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  • Machine Learning Systems
    • Machine Learning Systems - Index
      • MLSys Papers - Short Notes
      • [2011 NSDI] Dominant Resource Fairness: Fair Allocation of Multiple Resource Types
      • [2014 OSDI] Scaling Distributed Machine Learning with the Parameter Server
      • [2018 OSDI] Gandiva: Introspective Cluster Scheduling for Deep Learning
      • [2018 SIGCOMM] Chameleon: Scalable Adaptation of Video Analytics via Temporal and Cross-camera ...
      • [2018 NIPS] Dynamic Space-Time Scheduling for GPU Inference
      • [2019 ATC] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
      • [2019 NSDI] Tiresias: A GPU Cluster Manager for Distributed Deep Learning
      • [2019 SOSP] ByteScheduler: A Generic Communication Scheduler for Distributed DNN Training ...
      • [2019 SOSP] PipeDream: Generalized Pipeline Parallelism for DNN Training
      • [2019 SOSP] Parity Models: Erasure-Coded Resilience for Prediction Serving Systems
      • [2019 NIPS] GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
      • [2019 SC] ZeRO: memory optimizations toward training trillion parameter models
      • [2020 OSDI] Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
      • [2020 OSDI] AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
      • [2020 OSDI] BytePS: A High Performance and Generic Framework for Distributed DNN Training
      • [2020 SIGCOMM] Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
        • [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
    • Big Data Systems - Index
      • Big Data Systems Papers - Short Notes
      • [2003 SOSP] The Google File System
      • [2004 OSDI] MapReduce: Simplified Data Processing on Large Clusters
      • [2010 SIGMOD] Pregel: A System for Large-Scale Graph Processing
      • [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
  • Earlier Readings & Notes
    • High Performance Computing Course Notes
      • Lecture 1: Course Overview
      • Lecture 2: From Code to Instructions. The FDX Cycle. Instruction Level Parallelism.
      • Lecture 3: Superscalar architectures. Measuring Computer Performance. Memory Aspects.
      • Lecture 4: The memory hierarchy. Caches.
      • Lecture 5: Caches, wrap up. Virtual Memory.
      • Lecture 6: The Walls to Sequential Computing. Moore’s Law.
      • Lecture 7: Parallel Computing. Flynn's Taxonomy. Amdahl's Law.
      • Lecture 8: GPU Computing Intro. The CUDA Programming Model. CUDA Execution Configuration.
      • Lecture 9: GPU Memory Spaces
      • Lecture 10: GPU Scheduling Issues.
      • Lecture 11: Execution Divergence. Control Flow in CUDA. CUDA Shared Memory Issues.
      • Lecture 12: Global Memory Access Patterns and Implications.
      • Lecture 13: Atomic operations in CUDA. GPU ode optimization rules of thumb.
      • Lecture 14: CUDA Case Studies. (1) 1D Stencil Operation. (2) Vector Reduction in CUDA.
      • Lecture 15: CUDA Case Studies. (3) Parallel Prefix Scan on the GPU. Using Multiple Streams in CUDA.
      • Lecture 16: Streams, and overlapping data copy with execution.
      • Lecture 17: GPU Computing: Advanced Features.
      • Lecture 18: GPU Computing with thrust and cub.
      • Lecture 19: Hardware aspects relevant in multi-core, shared memory parallel computing.
      • Lecture 20: Multi-core Parallel Computing with OpenMP. Parallel Regions.
      • Lecture 21: OpenMP Work Sharing.
      • Lecture 22: OpenMP Work Sharing
      • Lecture 23: OpenMP NUMA Aspects. Caching and OpenMP.
      • Lecture 24: Critical Thinking. Code Optimization Aspects.
      • Lecture 25: Computing with Supercomputers.
      • Lecture 26: MPI Parallel Programming General Introduction. Point-to-Point Communication.
      • 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
      • 1.2 Gossip, Membership, and Grids
      • 1.3 P2P Systems
      • 1.4 Key-Value Stores, Time, and Ordering
      • 1.5 Classical Distributed Algorithms
      • 4.1 Spark, Hortonworks, HDFS, CAP
      • 4.2 Large Scale Data Storage
    • 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
      • Biscuit: The benefits and costs of writing a POSIX kernel in a high-level language
      • Data Domain: Avoiding the Disk Bottleneck in the Data Domain Deduplication File System
      • Disco: Running Commodity Operating Systems on Scalable Multiprocessors
      • FFS: A Fast File System for UNIX
      • From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
      • LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation
      • LFS: The Design and Implementation of a Log-Structured File System
      • Lottery Scheduling: Flexible Proportional-Share Resource Management
      • Memory Resource Management in VMware ESX Server
      • Monotasks: Architecting for Performance Clarity in Data Analytics Frameworks
      • NFS: Sun's Network File System
      • OptFS: Optimistic Crash Consistency
      • RAID: A Case for Redundant Arrays of Inexpensive Disks
      • 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
      • Scheduler Activations: Effective Kernel Support for the User-Level Management of Parallelism
      • 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
    • Earlier Notes
      • How to read a paper
  • FIXME
    • Template for Paper Reading Notes
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On this page
  • Summary
  • Background & Motivation
  • Design & Implementation
  • Which layer should ByteScheduler be implemented in to make it more general?
  • Unified abstraction for communication tasks
  • Interaction with framework engines and crossing the global barrier
  • Auto-tuning partition size and credits using Bayesian Optimization
  • Comparisons with P3 and TicTac
  • Evaluation
  • Links & References

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  1. Machine Learning Systems
  2. Machine Learning Systems - Index

[2019 SOSP] ByteScheduler: A Generic Communication Scheduler for Distributed DNN Training ...

...Acceleration

Previous[2019 NSDI] Tiresias: A GPU Cluster Manager for Distributed Deep LearningNext[2019 SOSP] PipeDream: Generalized Pipeline Parallelism for DNN Training

Last updated 2 years ago

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Summary

Priority-based communication scheduling + tensor partitioning: acceleration! Fig. 2 is a good toy example that showcases why the default order of communication (FIFO) in current ML frameworks is suboptimal. However, prior systems (P3 and TicTac) that try to tackle this are not generic, in the sense that each of them only targets one combination of DL framework & network stack. Moreover, existing work does not adapt well to different system setups.

In contrast, ByteScheduler is generic (framework/communication method-agnostic), which required some intricate engineering efforts/techniques. Also, ByteScheduler proposes a BO-based auto-tuning algorithm to search for the best system parameters (e.g., tensor partition sizes) under different environments (DNN models, communication paradigms, bandwidth, etc.).

Background & Motivation

In distributed DNN training using data parallelism, the default ML framework engines execute communication operations in a FIFO order, as the underlying communication stack (PS/all_reduce, TCP/RDMA) is inherently based on FIFO queues. However, this is suboptimal: if some communication operations are prioritized, the training can be sped up.

Tensor partitioning is a technique that enables more flexible priority-based scheduling. Without partitioning, a large, low-priority tensor might block high-priority tensors. Instead, the tensors can be partitioned before being en-queued, and high-priority tensor partitions can jump ahead of the queue after they arrive.

Design & Implementation

Which layer should ByteScheduler be implemented in to make it more general?

The five original layers are shown above. After some thoughtful thinking, the authors placed ByteScheduler at the high-level API implementation layer in the framework. For each ML framework, a shim layer ("plugin") is designed to wrap the original operation into a unified "CommTask" abstraction.

Unified abstraction for communication tasks

A single interface, Core.enqueue(CommTask), is exposed to the plugins. Once a communication tensor arrives, it is first wrapped into a CommTask. Then, the Core partitions it into SubCommTasks and decides when to send each. Four CommTask interfaces are implemented:

  • CommTask.partition(size): Partitions a CommTask into multiple SubCommTasks with tensors no larger than the specified size. This invokes a callback in the plugin, as tensor partitioning is framework-dependent. This has a low overhead, as DL frameworks provide zero-copy APIs for tensor partitioning.

  • CommTask.notify_ready(): The engine uses this interface to notify the Core about a tensor being ready, so it can be actually scheduled.

  • CommTask.start(): The Core calls this to let engines and the underlying communication stacks send the tensor.

  • CommTask.notify_finish(): The framework engines notify the Core once the communication (push/pull/all_reduce) finishes so that the Core can continue scheduling more Tasks.

Interaction with framework engines and crossing the global barrier

Auto-tuning partition size and credits using Bayesian Optimization

Comparisons with P3 and TicTac

P3 and TicTac, both in MLSys '19, employ similar ideas and techniques (transmission prioritization via tensor partitioning & reordering). However, both systems target specific training setups (e.g., P3 targets MXNet PS + TCP), while ByteScheduler devotes a significant chunk of engineering efforts on the system design so that it not only outperforms prior systems but also works well with different training configurations.

Evaluation

The paper provided the reasoning for different speedups in different setups.

Links & References

  • Paper PDF

  • Presentation video at SOSP '19

  • bytescheduler on GitHub

The training speedup of priority scheduling is 44%!