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Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Authors:

Abhinav Jangda,

Amir Hossein Nodehi Sabet,

Madanlal Musuvathi,

Todd Mytkowicz,

Olli SaarikiviAuthors Info & Claims

ASPLOS '22: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

Pages 402 - 416

https://doi.org/10.1145/3503222.3507778

Published: 22 February 2022 Publication History

Abstract

Recent trends towards large machine learning models require both training and inference tasks to be distributed. Considering the huge cost of training these models, it is imperative to unlock optimizations in computation and communication to obtain best performance. However, the current logical separation between computation and communication kernels in machine learning frameworks misses optimization opportunities across this barrier. Breaking this abstraction can provide many optimizations to improve the performance of distributed workloads. However, manually applying these optimizations requires modifying the underlying computation and communication libraries for each scenario, which is both time consuming and error-prone.

Therefore, we present CoCoNet, which contains (i) a domain specific language to express a distributed machine learning program in the form of computation and communication operations, (ii) a set of semantics preserving transformations to optimize the program, and (iii) a compiler to generate jointly optimized communication and computation GPU kernels. Providing both computation and communication as first class constructs allows users to work on a high-level abstraction and apply powerful optimizations, such as fusion or overlapping of communication and computation. CoCoNet enabled us to optimize data-, model- and pipeline-parallel workloads in large language models with only a few lines of code. Our experiments show that CoCoNet significantly outperforms state-of-the-art distributed machine learning implementations.

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Cited By

Cheng SLin JEmani MRaskar SForeman SXie ZVishwanath VKandemir M(2024)Thorough Characterization and Analysis of Large Transformer Model Training At-ScaleProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390348:1(1-25)Online publication date: 21-Feb-2024
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Jangda AYadav MLee IChabbi MSteuwer M(2024)Fast Kronecker Matrix-Matrix Multiplication on GPUsProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638489(390-403)Online publication date: 2-Mar-2024
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Index Terms

Breaking the computation and communication abstraction barrier in distributed machine learning workloads
1. Computing methodologies
  1. Parallel computing methodologies
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. Context specific languages
      1. Domain specific languages

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cover image ACM Conferences

ASPLOS '22: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

February 2022

1164 pages

ISBN:9781450392051

DOI:10.1145/3503222

General Chairs:
Babak Falsafi
EPFL, Switzerland
,
Michael Ferdman
Stony Brook University, USA
,
Program Chairs:
Shan Lu
University of Chicago, USA
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Tom Wenisch
University of Michigan, USA / Google, USA

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Published: 22 February 2022

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https://dl.acm.org/doi/10.1145/3639034
Jangda AYadav MLee IChabbi MSteuwer M(2024)Fast Kronecker Matrix-Matrix Multiplication on GPUsProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638489(390-403)Online publication date: 2-Mar-2024
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