---
_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: '11420'
abstract:
- lang: eng
text: 'Understanding the properties of neural networks trained via stochastic gradient
descent (SGD) is at the heart of the theory of deep learning. In this work, we
take a mean-field view, and consider a two-layer ReLU network trained via noisy-SGD
for a univariate regularized regression problem. Our main result is that SGD with
vanishingly small noise injected in the gradients is biased towards a simple solution:
at convergence, the ReLU network implements a piecewise linear map of the inputs,
and the number of “knot” points -- i.e., points where the tangent of the ReLU
network estimator changes -- between two consecutive training inputs is at most
three. In particular, as the number of neurons of the network grows, the SGD dynamics
is captured by the solution of a gradient flow and, at convergence, the distribution
of the weights approaches the unique minimizer of a related free energy, which
has a Gibbs form. Our key technical contribution consists in the analysis of the
estimator resulting from this minimizer: we show that its second derivative vanishes
everywhere, except at some specific locations which represent the “knot” points.
We also provide empirical evidence that knots at locations distinct from the data
points might occur, as predicted by our theory.'
acknowledgement: "We would like to thank Mert Pilanci for several exploratory discussions
in the early stage\r\nof the project, Jan Maas for clarifications about Jordan et
al. (1998), and Max Zimmer for\r\nsuggestive numerical experiments. A. Shevchenko
and M. Mondelli are partially supported\r\nby the 2019 Lopez-Loreta Prize. V. Kungurtsev
acknowledges support to the OP VVV\r\nproject CZ.02.1.01/0.0/0.0/16 019/0000765
Research Center for Informatics.\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Aleksandr
full_name: Shevchenko, Aleksandr
id: F2B06EC2-C99E-11E9-89F0-752EE6697425
last_name: Shevchenko
- first_name: Vyacheslav
full_name: Kungurtsev, Vyacheslav
last_name: Kungurtsev
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: Shevchenko A, Kungurtsev V, Mondelli M. Mean-field analysis of piecewise linear
solutions for wide ReLU networks. Journal of Machine Learning Research.
2022;23(130):1-55.
apa: Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis
of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning
Research. Journal of Machine Learning Research.
chicago: Shevchenko, Aleksandr, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field
Analysis of Piecewise Linear Solutions for Wide ReLU Networks.” Journal of
Machine Learning Research. Journal of Machine Learning Research, 2022.
ieee: A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise
linear solutions for wide ReLU networks,” Journal of Machine Learning Research,
vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
ista: Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise
linear solutions for wide ReLU networks. Journal of Machine Learning Research.
23(130), 1–55.
mla: Shevchenko, Aleksandr, et al. “Mean-Field Analysis of Piecewise Linear Solutions
for Wide ReLU Networks.” Journal of Machine Learning Research, vol. 23,
no. 130, Journal of Machine Learning Research, 2022, pp. 1–55.
short: A. Shevchenko, V. Kungurtsev, M. Mondelli, Journal of Machine Learning Research
23 (2022) 1–55.
date_created: 2022-05-29T22:01:54Z
date_published: 2022-04-01T00:00:00Z
date_updated: 2024-09-10T13:03:17Z
day: '01'
ddc:
- '000'
department:
- _id: MaMo
- _id: DaAl
external_id:
arxiv:
- '2111.02278'
file:
- access_level: open_access
checksum: d4ff5d1affb34848b5c5e4002483fc62
content_type: application/pdf
creator: cchlebak
date_created: 2022-05-30T08:22:55Z
date_updated: 2022-05-30T08:22:55Z
file_id: '11422'
file_name: 21-1365.pdf
file_size: 1521701
relation: main_file
success: 1
file_date_updated: 2022-05-30T08:22:55Z
has_accepted_license: '1'
intvolume: ' 23'
issue: '130'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 1-55
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Journal of Machine Learning Research
publication_identifier:
eissn:
- 1533-7928
issn:
- 1532-4435
publication_status: published
publisher: Journal of Machine Learning Research
quality_controlled: '1'
related_material:
link:
- relation: other
url: https://www.jmlr.org/papers/v23/21-1365.html
scopus_import: '1'
status: public
title: Mean-field analysis of piecewise linear solutions for wide ReLU networks
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: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 23
year: '2022'
...
---
_id: '11707'
abstract:
- lang: eng
text: 'In this work we introduce the graph-theoretic notion of mendability: for
each locally checkable graph problem we can define its mending radius, which captures
the idea of how far one needs to modify a partial solution in order to “patch
a hole.” We explore how mendability is connected to the existence of efficient
algorithms, especially in distributed, parallel, and fault-tolerant settings.
It is easy to see that O(1)-mendable problems are also solvable in O(log∗n) rounds
in the LOCAL model of distributed computing. One of the surprises is that in paths
and cycles, a converse also holds in the following sense: if a problem Π can be
solved in O(log∗n), there is always a restriction Π′⊆Π that is still efficiently
solvable but that is also O(1)-mendable. We also explore the structure of the
landscape of mendability. For example, we show that in trees, the mending radius
of any locally checkable problem is O(1), Θ(logn), or Θ(n), while in general graphs
the structure is much more diverse.'
acknowledgement: This project has received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement
No 840605. This work was supported in part by the Academy of Finland, Grants 314888
and 333837. The authors would also like to thank David Harris, Neven Villani, and
the anonymous reviewers for their very helpful comments and feedback on previous
versions of this work.
article_processing_charge: No
arxiv: 1
author:
- first_name: Alkida
full_name: Balliu, Alkida
last_name: Balliu
- first_name: Juho
full_name: Hirvonen, Juho
last_name: Hirvonen
- first_name: Darya
full_name: Melnyk, Darya
last_name: Melnyk
- first_name: Dennis
full_name: Olivetti, Dennis
last_name: Olivetti
- first_name: Joel
full_name: Rybicki, Joel
id: 334EFD2E-F248-11E8-B48F-1D18A9856A87
last_name: Rybicki
orcid: 0000-0002-6432-6646
- first_name: Jukka
full_name: Suomela, Jukka
last_name: Suomela
citation:
ama: 'Balliu A, Hirvonen J, Melnyk D, Olivetti D, Rybicki J, Suomela J. Local mending.
In: Parter M, ed. International Colloquium on Structural Information and Communication
Complexity. Vol 13298. LNCS. Springer Nature; 2022:1-20. doi:10.1007/978-3-031-09993-9_1'
apa: 'Balliu, A., Hirvonen, J., Melnyk, D., Olivetti, D., Rybicki, J., & Suomela,
J. (2022). Local mending. In M. Parter (Ed.), International Colloquium on Structural
Information and Communication Complexity (Vol. 13298, pp. 1–20). Paderborn,
Germany: Springer Nature. https://doi.org/10.1007/978-3-031-09993-9_1'
chicago: Balliu, Alkida, Juho Hirvonen, Darya Melnyk, Dennis Olivetti, Joel Rybicki,
and Jukka Suomela. “Local Mending.” In International Colloquium on Structural
Information and Communication Complexity, edited by Merav Parter, 13298:1–20.
LNCS. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-09993-9_1.
ieee: A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, and J. Suomela,
“Local mending,” in International Colloquium on Structural Information and
Communication Complexity, Paderborn, Germany, 2022, vol. 13298, pp. 1–20.
ista: 'Balliu A, Hirvonen J, Melnyk D, Olivetti D, Rybicki J, Suomela J. 2022. Local
mending. International Colloquium on Structural Information and Communication
Complexity. SIROCCO: Structural Information and Communication ComplexityLNCS vol.
13298, 1–20.'
mla: Balliu, Alkida, et al. “Local Mending.” International Colloquium on Structural
Information and Communication Complexity, edited by Merav Parter, vol. 13298,
Springer Nature, 2022, pp. 1–20, doi:10.1007/978-3-031-09993-9_1.
short: A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, J. Suomela, in:,
M. Parter (Ed.), International Colloquium on Structural Information and Communication
Complexity, Springer Nature, 2022, pp. 1–20.
conference:
end_date: 2022-06-29
location: Paderborn, Germany
name: 'SIROCCO: Structural Information and Communication Complexity'
start_date: 2022-06-27
date_created: 2022-07-31T22:01:49Z
date_published: 2022-06-25T00:00:00Z
date_updated: 2023-08-03T12:16:29Z
day: '25'
department:
- _id: DaAl
doi: 10.1007/978-3-031-09993-9_1
ec_funded: 1
editor:
- first_name: Merav
full_name: Parter, Merav
last_name: Parter
external_id:
arxiv:
- '2102.08703'
isi:
- '000876977400001'
intvolume: ' 13298'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.08703
month: '06'
oa: 1
oa_version: Preprint
page: 1-20
project:
- _id: 26A5D39A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '840605'
name: Coordination in constrained and natural distributed systems
publication: International Colloquium on Structural Information and Communication
Complexity
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031099922'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
series_title: LNCS
status: public
title: Local mending
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13298
year: '2022'
...
---
_id: '13076'
abstract:
- lang: eng
text: "The source code for replicating experiments presented in the paper.\r\n\r\nThe
implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h
is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe
provide images that contain all the dependencies and datasets. Images can be pulled
from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See
readme for more detail."
article_processing_charge: No
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. 2022. doi:10.5281/ZENODO.5733408
apa: Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues
can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian
Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo,
2022. https://doi.org/10.5281/ZENODO.5733408.
ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can
be state-of-the-art priority schedulers.” Zenodo, 2022.
ista: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be
state-of-the-art priority schedulers, Zenodo, 10.5281/ZENODO.5733408.
mla: Postnikova, Anastasiia, et al. Multi-Queues Can Be State-of-the-Art Priority
Schedulers. Zenodo, 2022, doi:10.5281/ZENODO.5733408.
short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).
date_created: 2023-05-23T17:05:40Z
date_published: 2022-01-03T00:00:00Z
date_updated: 2023-08-03T06:48:34Z
day: '03'
ddc:
- '510'
department:
- _id: DaAl
doi: 10.5281/ZENODO.5733408
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5813846
month: '01'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
link:
- relation: software
url: https://github.com/npostnikova/mq-based-schedulers/tree/v1.1
record:
- id: '11180'
relation: used_in_publication
status: public
status: public
title: Multi-queues can be state-of-the-art priority schedulers
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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: '12182'
abstract:
- lang: eng
text: 'Online algorithms make decisions based on past inputs, with the goal of being
competitive against an algorithm that sees also future inputs. In this work, we
introduce time-local online algorithms; these are online algorithms in which the
output at any given time is a function of only T latest inputs. Our main observation
is that time-local online algorithms are closely connected to local distributed
graph algorithms: distributed algorithms make decisions based on the local information
in the spatial dimension, while time-local online algorithms make decisions based
on the local information in the temporal dimension. We formalize this connection,
and show how we can directly use the tools developed to study distributed approximability
of graph optimization problems to prove upper and lower bounds on the competitive
ratio achieved with time-local online algorithms. Moreover, we show how to use
computational techniques to synthesize optimal time-local algorithms.'
acknowledgement: "This research has received funding from the German Research Foundation
(DFG), grant\r\n470029389 (FlexNets), 2021-2024, and the Marie Skłodowska-Curie
grant agreement No. 840605."
article_number: '52'
article_processing_charge: No
author:
- first_name: Maciej
full_name: Pacut, Maciej
last_name: Pacut
- first_name: Mahmoud
full_name: Parham, Mahmoud
last_name: Parham
- 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
- first_name: Aleksandr
full_name: Tereshchenko, Aleksandr
last_name: Tereshchenko
citation:
ama: 'Pacut M, Parham M, Rybicki J, Schmid S, Suomela J, Tereshchenko A. Brief announcement:
Temporal locality in online algorithms. In: 36th International Symposium on
Distributed Computing. Vol 246. Schloss Dagstuhl - Leibniz-Zentrum für Informatik;
2022. doi:10.4230/LIPIcs.DISC.2022.52'
apa: 'Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., & Tereshchenko,
A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th
International Symposium on Distributed Computing (Vol. 246). Augusta, GA,
United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2022.52'
chicago: 'Pacut, Maciej, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela,
and Aleksandr Tereshchenko. “Brief Announcement: Temporal Locality in Online Algorithms.”
In 36th International Symposium on Distributed Computing, Vol. 246. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.DISC.2022.52.'
ieee: 'M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, and A. Tereshchenko,
“Brief announcement: Temporal locality in online algorithms,” in 36th International
Symposium on Distributed Computing, Augusta, GA, United States, 2022, vol.
246.'
ista: 'Pacut M, Parham M, Rybicki J, Schmid S, Suomela J, Tereshchenko A. 2022.
Brief announcement: Temporal locality in online algorithms. 36th International
Symposium on Distributed Computing. DISC: Symposium on Distributed Computing vol.
246, 52.'
mla: 'Pacut, Maciej, et al. “Brief Announcement: Temporal Locality in Online Algorithms.”
36th International Symposium on Distributed Computing, vol. 246, 52, Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2022, doi:10.4230/LIPIcs.DISC.2022.52.'
short: M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, A. Tereshchenko,
in:, 36th International Symposium on Distributed Computing, Schloss Dagstuhl -
Leibniz-Zentrum für Informatik, 2022.
conference:
end_date: 2022-10-27
location: Augusta, GA, United States
name: 'DISC: Symposium on Distributed Computing'
start_date: 2022-10-25
date_created: 2023-01-13T11:06:28Z
date_published: 2022-10-17T00:00:00Z
date_updated: 2023-01-27T06:59:29Z
day: '17'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.4230/LIPIcs.DISC.2022.52
ec_funded: 1
file:
- access_level: open_access
checksum: 11bbb56f68a00f2cf6bcce6cc7f5c5f9
content_type: application/pdf
creator: dernst
date_created: 2023-01-27T06:58:02Z
date_updated: 2023-01-27T06:58:02Z
file_id: '12409'
file_name: 2022_LIPICs_Pacut.pdf
file_size: 524804
relation: main_file
success: 1
file_date_updated: 2023-01-27T06:58:02Z
has_accepted_license: '1'
intvolume: ' 246'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
project:
- _id: 26A5D39A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '840605'
name: Coordination in constrained and natural distributed systems
publication: 36th International Symposium on Distributed Computing
publication_identifier:
eisbn:
- '9783959772556'
eissn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Brief announcement: Temporal locality in online algorithms'
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: 246
year: '2022'
...
---
_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: '12780'
abstract:
- lang: eng
text: "The ability to scale out training workloads has been one of the key performance
enablers of deep learning. The main scaling approach is data-parallel GPU-based
training, which has been boosted by hardware and software support for highly efficient
point-to-point communication, and in particular via hardware bandwidth over-provisioning.
Overprovisioning comes at a cost: there is an order of magnitude price difference
between \"cloud-grade\" servers with such support, relative to their popular \"consumer-grade\"
counterparts, although single server-grade and consumer-grade GPUs can have similar
computational envelopes.\r\n\r\nIn this paper, we show that the costly hardware
overprovisioning approach can be supplanted via algorithmic and system design,
and propose a framework called CGX, which provides efficient software support
for compressed communication in ML applications, for both multi-GPU single-node
training, as well as larger-scale multi-node training. CGX is based on two technical
advances: At the system level, it relies on a re-developed communication stack
for ML frameworks, which provides flexible, highly-efficient support for compressed
communication. At the application level, it provides seamless, parameter-free
integration with popular frameworks, so that end-users do not have to modify training
recipes, nor significant training code. This is complemented by a layer-wise adaptive
compression technique which dynamically balances compression gains with accuracy
preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups
for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements
in the multi-node setting, with negligible impact on accuracy."
acknowledgement: The authors sincerely thank Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
and Bapi Chatterjee for useful discussions throughout the development of this project.
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Hamidreza
full_name: Ramezanikebrya, Hamidreza
last_name: Ramezanikebrya
- 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, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for
communication-efficient deep learning. In: Proceedings of the 23rd ACM/IFIP
International Middleware Conference. Association for Computing Machinery;
2022:241-254. doi:10.1145/3528535.3565248'
apa: 'Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive
system support for communication-efficient deep learning. In Proceedings of
the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec,
QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248'
chicago: 'Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX:
Adaptive System Support for Communication-Efficient Deep Learning.” In Proceedings
of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association
for Computing Machinery, 2022. https://doi.org/10.1145/3528535.3565248.'
ieee: 'I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support
for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP
International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.'
ista: 'Markov I, Ramezanikebrya H, Alistarh D-A. 2022. CGX: Adaptive system support
for communication-efficient deep learning. Proceedings of the 23rd ACM/IFIP International
Middleware Conference. Middleware: International Middleware Conference, 241–254.'
mla: 'Markov, Ilia, et al. “CGX: Adaptive System Support for Communication-Efficient
Deep Learning.” Proceedings of the 23rd ACM/IFIP International Middleware Conference,
Association for Computing Machinery, 2022, pp. 241–54, doi:10.1145/3528535.3565248.'
short: I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd
ACM/IFIP International Middleware Conference, Association for Computing Machinery,
2022, pp. 241–254.
conference:
end_date: 2022-11-11
location: Quebec, QC, Canada
name: 'Middleware: International Middleware Conference'
start_date: 2022-11-07
date_created: 2023-03-31T06:17:00Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-04-03T06:21:04Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.1145/3528535.3565248
external_id:
arxiv:
- '2111.08617'
file:
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checksum: 1a397746235f245da5468819247ff663
content_type: application/pdf
creator: dernst
date_created: 2023-04-03T06:17:58Z
date_updated: 2023-04-03T06:17:58Z
file_id: '12795'
file_name: 2022_ACMMiddleware_Markov.pdf
file_size: 1514169
relation: main_file
success: 1
file_date_updated: 2023-04-03T06:17:58Z
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 241-254
publication: Proceedings of the 23rd ACM/IFIP International Middleware Conference
publication_identifier:
isbn:
- '9781450393409'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
status: public
title: 'CGX: Adaptive system support for communication-efficient deep learning'
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: '10853'
abstract:
- lang: eng
text: Dynamic Connectivity is a fundamental algorithmic graph problem, motivated
by a wide range of applications to social and communication networks and used
as a building block in various other algorithms, such as the bi-connectivity and
the dynamic minimal spanning tree problems. In brief, we wish to maintain the
connected components of the graph under dynamic edge insertions and deletions.
In the sequential case, the problem has been well-studied from both theoretical
and practical perspectives. However, much less is known about efficient concurrent
solutions to this problem. This is the gap we address in this paper. We start
from one of the classic data structures used to solve this problem, the Euler
Tour Tree. Our first contribution is a non-blocking single-writer implementation
of it. We leverage this data structure to obtain the first truly concurrent generalization
of dynamic connectivity, which preserves the time complexity of its sequential
counterpart, but is also scalable in practice. To achieve this, we rely on three
main techniques. The first is to ensure that connectivity queries, which usually
dominate real-world workloads, are non-blocking. The second non-trivial technique
expands the above idea by making all queries that do not change the connectivity
structure non-blocking. The third ingredient is applying fine-grained locking
for updating the connected components, which allows operations on disjoint components
to occur in parallel. We evaluate the resulting algorithm on various workloads,
executing on both real and synthetic graphs. The results show the efficiency of
each of the proposed optimizations; the most efficient variant improves the performance
of a coarse-grained based implementation on realistic scenarios up to 6x on average
and up to 30x when connectivity queries dominate.
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Fedorov, Alexander
last_name: Fedorov
- first_name: Nikita
full_name: Koval, Nikita
last_name: Koval
- 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: 'Fedorov A, Koval N, Alistarh D-A. A scalable concurrent algorithm for dynamic
connectivity. In: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms
and Architectures. Association for Computing Machinery; 2021:208-220. doi:10.1145/3409964.3461810'
apa: 'Fedorov, A., Koval, N., & Alistarh, D.-A. (2021). A scalable concurrent
algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium
on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online:
Association for Computing Machinery. https://doi.org/10.1145/3409964.3461810'
chicago: Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable
Concurrent Algorithm for Dynamic Connectivity.” In Proceedings of the 33rd
ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association
for Computing Machinery, 2021. https://doi.org/10.1145/3409964.3461810.
ieee: A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm
for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism
in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.
ista: 'Fedorov A, Koval N, Alistarh D-A. 2021. A scalable concurrent algorithm for
dynamic connectivity. Proceedings of the 33rd ACM Symposium on Parallelism in
Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and
Architectures, 208–220.'
mla: Fedorov, Alexander, et al. “A Scalable Concurrent Algorithm for Dynamic Connectivity.”
Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures,
Association for Computing Machinery, 2021, pp. 208–20, doi:10.1145/3409964.3461810.
short: A. Fedorov, N. Koval, D.-A. Alistarh, in:, Proceedings of the 33rd ACM Symposium
on Parallelism in Algorithms and Architectures, Association for Computing Machinery,
2021, pp. 208–220.
conference:
end_date: 2021-07-08
location: Virtual, Online
name: 'SPAA: Symposium on Parallelism in Algorithms and Architectures'
start_date: 2021-07-06
date_created: 2022-03-18T08:21:47Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2022-03-18T08:45:46Z
day: '01'
department:
- _id: DaAl
doi: 10.1145/3409964.3461810
external_id:
arxiv:
- '2105.08098'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2105.08098
month: '07'
oa: 1
oa_version: Preprint
page: 208-220
publication: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and
Architectures
publication_identifier:
isbn:
- '9781450380706'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: A scalable concurrent algorithm for dynamic connectivity
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10854'
abstract:
- lang: eng
text: "Consider a distributed task where the communication network is fixed but
the local inputs given to the nodes of the distributed system may change over
time. In this work, we explore the following question: if some of the local inputs
change, can an existing solution be updated efficiently, in a dynamic and distributed
manner?\r\nTo address this question, we define the batch dynamic CONGEST model
in which we are given a bandwidth-limited communication network and a dynamic
edge labelling defines the problem input. The task is to maintain a solution to
a graph problem on the labelled graph under batch changes. We investigate, when
a batch of alpha edge label changes arrive, - how much time as a function of alpha
we need to update an existing solution, and - how much information the nodes have
to keep in local memory between batches in order to update the solution quickly.\r\nOur
work lays the foundations for the theory of input-dynamic distributed network
algorithms. We give a general picture of the complexity landscape in this model,
design both universal algorithms and algorithms for concrete problems, and present
a general framework for lower bounds. The diverse time complexity of our model
spans from constant time, through time polynomial in alpha, and to alpha time,
which we show to be enough for any task."
acknowledgement: We thank Jukka Suomela for discussions. We also thank our shepherd
Mohammad Hajiesmaili and the reviewers for their time and suggestions on how to
improve the paper. 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), from the European Union’s Horizon 2020 research
and innovation programme under the Marie Skłodowska–Curie grant agreement No. 840605,
from the Vienna Science and Technology Fund (WWTF) project WHATIF, ICT19-045, 2020-2024,
and from the Austrian Science Fund (FWF) and netIDEE SCIENCE project P 33775-N.
article_processing_charge: No
arxiv: 1
author:
- first_name: Klaus-Tycho
full_name: Foerster, Klaus-Tycho
last_name: Foerster
- 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
citation:
ama: 'Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. Input-dynamic distributed
algorithms for communication networks. In: Abstract Proceedings of the 2021
ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer
Systems. Association for Computing Machinery; 2021:71-72. doi:10.1145/3410220.3453923'
apa: 'Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021).
Input-dynamic distributed algorithms for communication networks. In Abstract
Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement
and Modeling of Computer Systems (pp. 71–72). Virtual, Online: Association
for Computing Machinery. https://doi.org/10.1145/3410220.3453923'
chicago: Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan
Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.” In
Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference
on Measurement and Modeling of Computer Systems, 71–72. Association for Computing
Machinery, 2021. https://doi.org/10.1145/3410220.3453923.
ieee: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic
distributed algorithms for communication networks,” in Abstract Proceedings
of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling
of Computer Systems, Virtual, Online, 2021, pp. 71–72.
ista: 'Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic
distributed algorithms for communication networks. Abstract Proceedings of the
2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of
Computer Systems. SIGMETRICS: International Conference on Measurement and Modeling
of Computer Systems, 71–72.'
mla: Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication
Networks.” Abstract Proceedings of the 2021 ACM SIGMETRICS / International
Conference on Measurement and Modeling of Computer Systems, Association for
Computing Machinery, 2021, pp. 71–72, doi:10.1145/3410220.3453923.
short: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, in:, Abstract
Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement
and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp.
71–72.
conference:
end_date: 2021-06-18
location: Virtual, Online
name: 'SIGMETRICS: International Conference on Measurement and Modeling of Computer
Systems'
start_date: 2021-06-14
date_created: 2022-03-18T08:48:41Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-26T10:40:55Z
day: '01'
department:
- _id: DaAl
doi: 10.1145/3410220.3453923
ec_funded: 1
external_id:
arxiv:
- '2005.07637'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2005.07637
month: '05'
oa: 1
oa_version: Preprint
page: 71-72
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: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference
on Measurement and Modeling of Computer Systems
publication_identifier:
isbn:
- '9781450380720'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '10855'
relation: extended_version
status: public
scopus_import: '1'
status: public
title: Input-dynamic distributed algorithms for communication networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10855'
abstract:
- lang: eng
text: 'Consider a distributed task where the communication network is fixed but
the local inputs given to the nodes of the distributed system may change over
time. In this work, we explore the following question: if some of the local inputs
change, can an existing solution be updated efficiently, in a dynamic and distributed
manner? To address this question, we define the batch dynamic \congest model in
which we are given a bandwidth-limited communication network and a dynamic edge
labelling defines the problem input. The task is to maintain a solution to a graph
problem on the labeled graph under batch changes. We investigate, when a batch
of α edge label changes arrive, \beginitemize \item how much time as a function
of α we need to update an existing solution, and \item how much information the
nodes have to keep in local memory between batches in order to update the solution
quickly. \enditemize Our work lays the foundations for the theory of input-dynamic
distributed network algorithms. We give a general picture of the complexity landscape
in this model, design both universal algorithms and algorithms for concrete problems,
and present a general framework for lower bounds. In particular, we derive non-trivial
upper bounds for two selected, contrasting problems: maintaining a minimum spanning
tree and detecting cliques.'
acknowledgement: "We thank Jukka Suomela for discussions. We also thank our shepherd
Mohammad Hajiesmaili\r\nand the reviewers for their time and suggestions on how
to improve the paper. This project\r\nhas received funding from the European Research
Council (ERC) under the European Union’s\r\nHorizon 2020 research and innovation
programme (grant agreement No 805223 ScaleML), from the European Union’s Horizon
2020 research and innovation programme under the Marie\r\nSk lodowska–Curie grant
agreement No. 840605, from the Vienna Science and Technology Fund (WWTF) project
WHATIF, ICT19-045, 2020-2024, and from the Austrian Science Fund (FWF) and netIDEE
SCIENCE project P 33775-N."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Klaus-Tycho
full_name: Foerster, Klaus-Tycho
last_name: Foerster
- 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
citation:
ama: Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. Input-dynamic distributed
algorithms for communication networks. Proceedings of the ACM on Measurement
and Analysis of Computing Systems. 2021;5(1):1-33. doi:10.1145/3447384
apa: Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021).
Input-dynamic distributed algorithms for communication networks. Proceedings
of the ACM on Measurement and Analysis of Computing Systems. Association for
Computing Machinery. https://doi.org/10.1145/3447384
chicago: Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan
Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.” Proceedings
of the ACM on Measurement and Analysis of Computing Systems. Association for
Computing Machinery, 2021. https://doi.org/10.1145/3447384.
ieee: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic
distributed algorithms for communication networks,” Proceedings of the ACM
on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association
for Computing Machinery, pp. 1–33, 2021.
ista: Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic
distributed algorithms for communication networks. Proceedings of the ACM on Measurement
and Analysis of Computing Systems. 5(1), 1–33.
mla: Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication
Networks.” Proceedings of the ACM on Measurement and Analysis of Computing
Systems, vol. 5, no. 1, Association for Computing Machinery, 2021, pp. 1–33,
doi:10.1145/3447384.
short: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, Proceedings of
the ACM on Measurement and Analysis of Computing Systems 5 (2021) 1–33.
date_created: 2022-03-18T09:10:27Z
date_published: 2021-03-01T00:00:00Z
date_updated: 2023-09-26T10:40:55Z
day: '01'
department:
- _id: DaAl
doi: 10.1145/3447384
ec_funded: 1
external_id:
arxiv:
- '2005.07637'
intvolume: ' 5'
issue: '1'
keyword:
- Computer Networks and Communications
- Hardware and Architecture
- Safety
- Risk
- Reliability and Quality
- Computer Science (miscellaneous)
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2005.07637
month: '03'
oa: 1
oa_version: Preprint
page: 1-33
project:
- _id: 26A5D39A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '840605'
name: Coordination in constrained and natural distributed systems
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the ACM on Measurement and Analysis of Computing Systems
publication_identifier:
issn:
- 2476-1249
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '10854'
relation: shorter_version
status: public
scopus_import: '1'
status: public
title: Input-dynamic distributed algorithms for communication networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2021'
...
---
_id: '11436'
abstract:
- lang: eng
text: Asynchronous distributed algorithms are a popular way to reduce synchronization
costs in large-scale optimization, and in particular for neural network training.
However, for nonsmooth and nonconvex objectives, few convergence guarantees exist
beyond cases where closed-form proximal operator solutions are available. As training
most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex
objectives, there is a pressing need for such convergence guarantees. In this
paper, we analyze for the first time the convergence of stochastic asynchronous
optimization for this general class of objectives. In particular, we focus on
stochastic subgradient methods allowing for block variable partitioning, where
the shared model is asynchronously updated by concurrent processes. To this end,
we use a probabilistic model which captures key features of real asynchronous
scheduling between concurrent processes. Under this model, we establish convergence
with probability one to an invariant set for stochastic subgradient methods with
momentum. From a practical perspective, one issue with the family of algorithms
that we consider is that they are not efficiently supported by machine learning
frameworks, which mostly focus on distributed data-parallel strategies. To address
this, we propose a new implementation strategy for shared-memory based training
of deep neural networks for a partitioned but shared model in single- and multi-GPU
settings. Based on this implementation, we achieve on average1.2x speed-up in
comparison to state-of-the-art training methods for popular image classification
tasks, without compromising accuracy.
acknowledgement: Vyacheslav Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16
019/0000765 “Research Center for Informatics. Bapi Chatterjee was supported by the
European Union’s Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie grant agreement No. 754411 (ISTPlus). Dan Alistarh 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: Vyacheslav
full_name: Kungurtsev, Vyacheslav
last_name: Kungurtsev
- first_name: Malcolm
full_name: Egan, Malcolm
last_name: Egan
- first_name: Bapi
full_name: Chatterjee, Bapi
id: 3C41A08A-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
- 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: 'Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. Asynchronous optimization
methods for efficient training of deep neural networks with guarantees. In: 35th
AAAI Conference on Artificial Intelligence, AAAI 2021. Vol 35. AAAI Press;
2021:8209-8216.'
apa: 'Kungurtsev, V., Egan, M., Chatterjee, B., & Alistarh, D.-A. (2021). Asynchronous
optimization methods for efficient training of deep neural networks with guarantees.
In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35,
pp. 8209–8216). Virtual, Online: AAAI Press.'
chicago: Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh.
“Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks
with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI
2021, 35:8209–16. AAAI Press, 2021.
ieee: V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization
methods for efficient training of deep neural networks with guarantees,” in 35th
AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021,
vol. 35, no. 9B, pp. 8209–8216.
ista: 'Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. 2021. Asynchronous optimization
methods for efficient training of deep neural networks with guarantees. 35th AAAI
Conference on Artificial Intelligence, AAAI 2021. AAAI: Conference on Artificial
Intelligence vol. 35, 8209–8216.'
mla: Kungurtsev, Vyacheslav, et al. “Asynchronous Optimization Methods for Efficient
Training of Deep Neural Networks with Guarantees.” 35th AAAI Conference on
Artificial Intelligence, AAAI 2021, vol. 35, no. 9B, AAAI Press, 2021, pp.
8209–16.
short: V. Kungurtsev, M. Egan, B. Chatterjee, D.-A. Alistarh, in:, 35th AAAI Conference
on Artificial Intelligence, AAAI 2021, AAAI Press, 2021, pp. 8209–8216.
conference:
end_date: 2021-02-09
location: Virtual, Online
name: 'AAAI: Conference on Artificial Intelligence'
start_date: 2021-02-02
date_created: 2022-06-05T22:01:52Z
date_published: 2021-05-18T00:00:00Z
date_updated: 2022-06-07T06:53:36Z
day: '18'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '1905.11845'
intvolume: ' 35'
issue: 9B
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1905.11845'
month: '05'
oa: 1
oa_version: Preprint
page: 8209-8216
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: 35th AAAI Conference on Artificial Intelligence, AAAI 2021
publication_identifier:
eissn:
- 2374-3468
isbn:
- '9781713835974'
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Asynchronous optimization methods for efficient training of deep neural networks
with guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '11452'
abstract:
- lang: eng
text: We study efficient distributed algorithms for the fundamental problem of principal
component analysis and leading eigenvector computation on the sphere, when the
data are randomly distributed among a set of computational nodes. We propose a
new quantized variant of Riemannian gradient descent to solve this problem, and
prove that the algorithm converges with high probability under a set of necessary
spherical-convexity properties. We give bounds on the number of bits transmitted
by the algorithm under common initialization schemes, and investigate the dependency
on the problem dimension in each case.
acknowledgement: We would like to thank the anonymous reviewers for helpful comments
and suggestions. We also thank Aurelien Lucchi and Antonio Orvieto for fruitful
discussions at an early stage of this work. FA is partially supported by the SNSF
under research project No. 192363 and conducted part of this work while at IST Austria
under the European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 805223 ScaleML). PD partly conducted this work while at IST Austria
and 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: Bart
full_name: Vandereycken, Bart
last_name: Vandereycken
- 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, Vandereycken B, Alistarh D-A. Distributed principal
component analysis with limited communication. In: Advances in Neural Information
Processing Systems - 35th Conference on Neural Information Processing Systems.
Vol 4. Neural Information Processing Systems Foundation; 2021:2823-2834.'
apa: 'Alimisis, F., Davies, P., Vandereycken, B., & Alistarh, D.-A. (2021).
Distributed principal component analysis with limited communication. In Advances
in Neural Information Processing Systems - 35th Conference on Neural Information
Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information
Processing Systems Foundation.'
chicago: Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh.
“Distributed Principal Component Analysis with Limited Communication.” In Advances
in Neural Information Processing Systems - 35th Conference on Neural Information
Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation,
2021.
ieee: F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed
principal component analysis with limited communication,” in Advances in Neural
Information Processing Systems - 35th Conference on Neural Information Processing
Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
ista: 'Alimisis F, Davies P, Vandereycken B, Alistarh D-A. 2021. Distributed principal
component analysis with limited communication. Advances in Neural Information
Processing Systems - 35th Conference on Neural Information Processing Systems.
NeurIPS: Neural Information Processing Systems vol. 4, 2823–2834.'
mla: Alimisis, Foivos, et al. “Distributed Principal Component Analysis with Limited
Communication.” Advances in Neural Information Processing Systems - 35th Conference
on Neural Information Processing Systems, vol. 4, Neural Information Processing
Systems Foundation, 2021, pp. 2823–34.
short: F. Alimisis, P. Davies, B. Vandereycken, D.-A. Alistarh, in:, Advances in
Neural Information Processing Systems - 35th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2021, pp.
2823–2834.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-06-19T22:01:58Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-06-20T08:31:52Z
day: '01'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2110.14391'
intvolume: ' 4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/1680e9fa7b4dd5d62ece800239bb53bd-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 2823-2834
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: Advances in Neural Information Processing Systems - 35th Conference on
Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Distributed principal component analysis with limited communication
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2021'
...
---
_id: '11458'
abstract:
- lang: eng
text: 'The increasing computational requirements of deep neural networks (DNNs)
have led to significant interest in obtaining DNN models that are sparse, yet
accurate. Recent work has investigated the even harder case of sparse training,
where the DNN weights are, for as much as possible, already sparse to reduce computational
costs during training. Existing sparse training methods are often empirical and
can have lower accuracy relative to the dense baseline. In this paper, we present
a general approach called Alternating Compressed/DeCompressed (AC/DC) training
of DNNs, demonstrate convergence for a variant of the algorithm, and show that
AC/DC outperforms existing sparse training methods in accuracy at similar computational
budgets; at high sparsity levels, AC/DC even outperforms existing methods that
rely on accurate pre-trained dense models. An important property of AC/DC is that
it allows co-training of dense and sparse models, yielding accurate sparse–dense
model pairs at the end of the training process. This is useful in practice, where
compressed variants may be desirable for deployment in resource-constrained settings
without re-doing the entire training flow, and also provides us with insights
into the accuracy gap between dense and compressed models. The code is available
at: https://github.com/IST-DASLab/ACDC.'
acknowledged_ssus:
- _id: ScienComp
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 a CNRS PEPS grant. This research was supported
by the Scientific Service Units (SSU) of IST Austria through resources provided
by Scientific Computing (SciComp). We would also like to thank Christoph Lampert
for his feedback on an earlier version of this work, as well as for providing hardware
for the Transformer-XL experiments.
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: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- 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, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed
training of deep neural networks. In: 35th Conference on Neural Information
Processing Systems. Vol 34. Curran Associates; 2021:8557-8570.'
apa: 'Peste, E.-A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC:
Alternating Compressed/DeCompressed training of deep neural networks. In 35th
Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570).
Virtual, Online: Curran Associates.'
chicago: 'Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian
Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural
Networks.” In 35th Conference on Neural Information Processing Systems,
34:8557–70. Curran Associates, 2021.'
ieee: 'E.-A. Peste, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating
Compressed/DeCompressed training of deep neural networks,” in 35th Conference
on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34,
pp. 8557–8570.'
ista: 'Peste E-A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed
training of deep neural networks. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems vol. 34, 8557–8570.'
mla: 'Peste, Elena-Alexandra, et al. “AC/DC: Alternating Compressed/DeCompressed
Training of Deep Neural Networks.” 35th Conference on Neural Information Processing
Systems, vol. 34, Curran Associates, 2021, pp. 8557–70.'
short: E.-A. Peste, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference
on Neural Information Processing Systems, Curran Associates, 2021, pp. 8557–8570.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-06-20T12:11:53Z
date_published: 2021-12-06T00:00:00Z
date_updated: 2023-06-01T12:54:45Z
day: '6'
department:
- _id: GradSch
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2106.12379'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/48000647b315f6f00f913caa757a70b3-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 8557-8570
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'AC/DC: Alternating Compressed/DeCompressed training of deep neural networks'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '11463'
abstract:
- lang: eng
text: "Efficiently approximating local curvature information of the loss function
is a key tool for optimization and compression of deep neural networks. Yet, most
existing methods to approximate second-order information have high computational\r\nor
storage costs, which limits their practicality. In this work, we investigate matrix-free,
linear-time approaches for estimating Inverse-Hessian Vector Products (IHVPs)
for the case when the Hessian can be approximated as a sum of rank-one matrices,
as in the classic approximation of the Hessian by the empirical Fisher matrix.
We propose two new algorithms: the first is tailored towards network compression
and can compute the IHVP for dimension d, if the Hessian is given as a sum of
m rank-one matrices, using O(dm2) precomputation, O(dm) cost for computing the
IHVP, and query cost O(m) for any single element of the inverse Hessian. The second
algorithm targets an optimization setting, where we wish to compute the product
between the inverse Hessian, estimated over a sliding window of optimization steps,
and a given gradient direction, as required for preconditioned SGD. We give an
algorithm with cost O(dm + m2) for computing the IHVP and O(dm + m3) for adding
or removing any gradient from the sliding window. These\r\ntwo algorithms yield
state-of-the-art results for network pruning and optimization with lower computational
overhead relative to existing second-order methods. Implementations are available
at [9] and [17]."
acknowledgement: We gratefully acknowledge funding the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme (grant
agreement No 805223 ScaleML), as well as computational support from Amazon Web Services
(AWS) EC2.
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: 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: 'Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations
of second-order information. In: 35th Conference on Neural Information Processing
Systems. Vol 34. Curran Associates; 2021:14873-14886.'
apa: 'Frantar, E., Kurtic, E., & Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free
approximations of second-order information. In 35th Conference on Neural Information
Processing Systems (Vol. 34, pp. 14873–14886). Virtual, Online: Curran Associates.'
chicago: 'Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient
Matrix-Free Approximations of Second-Order Information.” In 35th Conference
on Neural Information Processing Systems, 34:14873–86. Curran Associates,
2021.'
ieee: 'E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free
approximations of second-order information,” in 35th Conference on Neural Information
Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.'
ista: 'Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations
of second-order information. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems vol. 34, 14873–14886.'
mla: 'Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order
Information.” 35th Conference on Neural Information Processing Systems,
vol. 34, Curran Associates, 2021, pp. 14873–86.'
short: E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information
Processing Systems, Curran Associates, 2021, pp. 14873–14886.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-06-26T22:01:35Z
date_published: 2021-12-06T00:00:00Z
date_updated: 2022-06-27T07:05:12Z
day: '06'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2010.08222'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/7cfd5df443b4eb0d69886a583b33de4c-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 14873-14886
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'M-FAC: Efficient matrix-free approximations of second-order information'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '11464'
abstract:
- lang: eng
text: "We consider a standard distributed optimisation setting where N machines,
each holding a d-dimensional function\r\nfi, aim to jointly minimise the sum of
the functions ∑Ni=1fi(x). This problem arises naturally in large-scale distributed
optimisation, where a standard solution is to apply variants of (stochastic) gradient
descent. We focus on the communication complexity of this problem: our main result
provides the first fully unconditional bounds on total number of bits which need
to be sent and received by the N machines to solve this problem under point-to-point
communication, within a given error-tolerance. Specifically, we show that Ω(Ndlogd/Nε)
total bits need to be communicated between the machines to find an additive ϵ-approximation
to the minimum of ∑Ni=1fi(x). The result holds for both deterministic and randomised
algorithms, and, importantly, requires no assumptions on the algorithm structure.
The lower bound is tight under certain restrictions on parameter values, and is
matched within constant factors for quadratic objectives by a new variant of quantised
gradient descent, which we describe and analyse. Our results bring over tools
from communication complexity to distributed optimisation, which has potential
for further applications."
acknowledgement: We thank the NeurIPS reviewers for insightful comments that helped
us improve the positioning of our results, as well as for pointing out the subsampling
approach for complementing the randomised lower bound. We also thank Foivos Alimisis
and Peter Davies for useful discussions. 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: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Janne
full_name: Korhonen, Janne
id: C5402D42-15BC-11E9-A202-CA2BE6697425
last_name: Korhonen
citation:
ama: 'Alistarh D-A, Korhonen J. Towards tight communication lower bounds for distributed
optimisation. In: 35th Conference on Neural Information Processing Systems.
Vol 34. Curran Associates; 2021:7254-7266.'
apa: 'Alistarh, D.-A., & Korhonen, J. (2021). Towards tight communication lower
bounds for distributed optimisation. In 35th Conference on Neural Information
Processing Systems (Vol. 34, pp. 7254–7266). Virtual, Online: Curran Associates.'
chicago: Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication
Lower Bounds for Distributed Optimisation.” In 35th Conference on Neural Information
Processing Systems, 34:7254–66. Curran Associates, 2021.
ieee: D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds
for distributed optimisation,” in 35th Conference on Neural Information Processing
Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
ista: 'Alistarh D-A, Korhonen J. 2021. Towards tight communication lower bounds
for distributed optimisation. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems vol. 34, 7254–7266.'
mla: Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower
Bounds for Distributed Optimisation.” 35th Conference on Neural Information
Processing Systems, vol. 34, Curran Associates, 2021, pp. 7254–66.
short: D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing
Systems, Curran Associates, 2021, pp. 7254–7266.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
date_created: 2022-06-26T22:01:35Z
date_published: 2021-12-06T00:00:00Z
date_updated: 2022-06-27T06:54:31Z
day: '06'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2010.08222'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/3b92d18aa7a6176dd37d372bc2f1eb71-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 7254-7266
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards tight communication lower bounds for distributed optimisation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '7939'
abstract:
- lang: eng
text: "We design fast deterministic algorithms for distance computation in the Congested
Clique model. Our key contributions include:\r\n A (2+ϵ)-approximation for
all-pairs shortest paths in O(log2n/ϵ) rounds on unweighted undirected graphs.
With a small additional additive factor, this also applies for weighted graphs.
This is the first sub-polynomial constant-factor approximation for APSP in this
model.\r\n A (1+ϵ)-approximation for multi-source shortest paths from O(n−−√)
sources in O(log2n/ϵ) rounds on weighted undirected graphs. This is the first
sub-polynomial algorithm obtaining this approximation for a set of sources of
polynomial size.\r\n\r\nOur main techniques are new distance tools that are obtained
via improved algorithms for sparse matrix multiplication, which we leverage to
construct efficient hopsets and shortest paths. Furthermore, our techniques extend
to additional distance problems for which we improve upon the state-of-the-art,
including diameter approximation, and an exact single-source shortest paths algorithm
for weighted undirected graphs in O~(n1/6) rounds. "
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria). We thank Mohsen Ghaffari, Michael Elkin and Merav Parter for fruitful
discussions. This project has received funding from the European Union’s Horizon
2020 Research And Innovation Program under Grant Agreement No. 755839.
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Keren
full_name: Censor-Hillel, Keren
last_name: Censor-Hillel
- first_name: Michal
full_name: Dory, Michal
last_name: Dory
- first_name: Janne
full_name: Korhonen, Janne
id: C5402D42-15BC-11E9-A202-CA2BE6697425
last_name: Korhonen
- first_name: Dean
full_name: Leitersdorf, Dean
last_name: Leitersdorf
citation:
ama: Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest
paths in the congested clique. Distributed Computing. 2021;34:463-487.
doi:10.1007/s00446-020-00380-5
apa: Censor-Hillel, K., Dory, M., Korhonen, J., & Leitersdorf, D. (2021). Fast
approximate shortest paths in the congested clique. Distributed Computing.
Springer Nature. https://doi.org/10.1007/s00446-020-00380-5
chicago: Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf.
“Fast Approximate Shortest Paths in the Congested Clique.” Distributed Computing.
Springer Nature, 2021. https://doi.org/10.1007/s00446-020-00380-5.
ieee: K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate
shortest paths in the congested clique,” Distributed Computing, vol. 34.
Springer Nature, pp. 463–487, 2021.
ista: Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2021. Fast approximate
shortest paths in the congested clique. Distributed Computing. 34, 463–487.
mla: Censor-Hillel, Keren, et al. “Fast Approximate Shortest Paths in the Congested
Clique.” Distributed Computing, vol. 34, Springer Nature, 2021, pp. 463–87,
doi:10.1007/s00446-020-00380-5.
short: K. Censor-Hillel, M. Dory, J. Korhonen, D. Leitersdorf, Distributed Computing
34 (2021) 463–487.
date_created: 2020-06-07T22:00:54Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2024-03-07T14:43:39Z
day: '01'
department:
- _id: DaAl
doi: 10.1007/s00446-020-00380-5
external_id:
arxiv:
- '1903.05956'
isi:
- '000556444600001'
intvolume: ' 34'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s00446-020-00380-5
month: '12'
oa: 1
oa_version: Published Version
page: 463-487
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: Distributed Computing
publication_identifier:
eissn:
- 1432-0452
issn:
- 0178-2770
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '6933'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Fast approximate shortest paths in the congested clique
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 34
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'
...