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  • Rui's Blog/Paper Reading Notes - Introduction
  • Personal Blog
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      • How to Create Picture-in-Picture Effect / Video Overlay for a Presentation Video
      • How to Do Your Part to Protect the Environment in Wisconsin
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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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  • One-line Summary
  • Paper Structure Outline
  • Background & Motivation
  • Design and Implementation
  • Evaluation
  • New Vocabulary
  • Links

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

[2018 OSDI] Gandiva: Introspective Cluster Scheduling for Deep Learning

One-line Summary

The authors present Gandiva, a cluster scheduling framework that employs techniques like time-slicing, migration, intra-job elasticity, and dynamic priority.

Paper Structure Outline

  1. Introduction

  2. Background

  3. DLT Job Characteristics

    1. Sensitivity to locality

    2. Sensitivity to interference

    3. Intra-job predictability

  4. Design

    1. Mechanisms

    2. Scheduling Policy

      1. Reactive Mode

      2. Introspective Mode

  5. Implementation

    1. Scheduler

    2. Modifications to DL toolkits

  6. Evaluation

    1. Micro-benchmarks

    2. Model exploration in a multi-job

    3. Cluster experiments: time-slicing and packing

    4. Cluster experiments: time-slicing and migration

  7. Related Work

  8. Conclusion

Background & Motivation

Today's DNN schedulers (e.g., YARN, Kubernetes) treat deep learning jobs naively (as if they are traditional big-data jobs): A job is scheduled on a set of GPUs exclusively, and the job holds the GPUs until completion. There are some problems:

  1. High Latency (head-of-line blocking): Long DNN jobs have runtimes of hours and days, so we need time-slicing of jobs. However, GPUs are not efficiently virtualizable.

  2. Low Efficiency (fixed decision at the job-placement time): Need the ability to migrate jobs, and the sensitivity to locality varies across jobs.

DLT jobs have the following characteristics:

  1. Sensitivity to locality: Different models have various levels of sensitivity to intra-server and inter-server locality that a DLT scheduler needs to take into account.

  2. Sensitivity to interference: Similarly, different models demonstrate different levels of sensitivity to interference between jobs.

  3. Intra-job predictability: DLT jobs' GPU memory usage reveals a pattern (goes up during forward pass of a minibatch and goes down during backward pass). Gandiva leverages this in three ways:

    1. A job can be split into mini-batch iterations

    2. If suspend/resume is performed during the nadir, less amount of memory needs to be copied from GPU to CPU

    3. The progress rate can be profiled to evaluate the effectiveness of mechanisms

Design and Implementation

Gandiva employs the following mechanisms:

  1. Suspend-Resume and Packing

    1. Suspend-Resume: Intra-job predictability is leveraged to suspend/resume DLT jobs when their GPU usage is at the lowest.

    2. Packing: Run multiple jobs on a GPU simultaneously and let the GPU time-share the jobs, with the premise that the packed jobs do not interfere with each other. It is only considered during overload.

  2. Migration: The set of GPUs assigned to a job can be changed for (1) moving time-sliced jobs to vacated GPUs, (2) moving interfering jobs away from each other, and (3) doing de-fragmentation of the cluster. The migration overhead is as little as a second or two.

  3. Grow-Shrink: # GPUs available for a job can be increased during idle times and shrank when the load goes up.

  4. Profiling: Gandiva profiles each job's time for one forward/backward pass over a minibatch. With this, Gandiva introspects DLT jobs to estimate the rate of progress, e.g. to check if packing helped.

Gandiva's scheduler works in two modes: reactive and introspective. The reactive mode handles events such as job arrivals/departures and machine failures, while the introspective mode monitors and optimizes job placement to improve the overall utilization and the completion time.

Evaluation

New Vocabulary

  • Introspection (反省): The examination of one's own conscious thoughts and feelings.

Links

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Paper PDF
Presentation audio at OSDI '18
Presentation slides at OSDI '18
Presentation video by Muthian Sivathanu, one of the authors and a UW-Madison alumni
When suspending a job, as GPUs are not efficiently virtualizable, the state needs to be moved from GPU to CPU before suspension.
Microbenchmark: Time-slicing
Microbenchmark: Packing
Microbenchmark for AutoML: Gandiva provides much faster hyper-parameter exploration
Cluster utilization