508432.
short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of
the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming,
Association for Computing Machinery, 2022, pp. 353–367.
conference:
end_date: 2022-04-06
location: Seoul, Republic of Korea
name: 'PPoPP: Sympopsium on Principles and Practice of Parallel Programming'
start_date: 2022-04-02
date_created: 2022-04-17T22:01:46Z
date_published: 2022-04-02T00:00:00Z
date_updated: 2023-08-03T06:48:35Z
day: '02'
department:
- _id: DaAl
doi: 10.1145/3503221.3508432
ec_funded: 1
external_id:
arxiv:
- '2109.00657'
isi:
- '000883318200025'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2109.00657'
month: '04'
oa: 1
oa_version: Preprint
page: 353-367
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice
of Parallel Programming
publication_identifier:
isbn:
- '9781450392044'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '13076'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Multi-queues can be state-of-the-art priority schedulers
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2022'
...
---
_id: '11183'
abstract:
- lang: eng
text: "Subgraph detection has recently been one of the most studied problems in
the CONGEST model of distributed computing. In this work, we study the distributed
complexity of problems closely related to subgraph detection, mainly focusing
on induced subgraph detection. The main line of this work presents lower bounds
and parameterized algorithms w.r.t structural parameters of the input graph:\r\n-
On general graphs, we give unconditional lower bounds for induced detection of
cycles and patterns of treewidth 2 in CONGEST. Moreover, by adapting reductions
from centralized parameterized complexity, we prove lower bounds in CONGEST for
detecting patterns with a 4-clique, and for induced path detection conditional
on the hardness of triangle detection in the congested clique.\r\n- On graphs
of bounded degeneracy, we show that induced paths can be detected fast in CONGEST
using techniques from parameterized algorithms, while detecting cycles and patterns
of treewidth 2 is hard.\r\n- On graphs of bounded vertex cover number, we show
that induced subgraph detection is easy in CONGEST for any pattern graph. More
specifically, we adapt a centralized parameterized algorithm for a more general
maximum common induced subgraph detection problem to the distributed setting.
In addition to these induced subgraph detection results, we study various related
problems in the CONGEST and congested clique models, including for multicolored
versions of subgraph-detection-like problems."
acknowledgement: "Amir Nikabadi: Supported by the LABEX MILYON (ANR-10-LABX-0070)
of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007)
operated by the French National Research Agency (ANR). Janne H. Korhonen: Supported
by the European Research Council (ERC) under the European Union’s Horizon 2020 research
and innovation programme (grant agreement No 805223 ScaleML).\r\nWe thank François
Le Gall and Masayuki Miyamoto for sharing their work on lower bounds for induced
subgraph detection [36]."
alternative_title:
- LIPIcs
article_number: '15'
article_processing_charge: No
author:
- first_name: Amir
full_name: Nikabadi, Amir
last_name: Nikabadi
- first_name: Janne
full_name: Korhonen, Janne
id: C5402D42-15BC-11E9-A202-CA2BE6697425
last_name: Korhonen
citation:
ama: 'Nikabadi A, Korhonen J. Beyond distributed subgraph detection: Induced subgraphs,
multicolored problems and graph parameters. In: Bramas Q, Gramoli V, Milani A,
eds. 25th International Conference on Principles of Distributed Systems.
Vol 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022. doi:10.4230/LIPIcs.OPODIS.2021.15'
apa: 'Nikabadi, A., & Korhonen, J. (2022). Beyond distributed subgraph detection:
Induced subgraphs, multicolored problems and graph parameters. In Q. Bramas, V.
Gramoli, & A. Milani (Eds.), 25th International Conference on Principles
of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl -
Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.15'
chicago: 'Nikabadi, Amir, and Janne Korhonen. “Beyond Distributed Subgraph Detection:
Induced Subgraphs, Multicolored Problems and Graph Parameters.” In 25th International
Conference on Principles of Distributed Systems, edited by Quentin Bramas,
Vincent Gramoli, and Alessia Milani, Vol. 217. Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2022. https://doi.org/10.4230/LIPIcs.OPODIS.2021.15.'
ieee: 'A. Nikabadi and J. Korhonen, “Beyond distributed subgraph detection: Induced
subgraphs, multicolored problems and graph parameters,” in 25th International
Conference on Principles of Distributed Systems, Strasbourg, France, 2022,
vol. 217.'
ista: 'Nikabadi A, Korhonen J. 2022. Beyond distributed subgraph detection: Induced
subgraphs, multicolored problems and graph parameters. 25th International Conference
on Principles of Distributed Systems. OPODIS, LIPIcs, vol. 217, 15.'
mla: 'Nikabadi, Amir, and Janne Korhonen. “Beyond Distributed Subgraph Detection:
Induced Subgraphs, Multicolored Problems and Graph Parameters.” 25th International
Conference on Principles of Distributed Systems, edited by Quentin Bramas
et al., vol. 217, 15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022,
doi:10.4230/LIPIcs.OPODIS.2021.15.'
short: A. Nikabadi, J. Korhonen, in:, Q. Bramas, V. Gramoli, A. Milani (Eds.), 25th
International Conference on Principles of Distributed Systems, Schloss Dagstuhl
- Leibniz-Zentrum für Informatik, 2022.
conference:
end_date: 2021-12-15
location: Strasbourg, France
name: OPODIS
start_date: 2021-12-13
date_created: 2022-04-17T22:01:47Z
date_published: 2022-02-01T00:00:00Z
date_updated: 2022-05-02T07:56:35Z
day: '01'
ddc:
- '510'
department:
- _id: DaAl
doi: 10.4230/LIPIcs.OPODIS.2021.15
ec_funded: 1
editor:
- first_name: Quentin
full_name: Bramas, Quentin
last_name: Bramas
- first_name: Vincent
full_name: Gramoli, Vincent
last_name: Gramoli
- first_name: Alessia
full_name: Milani, Alessia
last_name: Milani
file:
- access_level: open_access
checksum: 626551c14de5d4091573200ed0535752
content_type: application/pdf
creator: dernst
date_created: 2022-05-02T07:53:00Z
date_updated: 2022-05-02T07:53:00Z
file_id: '11345'
file_name: 2022_LIPICs_Nikabadi.pdf
file_size: 790396
relation: main_file
success: 1
file_date_updated: 2022-05-02T07:53:00Z
has_accepted_license: '1'
intvolume: ' 217'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 25th International Conference on Principles of Distributed Systems
publication_identifier:
isbn:
- '9783959772198'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Beyond distributed subgraph detection: Induced subgraphs, multicolored problems
and graph parameters'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 217
year: '2022'
...
---
_id: '11184'
abstract:
- lang: eng
text: "Let G be a graph on n nodes. In the stochastic population protocol model,
a collection of n indistinguishable, resource-limited nodes collectively solve
tasks via pairwise interactions. In each interaction, two randomly chosen neighbors
first read each other’s states, and then update their local states. A rich line
of research has established tight upper and lower bounds on the complexity of
fundamental tasks, such as majority and leader election, in this model, when G
is a clique. Specifically, in the clique, these tasks can be solved fast, i.e.,
in n polylog n pairwise interactions, with high probability, using at most polylog
n states per node.\r\nIn this work, we consider the more general setting where
G is an arbitrary regular graph, and present a technique for simulating protocols
designed for fully-connected networks in any connected regular graph. Our main
result is a simulation that is efficient on many interesting graph families: roughly,
the simulation overhead is polylogarithmic in the number of nodes, and quadratic
in the conductance of the graph. As a sample application, we show that, in any
regular graph with conductance φ, both leader election and exact majority can
be solved in φ^{-2} ⋅ n polylog n pairwise interactions, with high probability,
using at most φ^{-2} ⋅ polylog n states per node. This shows that there are fast
and space-efficient population protocols for leader election and exact majority
on graphs with good expansion properties. We believe our results will prove generally
useful, as they allow efficient technology transfer between the well-mixed (clique)
case, and the under-explored spatial setting."
acknowledgement: "Dan Alistarh: This project has received funding from the European
Research Council (ERC)\r\nunder the European Union’s Horizon 2020 research and innovation
programme (grant agreement No.805223 ScaleML).\r\nJoel Rybicki: This project has
received from the European Union’s Horizon 2020 research and\r\ninnovation programme
under the Marie Skłodowska-Curie grant agreement No. 840605.\r\nAcknowledgements
We grateful to Giorgi Nadiradze for pointing out a generalisation of the phase clock
construction to non-regular graphs. We also thank anonymous reviewers for their
useful comments on earlier versions of this manuscript."
alternative_title:
- LIPIcs
article_number: '14'
article_processing_charge: No
arxiv: 1
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Rati
full_name: Gelashvili, Rati
last_name: Gelashvili
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
citation:
ama: 'Alistarh D-A, Gelashvili R, Rybicki J. Fast graphical population protocols.
In: Bramas Q, Gramoli V, Milani A, eds. 25th International Conference on Principles
of Distributed Systems. Vol 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik;
2022. doi:10.4230/LIPIcs.OPODIS.2021.14'
apa: 'Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2022). Fast graphical
population protocols. In Q. Bramas, V. Gramoli, & A. Milani (Eds.), 25th
International Conference on Principles of Distributed Systems (Vol. 217).
Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14'
chicago: Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical
Population Protocols.” In 25th International Conference on Principles of Distributed
Systems, edited by Quentin Bramas, Vincent Gramoli, and Alessia Milani, Vol.
217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14.
ieee: D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Fast graphical population
protocols,” in 25th International Conference on Principles of Distributed Systems,
Strasbourg, France, 2022, vol. 217.
ista: Alistarh D-A, Gelashvili R, Rybicki J. 2022. Fast graphical population protocols.
25th International Conference on Principles of Distributed Systems. OPODIS, LIPIcs,
vol. 217, 14.
mla: Alistarh, Dan-Adrian, et al. “Fast Graphical Population Protocols.” 25th
International Conference on Principles of Distributed Systems, edited by Quentin
Bramas et al., vol. 217, 14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2022, doi:10.4230/LIPIcs.OPODIS.2021.14.
short: D.-A. Alistarh, R. Gelashvili, J. Rybicki, in:, Q. Bramas, V. Gramoli, A.
Milani (Eds.), 25th International Conference on Principles of Distributed Systems,
Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
conference:
end_date: 2021-12-15
location: Strasbourg, France
name: OPODIS
start_date: 2021-12-13
date_created: 2022-04-17T22:01:47Z
date_published: 2022-02-01T00:00:00Z
date_updated: 2022-05-02T08:09:39Z
day: '01'
ddc:
- '510'
department:
- _id: DaAl
doi: 10.4230/LIPIcs.OPODIS.2021.14
ec_funded: 1
editor:
- first_name: Quentin
full_name: Bramas, Quentin
last_name: Bramas
- first_name: Vincent
full_name: Gramoli, Vincent
last_name: Gramoli
- first_name: Alessia
full_name: Milani, Alessia
last_name: Milani
external_id:
arxiv:
- '2102.08808'
file:
- access_level: open_access
checksum: 2c7c982174c6f98c4ca6e92539d15086
content_type: application/pdf
creator: dernst
date_created: 2022-05-02T08:06:33Z
date_updated: 2022-05-02T08:06:33Z
file_id: '11346'
file_name: 2022_LIPICs_Alistarh.pdf
file_size: 959406
relation: main_file
success: 1
file_date_updated: 2022-05-02T08:06:33Z
has_accepted_license: '1'
intvolume: ' 217'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
- _id: 26A5D39A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '840605'
name: Coordination in constrained and natural distributed systems
publication: 25th International Conference on Principles of Distributed Systems
publication_identifier:
isbn:
- '9783959772198'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Fast graphical population protocols
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 217
year: '2022'
...
---
_id: '11844'
abstract:
- lang: eng
text: "In the stochastic population protocol model, we are given a connected graph
with n nodes, and in every time step, a scheduler samples an edge of the graph
uniformly at random and the nodes connected by this edge interact. A fundamental
task in this model is stable leader election, in which all nodes start in an identical
state and the aim is to reach a configuration in which (1) exactly one node is
elected as leader and (2) this node remains as the unique leader no matter what
sequence of interactions follows. On cliques, the complexity of this problem has
recently been settled: time-optimal protocols stabilize in Θ(n log n) expected
steps using Θ(log log n) states, whereas protocols that use O(1) states require
Θ(n2) expected steps.\r\n\r\nIn this work, we investigate the complexity of stable
leader election on general graphs. We provide the first non-trivial time lower
bounds for leader election on general graphs, showing that, when moving beyond
cliques, the complexity landscape of leader election becomes very diverse: the
time required to elect a leader can range from O(1) to Θ(n3) expected steps. On
the upper bound side, we first observe that there exists a protocol that is time-optimal
on many graph families, but uses polynomially-many states. In contrast, we give
a near-time-optimal protocol that uses only O(log2n) states that is at most a
factor log n slower. Finally, we show that the constant-state protocol of Beauquier
et al. [OPODIS 2013] is at most a factor n log n slower than the fast polynomial-state
protocol. Moreover, among constant-state protocols, this protocol has near-optimal
average case complexity on dense random graphs."
acknowledgement: We thank the anonymous reviewers for their helpful comments. We gratefully
acknowledge funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (grant agreement No 805223
ScaleML).
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
- first_name: Sasha
full_name: Voitovych, Sasha
last_name: Voitovych
citation:
ama: 'Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population
protocols on graphs. In: Proceedings of the Annual ACM Symposium on Principles
of Distributed Computing. Association for Computing Machinery; 2022:246-256.
doi:10.1145/3519270.3538435'
apa: 'Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2022). Near-optimal leader
election in population protocols on graphs. In Proceedings of the Annual ACM
Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy:
Association for Computing Machinery. https://doi.org/10.1145/3519270.3538435'
chicago: Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal
Leader Election in Population Protocols on Graphs.” In Proceedings of the Annual
ACM Symposium on Principles of Distributed Computing, 246–56. Association
for Computing Machinery, 2022. https://doi.org/10.1145/3519270.3538435.
ieee: D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election
in population protocols on graphs,” in Proceedings of the Annual ACM Symposium
on Principles of Distributed Computing, Salerno, Italy, 2022, pp. 246–256.
ista: 'Alistarh D-A, Rybicki J, Voitovych S. 2022. Near-optimal leader election
in population protocols on graphs. Proceedings of the Annual ACM Symposium on
Principles of Distributed Computing. PODC: Symposium on Principles of Distributed
Computing, 246–256.'
mla: Alistarh, Dan-Adrian, et al. “Near-Optimal Leader Election in Population Protocols
on Graphs.” Proceedings of the Annual ACM Symposium on Principles of Distributed
Computing, Association for Computing Machinery, 2022, pp. 246–56, doi:10.1145/3519270.3538435.
short: D.-A. Alistarh, J. Rybicki, S. Voitovych, in:, Proceedings of the Annual
ACM Symposium on Principles of Distributed Computing, Association for Computing
Machinery, 2022, pp. 246–256.
conference:
end_date: 2022-07-29
location: Salerno, Italy
name: 'PODC: Symposium on Principles of Distributed Computing'
start_date: 2022-07-25
date_created: 2022-08-14T22:01:46Z
date_published: 2022-07-21T00:00:00Z
date_updated: 2023-06-14T12:06:01Z
day: '21'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1145/3519270.3538435
ec_funded: 1
external_id:
arxiv:
- '2205.12597'
file:
- access_level: open_access
checksum: 4c6b29172b8e355b4fbc364a2e0827b2
content_type: application/pdf
creator: cchlebak
date_created: 2022-08-16T08:05:15Z
date_updated: 2022-08-16T08:05:15Z
file_id: '11854'
file_name: 2022_PODC_Alistarh.pdf
file_size: 1593474
relation: main_file
success: 1
file_date_updated: 2022-08-16T08:05:15Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 246-256
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the Annual ACM Symposium on Principles of Distributed
Computing
publication_identifier:
isbn:
- '9781450392624'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Near-optimal leader election in population protocols on graphs
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13147'
abstract:
- lang: eng
text: "We investigate fast and communication-efficient algorithms for the classic
problem of minimizing a sum of strongly convex and smooth functions that are distributed
among n\r\n different nodes, which can communicate using a limited number of bits.
Most previous communication-efficient approaches for this problem are limited
to first-order optimization, and therefore have \\emph{linear} dependence on the
condition number in their communication complexity. We show that this dependence
is not inherent: communication-efficient methods can in fact have sublinear dependence
on the condition number. For this, we design and analyze the first communication-efficient
distributed variants of preconditioned gradient descent for Generalized Linear
Models, and for Newton’s method. Our results rely on a new technique for quantizing
both the preconditioner and the descent direction at each step of the algorithms,
while controlling their convergence rate. We also validate our findings experimentally,
showing faster convergence and reduced communication relative to previous methods."
acknowledgement: The authors would like to thank Janne Korhonen, Aurelien Lucchi,
Celestine MendlerDunner and Antonio Orvieto for helpful discussions. FA ¨and DA
were supported during this work by the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant agreement
No 805223 ScaleML). PD was supported by the European Union’s Horizon 2020 programme
under the Marie Skłodowska-Curie grant agreement No. 754411.
article_processing_charge: No
arxiv: 1
author:
- first_name: Foivos
full_name: Alimisis, Foivos
last_name: Alimisis
- first_name: Peter
full_name: Davies, Peter
id: 11396234-BB50-11E9-B24C-90FCE5697425
last_name: Davies
orcid: 0000-0002-5646-9524
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization
with quantized preconditioners. In: Proceedings of the 38th International Conference
on Machine Learning. Vol 139. ML Research Press; 2021:196-206.'
apa: 'Alimisis, F., Davies, P., & Alistarh, D.-A. (2021). Communication-efficient
distributed optimization with quantized preconditioners. In Proceedings of
the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206).
Virtual: ML Research Press.'
chicago: Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient
Distributed Optimization with Quantized Preconditioners.” In Proceedings of
the 38th International Conference on Machine Learning, 139:196–206. ML Research
Press, 2021.
ieee: F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed
optimization with quantized preconditioners,” in Proceedings of the 38th International
Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
ista: Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed
optimization with quantized preconditioners. Proceedings of the 38th International
Conference on Machine Learning. International Conference on Machine Learning vol.
139, 196–206.
mla: Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization
with Quantized Preconditioners.” Proceedings of the 38th International Conference
on Machine Learning, vol. 139, ML Research Press, 2021, pp. 196–206.
short: F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International
Conference on Machine Learning, ML Research Press, 2021, pp. 196–206.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-06-18T22:00:48Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-06-19T10:44:38Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2102.07214'
file:
- access_level: open_access
checksum: 7ec0d59bac268b49c76bf2e036dedd7a
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T10:41:05Z
date_updated: 2023-06-19T10:41:05Z
file_id: '13154'
file_name: 2021_PMLR_Alimisis.pdf
file_size: 429087
relation: main_file
success: 1
file_date_updated: 2023-06-19T10:41:05Z
has_accepted_license: '1'
intvolume: ' 139'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 196-206
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Proceedings of the 38th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
isbn:
- '9781713845065'
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Communication-efficient distributed optimization with quantized preconditioners
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '8286'
abstract:
- lang: eng
text: "We consider the following dynamic load-balancing process: given an underlying
graph G with n nodes, in each step t≥ 0, one unit of load is created, and placed
at a randomly chosen graph node. In the same step, the chosen node picks a random
neighbor, and the two nodes balance their loads by averaging them. We are interested
in the expected gap between the minimum and maximum loads at nodes as the process
progresses, and its dependence on n and on the graph structure. Variants of the
above graphical balanced allocation process have been studied previously by Peres,
Talwar, and Wieder [Peres et al., 2015], and by Sauerwald and Sun [Sauerwald and
Sun, 2015]. These authors left as open the question of characterizing the gap
in the case of cycle graphs in the dynamic case, where weights are created during
the algorithm’s execution. For this case, the only known upper bound is of \U0001D4AA(n
log n), following from a majorization argument due to [Peres et al., 2015], which
analyzes a related graphical allocation process. In this paper, we provide an
upper bound of \U0001D4AA (√n log n) on the expected gap of the above process
for cycles of length n. We introduce a new potential analysis technique, which
enables us to bound the difference in load between k-hop neighbors on the cycle,
for any k ≤ n/2. We complement this with a \"gap covering\" argument, which bounds
the maximum value of the gap by bounding its value across all possible subsets
of a certain structure, and recursively bounding the gaps within each subset.
We provide analytical and experimental evidence that our upper bound on the gap
is tight up to a logarithmic factor. "
acknowledgement: The authors sincerely thank Thomas Sauerwald and George Giakkoupis
for insightful discussions, and Mohsen Ghaffari, Yuval Peres, and Udi Wieder for
feedback on earlier versions of this draft. We also thank the ICALP anonymous reviewers
for their very useful comments. Open access funding provided by Institute of Science
and Technology (IST Austria). Funding was provided by European Research Council
(Grant No. PR1042ERC01).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
- first_name: Amirmojtaba
full_name: Sabour, Amirmojtaba
id: bcc145fd-e77f-11ea-ae8b-80d661dbff67
last_name: Sabour
citation:
ama: Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles.
Algorithmica. 2021. doi:10.1007/s00453-021-00905-9
apa: 'Alistarh, D.-A., Nadiradze, G., & Sabour, A. (2021). Dynamic averaging
load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer
Nature. https://doi.org/10.1007/s00453-021-00905-9'
chicago: Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic
Averaging Load Balancing on Cycles.” Algorithmica. Springer Nature, 2021.
https://doi.org/10.1007/s00453-021-00905-9.
ieee: D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing
on cycles,” Algorithmica. Springer Nature, 2021.
ista: Alistarh D-A, Nadiradze G, Sabour A. 2021. Dynamic averaging load balancing
on cycles. Algorithmica.
mla: Alistarh, Dan-Adrian, et al. “Dynamic Averaging Load Balancing on Cycles.”
Algorithmica, Springer Nature, 2021, doi:10.1007/s00453-021-00905-9.
short: D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica (2021).
conference:
end_date: 2020-07-11
location: Virtual, Online; Germany
name: 'ICALP: International Colloquium on Automata, Languages, and Programming '
start_date: 2020-07-08
date_created: 2020-08-24T06:24:04Z
date_published: 2021-12-24T00:00:00Z
date_updated: 2024-03-05T07:35:53Z
day: '24'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1007/s00453-021-00905-9
ec_funded: 1
external_id:
arxiv:
- '2003.09297'
isi:
- '000734004600001'
file:
- access_level: open_access
checksum: 21169b25b0c8e17b21e12af22bff9870
content_type: application/pdf
creator: cchlebak
date_created: 2021-12-27T10:36:40Z
date_updated: 2021-12-27T10:36:40Z
file_id: '10577'
file_name: 2021_Algorithmica_Alistarh.pdf
file_size: 525950
relation: main_file
success: 1
file_date_updated: 2021-12-27T10:36:40Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: Algorithmica
publication_identifier:
eissn:
- 1432-0541
issn:
- 0178-4617
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: earlier_version
url: https://doi.org/10.4230/LIPIcs.ICALP.2020.7
record:
- id: '15077'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Dynamic averaging load balancing on cycles
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '8723'
abstract:
- lang: eng
text: Deep learning at scale is dominated by communication time. Distributing samples
across nodes usually yields the best performance, but poses scaling challenges
due to global information dissemination and load imbalance across uneven sample
lengths. State-of-the-art decentralized optimizers mitigate the problem, but require
more iterations to achieve the same accuracy as their globally-communicating counterparts.
We present Wait-Avoiding Group Model Averaging (WAGMA) SGD, a wait-avoiding stochastic
optimizer that reduces global communication via subgroup weight exchange. The
key insight is a combination of algorithmic changes to the averaging scheme and
the use of a group allreduce operation. We prove the convergence of WAGMA-SGD,
and empirically show that it retains convergence rates similar to Allreduce-SGD.
For evaluation, we train ResNet-50 on ImageNet; Transformer for machine translation;
and deep reinforcement learning for navigation at scale. Compared with state-of-the-art
decentralized SGD variants, WAGMA-SGD significantly improves training throughput
(e.g., 2.1× on 1,024 GPUs for reinforcement learning), and achieves the fastest
time-to-solution (e.g., the highest score using the shortest training time for
Transformer).
acknowledgement: "This project has received funding from the European Research Council
(ERC) under the European Union’s Hori-\r\nzon 2020 programme under Grant DAPP, Grant
678880; EPi-GRAM-HS, Grant 801039; and ERC Starting Grant ScaleML, Grant 805223.
The work of Tal Ben-Nun is supported by the Swiss National Science Foundation (Ambizione
Project No. 185778). The work of Nikoli Dryden is supported by the ETH Postdoctoral
Fellowship. The authors would like to thank the Swiss National Supercomputing Center
for providing the computing resources and technical support."
article_number: '9271898'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Shigang
full_name: Li, Shigang
last_name: Li
- first_name: Tal Ben-Nun
full_name: Tal Ben-Nun, Tal Ben-Nun
last_name: Tal Ben-Nun
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
- first_name: Salvatore Di
full_name: Girolamo, Salvatore Di
last_name: Girolamo
- first_name: Nikoli
full_name: Dryden, Nikoli
last_name: Dryden
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
citation:
ama: Li S, Tal Ben-Nun TB-N, Nadiradze G, et al. Breaking (global) barriers in parallel
stochastic optimization with wait-avoiding group averaging. IEEE Transactions
on Parallel and Distributed Systems. 2021;32(7). doi:10.1109/TPDS.2020.3040606
apa: Li, S., Tal Ben-Nun, T. B.-N., Nadiradze, G., Girolamo, S. D., Dryden, N.,
Alistarh, D.-A., & Hoefler, T. (2021). Breaking (global) barriers in parallel
stochastic optimization with wait-avoiding group averaging. IEEE Transactions
on Parallel and Distributed Systems. IEEE. https://doi.org/10.1109/TPDS.2020.3040606
chicago: Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo,
Nikoli Dryden, Dan-Adrian Alistarh, and Torsten Hoefler. “Breaking (Global) Barriers
in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging.” IEEE
Transactions on Parallel and Distributed Systems. IEEE, 2021. https://doi.org/10.1109/TPDS.2020.3040606.
ieee: S. Li et al., “Breaking (global) barriers in parallel stochastic optimization
with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed
Systems, vol. 32, no. 7. IEEE, 2021.
ista: Li S, Tal Ben-Nun TB-N, Nadiradze G, Girolamo SD, Dryden N, Alistarh D-A,
Hoefler T. 2021. Breaking (global) barriers in parallel stochastic optimization
with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed
Systems. 32(7), 9271898.
mla: Li, Shigang, et al. “Breaking (Global) Barriers in Parallel Stochastic Optimization
with Wait-Avoiding Group Averaging.” IEEE Transactions on Parallel and Distributed
Systems, vol. 32, no. 7, 9271898, IEEE, 2021, doi:10.1109/TPDS.2020.3040606.
short: S. Li, T.B.-N. Tal Ben-Nun, G. Nadiradze, S.D. Girolamo, N. Dryden, D.-A.
Alistarh, T. Hoefler, IEEE Transactions on Parallel and Distributed Systems 32
(2021).
date_created: 2020-11-05T15:25:43Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-08-04T11:08:52Z
day: '01'
department:
- _id: DaAl
doi: 10.1109/TPDS.2020.3040606
ec_funded: 1
external_id:
arxiv:
- '2005.00124'
isi:
- '000621405200019'
intvolume: ' 32'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2005.00124
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: IEEE Transactions on Parallel and Distributed Systems
publication_identifier:
issn:
- '10459219'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Breaking (global) barriers in parallel stochastic optimization with wait-avoiding
group averaging
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 32
year: '2021'
...
---
_id: '10217'
abstract:
- lang: eng
text: This paper gives tight logarithmic lower bounds on the solo step complexity
of leader election in an asynchronous shared-memory model with single-writer multi-reader
(SWMR) registers, for both deterministic and randomized obstruction-free algorithms.
The approach extends to lower bounds for deterministic and randomized obstruction-free
algorithms using multi-writer registers under bounded write concurrency, showing
a trade-off between the solo step complexity of a leader election algorithm, and
the worst-case number of stalls incurred by a processor in an execution.
acknowledgement: "Dan Alistarh: Supported in part by the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML). Giorgi Nadiradze: Supported in part by the
European Research Council (ERC) under the European Union’s Horizon 2020 research
and innovation programme (grant agreement No 805223 ScaleML). The authors would
like to thank the DISC anonymous reviewers for their useful\r\nfeedback and comments."
alternative_title:
- LIPIcs
article_number: '4'
article_processing_charge: No
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Rati
full_name: Gelashvili, Rati
last_name: Gelashvili
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
citation:
ama: 'Alistarh D-A, Gelashvili R, Nadiradze G. Lower bounds for shared-memory leader
election under bounded write contention. In: 35th International Symposium on
Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik;
2021. doi:10.4230/LIPIcs.DISC.2021.4'
apa: 'Alistarh, D.-A., Gelashvili, R., & Nadiradze, G. (2021). Lower bounds
for shared-memory leader election under bounded write contention. In 35th International
Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss
Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.4'
chicago: Alistarh, Dan-Adrian, Rati Gelashvili, and Giorgi Nadiradze. “Lower Bounds
for Shared-Memory Leader Election under Bounded Write Contention.” In 35th
International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl
- Leibniz Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.DISC.2021.4.
ieee: D.-A. Alistarh, R. Gelashvili, and G. Nadiradze, “Lower bounds for shared-memory
leader election under bounded write contention,” in 35th International Symposium
on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
ista: 'Alistarh D-A, Gelashvili R, Nadiradze G. 2021. Lower bounds for shared-memory
leader election under bounded write contention. 35th International Symposium on
Distributed Computing. DISC: Distributed Computing, LIPIcs, vol. 209, 4.'
mla: Alistarh, Dan-Adrian, et al. “Lower Bounds for Shared-Memory Leader Election
under Bounded Write Contention.” 35th International Symposium on Distributed
Computing, vol. 209, 4, Schloss Dagstuhl - Leibniz Zentrum für Informatik,
2021, doi:10.4230/LIPIcs.DISC.2021.4.
short: D.-A. Alistarh, R. Gelashvili, G. Nadiradze, in:, 35th International Symposium
on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.
conference:
end_date: 2021-10-08
location: Freiburg, Germany
name: 'DISC: Distributed Computing'
start_date: 2021-10-04
date_created: 2021-11-07T23:01:23Z
date_published: 2021-10-04T00:00:00Z
date_updated: 2022-08-19T07:23:28Z
day: '04'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.4230/LIPIcs.DISC.2021.4
ec_funded: 1
file:
- access_level: open_access
checksum: b4cdc6668c899a601c5e6a96b8ca54d9
content_type: application/pdf
creator: cchlebak
date_created: 2021-11-12T09:33:26Z
date_updated: 2021-11-12T09:33:26Z
file_id: '10277'
file_name: 2021_LIPIcsDISC_Alistarh.pdf
file_size: 706791
relation: main_file
success: 1
file_date_updated: 2021-11-12T09:33:26Z
has_accepted_license: '1'
intvolume: ' 209'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th International Symposium on Distributed Computing
publication_identifier:
isbn:
- 9-783-9597-7210-5
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Lower bounds for shared-memory leader election under bounded write contention
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 209
year: '2021'
...
---
_id: '10219'
abstract:
- lang: eng
text: We show that any algorithm that solves the sinkless orientation problem in
the supported LOCAL model requires Ω(log n) rounds, and this is tight. The supported
LOCAL is at least as strong as the usual LOCAL model, and as a corollary this
also gives a new, short and elementary proof that shows that the round complexity
of the sinkless orientation problem in the deterministic LOCAL model is Ω(log
n).
acknowledgement: "Janne H. Korhonen: Project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (grant agreement No 805223 ScaleML). Ami Paz: We acknowledge the Austrian
Science Fund (FWF) and netIDEE SCIENCE project P 33775-N. Stefan Schmid: Research
supported by the Austrian Science Fund (FWF) project ADVISE, I 4800-N, 2020-2023.\r\n"
alternative_title:
- LIPIcs
article_number: '58'
article_processing_charge: No
arxiv: 1
author:
- first_name: Janne
full_name: Korhonen, Janne
id: C5402D42-15BC-11E9-A202-CA2BE6697425
last_name: Korhonen
- first_name: Ami
full_name: Paz, Ami
last_name: Paz
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
- first_name: Stefan
full_name: Schmid, Stefan
last_name: Schmid
- first_name: Jukka
full_name: Suomela, Jukka
last_name: Suomela
citation:
ama: 'Korhonen J, Paz A, Rybicki J, Schmid S, Suomela J. Brief announcement: Sinkless
orientation is hard also in the supported LOCAL model. In: 35th International
Symposium on Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz Zentrum
für Informatik; 2021. doi:10.4230/LIPIcs.DISC.2021.58'
apa: 'Korhonen, J., Paz, A., Rybicki, J., Schmid, S., & Suomela, J. (2021).
Brief announcement: Sinkless orientation is hard also in the supported LOCAL model.
In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg,
Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.58'
chicago: 'Korhonen, Janne, Ami Paz, Joel Rybicki, Stefan Schmid, and Jukka Suomela.
“Brief Announcement: Sinkless Orientation Is Hard Also in the Supported LOCAL
Model.” In 35th International Symposium on Distributed Computing, Vol.
209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.DISC.2021.58.'
ieee: 'J. Korhonen, A. Paz, J. Rybicki, S. Schmid, and J. Suomela, “Brief announcement:
Sinkless orientation is hard also in the supported LOCAL model,” in 35th International
Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.'
ista: 'Korhonen J, Paz A, Rybicki J, Schmid S, Suomela J. 2021. Brief announcement:
Sinkless orientation is hard also in the supported LOCAL model. 35th International
Symposium on Distributed Computing. DISC: Distributed Computing , LIPIcs, vol.
209, 58.'
mla: 'Korhonen, Janne, et al. “Brief Announcement: Sinkless Orientation Is Hard
Also in the Supported LOCAL Model.” 35th International Symposium on Distributed
Computing, vol. 209, 58, Schloss Dagstuhl - Leibniz Zentrum für Informatik,
2021, doi:10.4230/LIPIcs.DISC.2021.58.'
short: J. Korhonen, A. Paz, J. Rybicki, S. Schmid, J. Suomela, in:, 35th International
Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik,
2021.
conference:
end_date: 2021-10-08
location: Freiburg, Germany
name: 'DISC: Distributed Computing '
start_date: 2021-10-04
date_created: 2021-11-07T23:01:24Z
date_published: 2021-10-04T00:00:00Z
date_updated: 2021-11-12T09:37:18Z
day: '04'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.4230/LIPIcs.DISC.2021.58
ec_funded: 1
external_id:
arxiv:
- '2108.02655'
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date_created: 2021-11-12T08:27:42Z
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call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th International Symposium on Distributed Computing
publication_identifier:
isbn:
- 9-783-9597-7210-5
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Brief announcement: Sinkless orientation is hard also in the supported LOCAL
model'
tmp:
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legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 209
year: '2021'
...
---
_id: '10429'
abstract:
- lang: eng
text: "The scalability of concurrent data structures and distributed algorithms
strongly depends on\r\nreducing the contention for shared resources and the costs
of synchronization and communication. We show how such cost reductions can be
attained by relaxing the strict consistency conditions required by sequential
implementations. In the first part of the thesis, we consider relaxation in the
context of concurrent data structures. Specifically, in data structures \r\nsuch
as priority queues, imposing strong semantics renders scalability impossible,
since a correct implementation of the remove operation should return only the
element with highest priority. Intuitively, attempting to invoke remove operations
concurrently creates a race condition. This bottleneck can be circumvented by
relaxing semantics of the affected data structure, thus allowing removal of the
elements which are no longer required to have the highest priority. We prove that
the randomized implementations of relaxed data structures provide provable guarantees
on the priority of the removed elements even under concurrency. Additionally,
we show that in some cases the relaxed data structures can be used to scale the
classical algorithms which are usually implemented with the exact ones. In the
second part, we study parallel variants of the stochastic gradient descent (SGD)
algorithm, which distribute computation among the multiple processors, thus reducing
the running time. Unfortunately, in order for standard parallel SGD to succeed,
each processor has to maintain a local copy of the necessary model parameter,
which is identical to the local copies of other processors; the overheads from
this perfect consistency in terms of communication and synchronization can negate
the speedup gained by distributing the computation. We show that the consistency
conditions required by SGD can be relaxed, allowing the algorithm to be more
flexible in terms of tolerating quantized communication, asynchrony, or even crash
faults, while its convergence remains asymptotically the same."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
citation:
ama: Nadiradze G. On achieving scalability through relaxation. 2021. doi:10.15479/at:ista:10429
apa: Nadiradze, G. (2021). On achieving scalability through relaxation. Institute
of Science and Technology Austria. https://doi.org/10.15479/at:ista:10429
chicago: Nadiradze, Giorgi. “On Achieving Scalability through Relaxation.” Institute
of Science and Technology Austria, 2021. https://doi.org/10.15479/at:ista:10429.
ieee: G. Nadiradze, “On achieving scalability through relaxation,” Institute of
Science and Technology Austria, 2021.
ista: Nadiradze G. 2021. On achieving scalability through relaxation. Institute
of Science and Technology Austria.
mla: Nadiradze, Giorgi. On Achieving Scalability through Relaxation. Institute
of Science and Technology Austria, 2021, doi:10.15479/at:ista:10429.
short: G. Nadiradze, On Achieving Scalability through Relaxation, Institute of Science
and Technology Austria, 2021.
date_created: 2021-12-08T21:52:28Z
date_published: 2021-12-09T00:00:00Z
date_updated: 2023-10-17T11:48:55Z
day: '09'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
doi: 10.15479/at:ista:10429
ec_funded: 1
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date_created: 2021-12-09T17:47:49Z
date_updated: 2021-12-09T17:47:49Z
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content_type: application/zip
creator: gnadirad
date_created: 2021-12-09T17:47:49Z
date_updated: 2022-03-28T12:55:12Z
file_id: '10437'
file_name: Thesis_Final_09_12_2021.zip
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file_date_updated: 2022-03-28T12:55:12Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '132'
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10432'
relation: part_of_dissertation
status: public
- id: '6673'
relation: part_of_dissertation
status: public
- id: '5965'
relation: part_of_dissertation
status: public
- id: '10435'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
title: On achieving scalability through relaxation
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2021'
...
---
_id: '10432'
abstract:
- lang: eng
text: One key element behind the recent progress of machine learning has been the
ability to train machine learning models in large-scale distributed shared-memory
and message-passing environments. Most of these models are trained employing variants
of stochastic gradient descent (SGD) based optimization, but most methods involve
some type of consistency relaxation relative to sequential SGD, to mitigate its
large communication or synchronization costs at scale. In this paper, we introduce
a general consistency condition covering communication-reduced and asynchronous
distributed SGD implementations. Our framework, called elastic consistency, decouples
the system-specific aspects of the implementation from the SGD convergence requirements,
giving a general way to obtain convergence bounds for a wide variety of distributed
SGD methods used in practice. Elastic consistency can be used to re-derive or
improve several previous convergence bounds in message-passing and shared-memory
settings, but also to analyze new models and distribution schemes. As a direct
application, we propose and analyze a new synchronization-avoiding scheduling
scheme for distributed SGD, and show that it can be used to efficiently train
deep convolutional models for image classification.
acknowledgement: "We would like to thank Christopher De Sa for his feedback on an
earlier draft of this paper, as well as the anonymous AAAI reviewers for their useful
comments. This project has received\r\nfunding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML). Bapi\r\nChatterjee was supported by the European
Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie
grant agreement No. 754411 (ISTPlus)."
article_processing_charge: No
arxiv: 1
author:
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Bapi
full_name: Chatterjee, Bapi
id: 3C41A08A-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-2742-4028
- first_name: 'Vyacheslav '
full_name: 'Kungurtsev, Vyacheslav '
last_name: Kungurtsev
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency:
A practical consistency model for distributed stochastic gradient descent. In:
Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35.
; 2021:9037-9045.'
apa: 'Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh,
D.-A. (2021). Elastic consistency: A practical consistency model for distributed
stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 9037–9045). Virtual.'
chicago: 'Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev,
and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for
Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference
on Artificial Intelligence, 35:9037–45, 2021.'
ieee: 'G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh,
“Elastic consistency: A practical consistency model for distributed stochastic
gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.'
ista: 'Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. 2021. Elastic
consistency: A practical consistency model for distributed stochastic gradient
descent. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI:
Association for the Advancement of Artificial Intelligence vol. 35, 9037–9045.'
mla: 'Nadiradze, Giorgi, et al. “Elastic Consistency: A Practical Consistency Model
for Distributed Stochastic Gradient Descent.” Proceedings of the AAAI Conference
on Artificial Intelligence, vol. 35, no. 10, 2021, pp. 9037–45.'
short: G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:,
Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
date_created: 2021-12-09T09:21:35Z
date_published: 2021-05-18T00:00:00Z
date_updated: 2023-09-07T13:31:39Z
day: '18'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2001.05918'
intvolume: ' 35'
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/17092
month: '05'
oa: 1
oa_version: Published Version
page: 9037-9045
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_status: published
quality_controlled: '1'
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record:
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relation: dissertation_contains
status: public
status: public
title: 'Elastic consistency: A practical consistency model for distributed stochastic
gradient descent'
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 35
year: '2021'
...