Index

• A Berkeley View of Systems Challenges for AI - Stoica et al., 2017

Data Validation

• Automating Large-Scale Data Quality Verification - Schelter et al., VLDB '18

  • Presented a system for automating the verification of data quality at scale

• Data Validation for Machine Learning - Breck et al., SysML '19

• Designed a system to monitor the quality of data fed into machine learning algorithm in Google

Federated/Decentralized Learning

• Towards Federated Learning at Scale: System Design - Bonawitz et al., SysML '19 [Summary]

  • Discussed the general architecture and protocol of federated learning in Google

• Communication-Efficient Learning of Deep Networks from Decentralized Data - McMahan et al.,arXiv '17 [Summary]

  • Described the Federated averaging algorithm

• The Non-IID Data Quagmire of Decentralized Machine Learning - Hsieh et al., arXiv '19 [Summary]

  • Studied the problem of non-IID data partition, and designed a system-level approach that adapts the communication frequency to reflect the skewness in the data.

• Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data - Yang et al., WWW' 21

  • Studied the impact of system heterogeneity on existing federated learning algorithms and proposed several nice observations on potential impact factors

Distributed Machine Learning

• Large Scale Distributed Deep Networks - Dean et al., NIPS '12

• Introduced the idea of Model Parallelism and Data Parallelism, as well as Google's first-generation Deep Network platform, DistBelief. The ideas are "old", but it is a must-read if you are interested in distributed Deep Network platforms.

• More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server - Ho et al., NIPS '13 [Summary]

  • Discussed the Stale Synchronous Parallel(SSP) and implementation of SSP parameter server

• Scaling Distributed Machine Learning with the Parameter Server - Li et al., OSDI '14 [Summary]

  • Described the architecture and protocol of parameter server

• Project Adam: Building an Efficient and Scalable Deep Learning Training System - Chilimbi et al., OSDI '14

  • Described the design and implementation of a distributed system called Adam comprised of commodity server machines to train deep neural networks.

• TensorFlow: A System for Large-Scale Machine Learning - Abadi et al., OSDI '16

  • The core idea behind TensorFlow is the dataflow(with mutable state) representation of the Deep Networks, which they claim to subsume existing work on parameter servers, and offers a uniform programming model that allows users to harness large-scale heterogeneous systems, both for production tasks and for experimenting with new approaches.

• Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds - Hsieh et al., NSDI '17 [Summary]

  • Designed a geo-distributed ML system which differentiates communication within data center and across data centers and presented the Approximate Synchronous Parallel model(similar to SSP).

• Gradient Coding: Avoiding Stragglers in Distributed Learning - Tandon et al., ICML '17 [Summary]

  • Coded Computation

• Revisiting Distributed Synchronous SGD - Chen et al., arXiv '17

  • Proposed a solution to mitigate stragglers: adding b extra workers, but as soon as the parameter servers receive gradients from any N workers, they stop waiting and update their parameters using the N gradients.

• Adaptive Communication Strategies in Local-Update SGD - Wang et al., SysML '18 [Summary]

  • Proposed an adaptive algorithm for choosing $\tau$ in asynchronous training. (Change $\tau$ as algorithm converges)
• Ray: A Distributed Framework for Emerging AI Applications - Moritz et al., OSDI '18 [Summary]

• Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads - Jeon et al., ATC '19 [Summary]

• PipeDream: Generalized Pipeline Parallelism for DNN Training - Narayanan et al., SOSP '19 [Summary]

  • Proposed Pipeline-parallel training that combines data and model parallelism with pipelining.

• A Generic Communication Scheduler for Distributed DNN Training Acceleration - Peng et al., SOSP '19 [Summary]

  • Key insight: Communication of former layers of a neural network has higher priority and can preempt communication of latter layers.

• HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism - Park et al., ATC '20

• A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters - Jiang et al., OSDI '20

  • Presented a concise overview of the theoretical performance of PS and All-reduce architecture

  • Proposed an architecture to accelerate distributed DNN training by 1) leveraging spare CPU and network resources, 2) optimizing both inter-machine and intra-machine communication, and 3) move parameter updates to GPUs

Deep Learning Scheduler

• Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters - Peng et al., EuroSys '18

• Gandiva: Introspective Cluster Scheduling for Deep Learning - Xiao et al., OSDI '18 [Summary]

• Tiresias: A GPU Cluster Manager for Distributed Deep Learning - Gu et al., NSDI '19 [Summary]

• Themis: Fair and Efficient GPU Cluster Scheduling - Mahajan et al., NSDI '20

• AntMan: Dynamic Scaling on GPU Clusters for Deep Learning - Xiao et al., OSDI '20

Inference

• Clipper: A Low-Latency Online Prediction Serving System - Crankshaw et al., NSDI '17 [Summary]

  • Discussed the challenge of prediction serving systems and presented their general-purpose low-latency prediction serving system.

• Pretzel: Opening the Black Box of Machine Learning Prediction Serving Systems - Lee et al., OSDI '18

• InferLine: ML Prediction Pipeline Provisioning and Management for Tight Latency Objectives - Crankshaw et al., arXiv '18

• DeepCPU: Serving RNN-based Deep Learning Models 10x Faster - Zhang et al., ATC '18

• GRNN: Low-Latency and Scalable RNN Inference on GPUs - Holmes et al., EuroSys '19

• MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving - Zhang et al., ATC '19

  • Proposes a SLO-aware model scheduling and scaling by selecting between AWS EC2 and AWS lambda to absorb load bursts.

• Optimizing CNN Model Inference on CPUs - Liu et al., ATC '19

• Parity Models: Erasure-Coded Resilience for Prediction Serving Systems - Kosaian et al, SOSP '19 [Summary]

  • A Learning-based approach to achieve erasure-coded resilience for Neural Networks.

• Nexus: a GPU cluster engine for accelerating DNN-based video analysis - Shen et al., SOSP '19 [Summary]

• Serving DNNs like Clockwork: Performance Predictability from the Bottom Up - Gujarati et al., OSDI '20

Machine Learning Systems in industry

• Uber's Machine Learning Platform - Michelangelo: [Blog and Video]

• Horovod: fast and easy distributed deep learning in TensorFlow - Sergeev et al., 2018 [Github][Summary]

  • Horovod is Uber's distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and easy to use.

• Machine Learning at Facebook: Understanding Inference at the Edge - Wu et al., 2018 [Summary]

  • Facebook's work on bringing machine learning inference to the edge.

Misc.

• Cartel: A System for Collaborative Transfer Learning at the Edge - Daga et al., SoCC '19 [Summary]

  • Proposed a framework for Collaborative Learning

• Collaborative Learning between Cloud and End Devices: An Empirical Study on Location Prediction - Lu et al., SEC '19 [Summary]

• DeepXplore: Automated Whitebox Testing of Deep Learning Systems - Pei et al., SOSP '17 [Summary]

• A unifying view on dataset shift in classification - Moreno-Torres et al., 2010

  • Explained various types of data shift(i.e. covariate shift, prior probability shift, and Concept Shift)

• A First Look at Deep Learning Apps on Smartphones - Xu et al., WWW '19