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
  • [2019 arXiv] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
  • [2019 MLSys] BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy
  • [2020 MLSys] Blink: Fast and Generic Collectives for Distributed ML
  • [2021 ICML] Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
  • [2021 arXiv] Synthesizing Collective Communication Algorithms for Heterogeneous Networks with TACCL
  • [2021 SC] Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
  • [2022 OSDI] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters

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

MLSys Papers - Short Notes

PreviousMachine Learning Systems - IndexNext[2011 NSDI] Dominant Resource Fairness: Fair Allocation of Multiple Resource Types

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[2019 arXiv] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

This work proposes tensor parallelism (TP), where tensors are partitioned across devices and are only aggregated for operations that require the whole tensor. A key insight of TP is that matrix multiplication can be split between multiple GPUs to parallelize computation and save memory.

Each transformer layer consists of a self-attention block followed by a two-layer, multi-layer perceptron (MLP). To parallelize an MLP, column parallelism can be used to split the matrix multiplication, and synchronizations are not needed until the very end of the computation. Parallelizing the multi-headed attention layers is even easier since they are already inherently parallel. As a result, each transformer layer requires two allreduce during the forward pass and two allreduce during the backward pass.

Note that using TP requires a super fast network for near-theoretical-optimal performance, and in real life, TP is usually used in conjugation with other forms of parallelism.

[2019 MLSys] BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy

BlueConnect adapts to the hierarchy of communication bandwidths by leveraging topology-awareness to fully utilize the heterogeneous network architecture. It decomposes all-reduce (reduce-scatter + all-gather) into multiple stages of parallelizable reduce-scatter & all-gather, which provides more granularity and flexibility to map operations to the heterogeneous underlying network hierarchy.

[2020 MLSys] Blink: Fast and Generic Collectives for Distributed ML

This paper address the problem of link under-utilization due to topology heterogeneity in distributed ML training. Topology heterogeneity mainly comes from (1) differing server configurations (e.g, different NVLink topologies across generations of DGX nodes) and (2) scheduler’s topology-agnostic placements/allocations (e.g., an 8-GPU job uses 3 GPUs in an 8-GPU DGX node and 5 GPUs from another). To handle topology heterogeneity from hardware generations or partial allocations from cluster schedulers, Blink dynamically generates optimal communication primitives for a given topology. Blink models collective communication operations as flows on a directed graph and uses a spanning-tree packing algorithm to maximize link bandwidth utilization.

[2021 ICML] Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size

Serving specialized CNNs (e.g., for offline video analytics) have low arithmetic intensity, leading to the severe under-utilization of server-grade accelerators. Increasing the batch size is a popular technique to boost the arithmetic intensity, utilization, and application-level throughput by amortizing the cost of loading a CNN’s weights from memory. However, it suffers from diminishing returns. This paper proposes a technique to redesign specialized CNNs with the purpose of boosting the inference utilization and throughput. The key insight is that, once arithmetic intensity has plateaued due to increased batch size, reading/writing activations accounts for most of the memory traffic in specialized CNNs. The authors show that this memory traffic can be significantly reduced, while performing the same number of FLOPs, by jointly decreasing the size of the batch of input/output activations for a layer and increasing the layer’s width.

Compared to vanilla CNNs, FoldedCNNs have improvements on the throughput and the accelerator utilization while suffering slight accuracy loss.

[2021 arXiv] Synthesizing Collective Communication Algorithms for Heterogeneous Networks with TACCL

TACCL encodes a profiled topology and input size into a synthesis problem to generate optimized communication algorithms.

NCCL uses the topology of GPU connections and NIC placement along with buffer size to decide between two main types of communication algorithms — Ring and Tree, but it is agnostic to the exact performance profile of the links, and thus is often multiple times slower than TACCL’s custom collectives.

[2021 SC] Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines

Chimera is yet another pipeline parallelism paradigm. Compared with the other STOA systems, it reduces more compute idleness and has a more balanced activation memory consumption.

[2022 OSDI] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters

Nowadays, DNN workload schedulers in shared GPU clusters consider GPU as the dominant resource and only allocate other types of resources (e.g., CPU and memory) proportional to the number of GPUs. However, different jobs have various sensitivity to these other types of resources, which leads to sub-optimal allocation results by current schedulers.

Synergy is an idea that applies to all existing scheduling policies: It uses profiling to infer a workload's sensitivity to different resources and performs multi-resource workload-aware resource allocation. The key nugget is to co-locate two jobs on the same server, one of which is CPU-sensitive and the other is not, so that while the CPU-insensitive job does not hurt from the reduced resource allocation, the CPU-sensitive job can gain a higher throughput, benefiting the cluster-wide aggregate throughput and metrics like avg JCT, makespan, fairness, etc.

The main technical contributions of this paper are two-fold:

  • Profiling the workloads: Naively profiling all possible resource configurations can be expensive due to the large combination space. Synergy introduces an optimistic profiling technique that exploits the predictability in the relationship between job throughput and memory allocation. As for the CPU allocation, Synergy empirically profiles the job for varying, discrete CPU allocations at full memory allocation. The profiling time is tens of minutes, which is reasonable considering most DNN jobs are long-running.

  • Encorporating resource-sensitivity-awareness into existing scheduling algorithms.