Rui's Blog
  • Rui's Blog/Paper Reading Notes - Introduction
  • Personal Blog
    • Personal Blog - Index
      • How to Create Picture-in-Picture Effect / Video Overlay for a Presentation Video
      • How to Do Your Part to Protect the Environment in Wisconsin
      • How to Get a Driver's License in Wisconsin
      • How to Travel from the U.S. to China onboard AA127 in June 2021
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    • Towards applying to CS Ph.D. programs
  • 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
  • One-line Summary
  • Paper Structure Outline
  • Background & Motivation
  • Design and Implementation
  • Allocations as time fractions
  • Effective throughput
  • Policies as optimization problems
  • Realizing the optimal allocation: round-based scheduling + priorities
  • Evaluation
  • Links

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

[2020 OSDI] Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

One-line Summary

Gavel is a scheduler that takes into account the performance heterogeneity of underlying accelerators (GPUs, TPUs, etc.) when training DNN jobs. Gavel makes existing policies heterogeneity-aware and incorporates them as optimization problems.

Paper Structure Outline

  1. Introduction

  2. Background

    1. DNN Training

    2. Performance Optimizations

  3. System Overview

    1. Heterogeneity-Aware Policies

    2. Round-based Scheduling Mechanism

    3. Throughput Estimator

    4. Limitations and Non-Goals

  4. Scheduling Policies

    1. Max-Min Fairness as an Optimization Problem

    2. Other Policies as Optimization Problems

    3. Hierarchical Scheduling Policies

    4. Properties of Gavel's Policies

  5. Scheduling Mechanism

  6. Implementation

  7. Evaluation

    1. Experiment Setup

    2. End-to-End Results on Physical Cluster

    3. End-to-End Results in Simulation

    4. Scalability of Heterogeneity-Aware Policies

    5. Efficacy of Scheduling Mechanism

    6. Impact of Throughput Estimation

  8. Related Work and Discussion

  9. Conclusion

Background & Motivation

For a cluster scheudler, choosing the optimal accelerator types is difficult for three reasons:

  1. Performance heterogeneity: Common deep learning training workloads show heterogeneous performance on different hardware accelerators due to architectural differences. Not being aware of this result in suboptimal allocations.

  2. Generality across policies: Recent scheudlers like Allox and Gandiva optimize for a single scheduling objective, while in reality, clusters may take differnet (and sophisticated) scheduling policies.

  3. Colocation and optimization optimizations: The performance benefits of these optimizations should be considered explicitly while optimizing for global scheduling objectives, since these optimizations are more effective when deployed in a heterogeneity-aware way.

Design and Implementation

Allocations as time fractions

Gavel expresses scheduling policies as optimization problems and produces an allocation matrix that indicates the fraction of time each job should spend on the different accelerators between allocation recomputations (new jobs arriving, old jobs finishing, periodic recomputations, etc).

Effective throughput

Policies as optimization problems

Gavel converted the following policies into optimization problems and included them in the code base:

    • LAS w/ weights

  • Minimize makespan

  • FIFO

  • Shortest job first (SJF)

  • Minimize cost (in public cloud instances)

    • Minimize cost w/ SLOs

  • Hierarchical (multi-level policy: FIFO, fairness, etc.)

Realizing the optimal allocation: round-based scheduling + priorities

With the optimal allocation computed, Gavel tries to dispatch jobs while matching the optimal allocation as close as possible by using two techniques:

  1. Round-based scheduling: Gavel allows users to set the round length (optimal length that allows for the effective approximation is 6 minutes). This mechanism ensures that jobs receive time on accelerator types according to the optimal allocation.

  2. Per-job priority score: In each round, the scheduler runs jobs in decreasing priority order. The priority for each job is computed as the target allocation divided by the number of rounds received.

Evaluation

The paper also covers extensive evaluations on:

  • How well the heterogeneity-aware policies improve objective metrics, both in simulation and physical experiments

  • How do Gavel's policies scale

  • Whether Gavel can accurately estimate the throughputs of co-located jobs when using space sharing

Links

Previous[2019 SC] ZeRO: memory optimizations toward training trillion parameter modelsNext[2020 OSDI] AntMan: Dynamic Scaling on GPU Clusters for Deep Learning

Last updated 2 years ago

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LAS/Max-min fairness: Least Attained Service policy, used by

Finish time fairness: Used by

Tiresias
Themis
Paper PDF
Presentation video at OSDI '20
Presentation slides at OSDI '20
Gavel on GitHub
In this case, job 0 spends 60% of the time on a V100 and 40% of the time on a P100.
m: model, X: allocation matrix, T: throughput matrix
Round-based scheduling
This priority mechanism helps the approximation of the optimal allocation as shown in X^example.
Heterogeneity-aware policies reduces avg. JCT by 1.5x
How well the Gavel scheduling/dispatching mechanism works