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      • [2011 NSDI] Dominant Resource Fairness: Fair Allocation of Multiple Resource Types
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      • [2018 OSDI] Gandiva: Introspective Cluster Scheduling for Deep Learning
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      • [2019 NSDI] Tiresias: A GPU Cluster Manager for Distributed Deep Learning
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      • [2020 OSDI] Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
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      • [2020 OSDI] BytePS: A High Performance and Generic Framework for Distributed DNN Training
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        • [2020 MLSys] Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
      • [2020 EuroSys] AlloX: Compute Allocation in Hybrid Clusters
      • [2020 VLDB] PyTorch Distributed: Experiences on Accelerating Data Parallel Training
      • [2020 NetAI] Is Network the Bottleneck of Distributed Training?
      • [2020 NSDI] Themis: Fair and Efficient GPU Cluster Scheduling
      • [2021 MLSys] Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
      • [2021 VLDB] Analyzing and Mitigating Data Stalls in DNN Training
      • [2021 FAST] CheckFreq: Frequent, Fine-Grained DNN Checkpointing
      • [2021 EuroMLSys] Interference-Aware Scheduling for Inference Serving
      • [2021 OSDI] Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
      • [2021 MLSys] Wavelet: Efficient DNN Training with Tick-Tock Scheduling
      • [2021 NSDI] SwitchML: Scaling Distributed Machine Learning with In-Network Aggregation
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      • [2003 SOSP] The Google File System
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      • [2011 NSDI] Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
      • [2012 NSDI] Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster ...
      • [2012 OSDI] PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
      • [2019 FAST] DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed...
      • [2021 HotOS] From Cloud Computing to Sky Computing
      • [2021 EuroSys] NextDoor: Accelerating graph sampling for graph machine learning using GPUs
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    • High Performance Computing Course Notes
      • Lecture 1: Course Overview
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      • Lecture 3: Superscalar architectures. Measuring Computer Performance. Memory Aspects.
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      • Lecture 5: Caches, wrap up. Virtual Memory.
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      • Lecture 27: MPI Parallel Programming Point-to-Point communication: Blocking vs. Non-blocking sends.
      • Lecture 28: MPI Parallel Programming: MPI Collectives. Overview of topics covered in the class.
    • Cloud Computing Course Notes
      • 1.1 Introduction to Clouds, MapReduce
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    • Operating Systems Papers - Index
      • CS 736 @ UW-Madison Fall 2020 Reading List
      • All File Systems Are Not Created Equal: On the Complexity of Crafting Crash-Consistent Applications
      • ARC: A Self-Tuning, Low Overhead Replacement Cache
      • A File is Not a File: Understanding the I/O Behavior of Apple Desktop Applications
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      • FFS: A Fast File System for UNIX
      • From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
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      • RDP: Row-Diagonal Parity for Double Disk Failure Correction
      • Resource Containers: A New Facility for Resource Management in Server Systems
      • ReVirt: Enabling Intrusion Analysis through Virtual-Machine Logging and Replay
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      • SnapMirror: File-System-Based Asynchronous Mirroring for Disaster Recovery
      • The Linux Scheduler: a Decade of Wasted Cores
      • The Unwritten Contract of Solid State Drives
      • Venti: A New Approach to Archival Storage
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  • Evaluation
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  1. Machine Learning Systems
  2. Machine Learning Systems - Index

[2020 NetAI] Is Network the Bottleneck of Distributed Training?

Previous[2020 VLDB] PyTorch Distributed: Experiences on Accelerating Data Parallel TrainingNext[2020 NSDI] Themis: Fair and Efficient GPU Cluster Scheduling

Last updated 2 years ago

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Summary

Distributed training suffers from sub-linear scale-out. The authors argue that this is due to the network not being fully saturated as a result of the poor implementation of network transport. If the network can be fully utilized, distributed training can achieve an almost-linear scale-out. Also, in a highly-utilized network, the extent of gradient compression does not need to be that high.

Background & Motivation

Current distributed DNN training using data parallelism suffers from a sub-linear scaling when scaled out. People have been optimizing the communication phase of distributed training, as the computation phase (the other phase) is embarrassingly parallel and should scale almost linearly. An example of those optimizations is gradient compression, which lies at the application level.

The authors of this paper instead look at the network layer (network-level optimizations do not require changes at the application level).

Evaluation

The authors first argue that the computation phase is not the bottleneck. They found that due to (1) distributed backward pass overlaps with all-reduce and (2) Horovod injects per-layer hooks in distributed training, the computation time has a slight increase in distributed training. However, this inevitable side effect (at most 15%) does not offset the extent (merely 56% - 75%) of the sub-linear scaling.

Then, the only possibility is that the communication phase is the bottleneck, so the authors tried different network bandwidths. Surprisingly, the scaling factor line plateaus after 25Gbps, meaning that a faster network does not necessarily benefit the system.

Links & References

The low network utilization at a high bandwidth explains the issue, as only a small fraction of the bandwidth is properly utilized. This might be explained by TCP being CPU-intensive at high speed (100 Gbps), but modern GPU instances have sufficient CPUs, and the authors found the actual CPU utilization is low. The conclusion is that the poor implementation of network transport cannot fully saturate the available bandwidth during communication.

A what-if simulation analysis shows that under a fully-utilized network, almost-linear scale-out is possible.

Also, gradient compression is useful in low-speed networks, but a large compression ratio is not necessary.

Paper PDF
training-bottleneck on GitHub