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
  • Garbage collection
  • Avoiding heap exhaustion
  • Evaluation
  • New Vocabulary
  • Links

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

Biscuit: The benefits and costs of writing a POSIX kernel in a high-level language

One-line Summary

This paper analyzes (duh) the benefits and costs of writing a POSIX kernel in a high-level language, Go.

Paper Structure Outline

  1. Introduction

  2. Related work

  3. Motivation

    1. Why C?

    2. Why an HLL?

  4. Overview

  5. Garbage Collection

    1. Go's collector

    2. Biscuit's heap size

  6. Avoiding heap exhaustion

    1. Approach: reservations

    2. How Biscuit reserves

    3. Static analysis to find s

      1. Basic MAXLIVE operation

      2. Handling loops

      3. Kernel threads

    4. Limitations

    5. Heap exhaustion summary

  7. Implementation

  8. Evaluation

    1. Biscuit's use of HLL features

    2. Potential to reduce bugs

    3. Experimental Setup

    4. HLL tax

    5. GC delays

    6. Sensitivity to heap size

    7. Go versus C

      1. Ping-pong

      2. Page-faults

    8. Biscuit versus Linux

    9. Handling kernel heap exhaustion

    10. Lock-free lookups

  9. Discussion and future work

  10. Conclusions

Background & Motivation

The main reason for using low level languages like C to implement a kernel is that C supports low-level techniques that can help performance (pointer arithmetic, explicit memory allocation, etc.).

High level languages (HLL), on the other hand, have some potential advantages compared to C:

  1. Automatic memory management: reduces programmer effort and use-after-free bugs

  2. Type-safety: detects bugs

  3. Runtime typing and method dispatch: helps with abstraction

  4. Language support for threads and synchronization eases concurrent programming

With the idea of exploring the possibility of using a HLL to implement a monolithic POSIX-style kernel in mind, the authors present Biscuit, a kernel written in Go, which has good performance.

Design and Implementation

The Biscuit kernel is written using 27583 lines of Go, 1546 lines of assembly, and no C. Biscuit provides 58 syscalls and it has enough POSIX compatibility to run some existing server programs (NGINX, Redic, etc.).

Garbage collection

Biscuit uses Go's collector, which suspends ordinary execution on all cores ("stop-the-world" pause of ~10μs) twice during a collection. This hurts tail latency the most, and it's especially bad for machines that are dependent on pauses (e.g., datacenters).

Avoiding heap exhaustion

Heap exhaustion refers to live kernel data completely filling the RAM allocated for the heap. Waiting for memory in allocator might lead to deadlocks; Checking and handling allocation failure (like C kernels) is difficult to get right, and Go does not expose failed allocations. Biscuit uses reservations as a solution:

A syscall does not start until either it can reserve enough heap memory or a killer thread frees up some memory. To execute a syscall,

reserve()
    (no locks held)
    evict, kill
    wait...
sys_read()
    ...
unreserve()

Evaluation

Some missing features of Biscuit:

  • Scheduling priority (relies on Go runtime scheduler)

  • Does not handle large multicore machines or NUMA

  • Does not swap or page out to disk

  • Does not implement reverse page mappings (revoke shared pages)

  • Security features (Users, access control lists, address space randomization)

  • 58 out of 300-400 syscalls

The experiments used three kernel-intensive applications: CMailbench, NGINX, and Redis.

  • Tput: throughput in application requests per second

  • Prologue cycles: the fraction of total time used by compiler-generated code at the start of each function that checks whether the stack must be expanded, and whether the garbage collector needs a stop-the-world pause

  • Safety cycles: the cost of runtime checks for nil pointers, array and slice bounds, divide by zero, and incorrect dynamic casts

  • Alloc cycles: the time spent in the Go allocator, examining free lists to satisfy allocation requests (but not including concurrent collection work)

New Vocabulary

  • CVE: Common Vulnerabilities and Exposures

  • Code path: the set of specific instructions that are actually executed during a single run of a program or program fragment.

Links

PreviousA File is Not a File: Understanding the I/O Behavior of Apple Desktop ApplicationsNextData Domain: Avoiding the Disk Bottleneck in the Data Domain Deduplication File System

Last updated 4 years ago

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Goroutines
Paper PDF
Presentation Audio at OSDI '18
Presentation Slides
The use of Go improved the outcome of 40 out of 65 Linux execute-code bugs in the CVE database: 8 won't happen at all and 32 will cause runtime error + panic.
The performance of Biscuit is in the same league as Linux
Measurement of HLL tax. Prologue cycles are the most expensive. The cost of the GC cycles increases with the size of live kernel heap.
The performance of code paths are compared using two benchmarks: ping-pong and page-faults. Go has 5% - 15% performance tax.