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
      • How to Transfer Credits Back to UW-Madison
      • Resources on Learning Academic Writing (for Computer Science)
    • 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
  • Evaluation
  • New Vocabulary
  • Links

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  1. Earlier Readings & Notes
  2. Operating Systems Papers - Index

Disco: Running Commodity Operating Systems on Scalable Multiprocessors

One-line Summary

Disco uses virtual machines to run multiple commodity operating systems on large-scale shared-memory multiprocessors. Disco VMM hides NUMA-ness from non-NUMA aware OSes, requires low effort to implement, and introduces moderate overhead due to virtualization.

Paper Structure Outline

  1. Introduction

  2. Problem Description

  3. A Return to Virtual Machine Monitors

    1. Challenges Facing Virtual Machines

  4. Disco: A Virtual Machine Monitor

    1. Disco's Interface

    2. Implementation of Disco

      1. Virtual CPUs

      2. Virtual Physical Memory

      3. NUMA Memory Management

      4. Virtual I/O Devices

      5. Copy-on-write Disks

      6. Virtual Network Interface

    3. Running Commodity Operating Systems

      1. Necessary Changes for MIPS Architecture

      2. Device Drivers

      3. Changes to the HAL

      4. Other Changes to IRIX

    4. SPLASHOS: A Specialized Operating System

  5. Experimental Results

    1. Experimental Setup and Workloads

    2. Execution Overheads

    3. Memory Overheads

    4. Scalability

    5. Dynamic Page Migration and Replication

  6. Related Work

    1. System Software for Scalable Shared Memory Machines

    2. Virtual Machine Monitors

    3. Other System Software Structuring Techniques

    4. ccNUMA Memory Management

  7. Conclusions

Background & Motivation

The motivation is to enable existing commodity operating systems to handle Non-Uniform Memory Access (NUMA) architectures. Instead of modifying existing operating systems to run on scalable shared-memory multiprocessors, an additional layer (VM monitor) is inserted between the hardware and the OS.

Cache-coherent Non-Uniform Memory Architecture (cc-NUMA) makes hardware scalable, while SMP ensures the same performance to all memory from everywhere. Both ensure correctness, though.

Design and Implementation

The advantages of using virtual machines in the context of this work are:

  • The Disco layer understands the NUMA architecture

  • It's a portability layer

  • Monitors are smaller and easier to understand & trust than operating systems

  • Allows to run different OSes concurrently (almost unmodified)

The drawbacks of using virtual machines are:

  • Overhead: cost of virtualizing

    • Time: VMM (Disco) acts as an emulator. Most instructions can just run, but privileged instructions + TLB instructions must be trapped & emulated

    • Space: Multiple copies (OS code & each OS's file cache) waste memory

  • Resource management: Lack of information to make good policy decisions

    • Lost information about what is being used

      • CPU - idle thread

      • Memory - pages on the free list

  • Communication and Sharing problems:

    • Hard to communicate between standalone VMs

    • Most OSes require exclusive access to disks

Evaluation

This paper started off VMWare (which was founded by authors of Disco in 1998 and successfully commercialized this work) and revived virtual machines for the next 20 years. Now VMs are commodities, and every cloud provider and virtually every enterprise uses VMs today.

New Vocabulary

  • IRIX: A variety of UNIX System V with BSD extensions.

Links

PreviousData Domain: Avoiding the Disk Bottleneck in the Data Domain Deduplication File SystemNextFFS: A Fast File System for UNIX

Last updated 4 years ago

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NUMA:

What is NUMA?
Paper PDF
Paper review notes from CS 443 @ Northwestern by Joseph Paris
Discussion panel from CS 736 @ UW-Madison
Lecture slides from CS 262a @ Berkeley by Prof. Ion Stoica and Ali Ghodsi
1MB
21-Disco-Instructor-Notes Combined with Questions.pdf
pdf
Prof. Andrea's notes on Disco
Course notes by Prof. Andrea. Left: SMP (symmetrical multiprocessor uniform memory access machine), right: cc-NUMA
Disco is a layer between OSes and hardware
High-level challenges of using virtual machines
How Disco virtualizes CPU. Three priviliged levels: user, supervisor, and kernel.
How Disco virtualizes memory. Users generate virtual addresses, OS translates to physical addreses, Disco translates to machine addresses.
Records copy-on-write to track shared data efficiently.
Send becomes additional mapping (emulate device); Copy becomes additional mapping
Changes Disco made to IRIX to improve performance
Pmake & Database do a lot of syscalls, often traps into Disco, which then goes to kernel. The extra 16% overhead for those workloads is due to the extra work handling TLB misses. The kernel time being less is because Disco zeros the pages (does work instead of IRIX).
Disco does a good job sharing buffer cache space across VMs and sharing IRIX text.
No migration + replication, just looking at how much more scalable is Disco than IRIX due to optimizations of not having locks in which IRIX does a bad job at. IRIX on 8-processor cc-NUMA machine. 2VM -> 8VM actually improves because Disco does not have bad lock
Much less time accessing remote memory, more local memory.