Optimization Algorithms for Distributed Machine Learning

137 Pages·2023·4.476 MB·English

by  Gauri Joshi

Synthesis Lectures on
Learning, Networks, and Algorithms
Gauri Joshi
Optimization
Algorithms for
Distributed
Machine Learning
Synthesis Lectures on Learning, Networks,
and Algorithms
SeriesEditor
LeiYing,ECE,UniversityofMichigan–AnnArbor,AnnArbor,USA
The series publishes short books on the design, analysis, and management of complex
networkedsystemsusingtoolsfromcontrol,communications,learning,optimization,and
stochasticanalysis.EachLectureisaself-containedpresentationofonetopicbyaleading
expert. The topics include learning, networks, and algorithms, and cover a broad spec-
trumofapplicationstonetworkedsystemsincludingcommunicationnetworks,data-center
networks, social, and transportation networks.
Gauri Joshi
Optimization Algorithms
for Distributed Machine
Learning
GauriJoshi
CarnegieMellonUniversity
Pittsburgh,PA,USA
ISSN2690-4306 ISSN2690-4314 (electronic)
SynthesisLecturesonLearning,Networks,andAlgorithms
ISBN978-3-031-19066-7 ISBN978-3-031-19067-4 (eBook)
https://doi.org/10.1007/978-3-031-19067-4
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Preface
Stochasticgradientdescentisthebackboneofsupervisedmachinelearningtrainingtoday.
Classical SGD was designed to be run on a single computing node, and its error con-
vergence with respect to the number of iterations has been extensively analyzed and
improvedinoptimizationandlearningtheoryliterature.However,duetothemassivetrain-
ing datasets and models used today, running SGD at a single node can be prohibitively
slow. This calls for distributed implementations of SGD, where gradient computation
and aggregation are split across multiple worker nodes. Although parallelism boosts the
amount of data processed per iteration, it exposes SGD to unpredictable node slowdown
and communication delays stemming from variability in the computing infrastructure.
Thus,thereisacriticalneedtomakedistributedSGDfast,yetrobusttosystemvariability.
In this book, we will discuss state-of-the-art algorithms in large-scale machine learn-
ing that improve the scalability of distributed SGD via techniques such as asynchronous
aggregation, local updates, quantization and decentralized consensus. These methods
reduce the communication cost in several different ways—asynchronous aggregation
allows overlap between communication and local computation, local updates reduce the
communication frequency thus amortizing the communication delay across several iter-
ations, quantization and sparsification methods reduce the per-iteration communication
time, and decentralized consensus offers spatial communication reduction by allowing
different nodes in a network topology to train models and average them with neighbors
in parallel.
For each of the distributed SGD algorithms presented here, the book also provides
an analysis of its convergence. However, unlike traditional optimization literature, we do
not only focus on the error versus iterations convergence, or the iteration complexity. In
distributed implementations, it is important to study the error versus wallclock time con-
vergence because the wallclock time taken to complete each iteration is impacted by the
synchronizationandcommunicationprotocol.Wemodelcomputationandcommunication
delaysasrandomvariablesanddeterminetheexpectedwallclockruntimeperiterationof
the various distributed SGD algorithms presented in this book. By pairing this runtime
analysis with the error convergence analysis, one can get a true comparison of the con-
vergence speed of different algorithms. The book advocates a system-aware philosophy,
vii
viii Preface
which is cognizant of computation, synchronization and communication delays, toward
the design and analysis of distributed machine learning algorithms.
This book would not have been possible without the wonderful research done by my
students and collaborators. I thank them for helping me learn the material presented in
this book. Our research was generously supported by several funding agencies including
the National Science Foundations and research awards from IBM, Google and Meta. I
was also inspired by the enthusiasm of the students who took my class on large-scale
machine learninginfrastructure over the past few years. Finally, I am immenselygrateful
to my family and friends for their constant support and encouragement.
Pittsburgh, PA, USA Gauri Joshi
August 2022
Contents
1 DistributedOptimizationinMachineLearning ......................... 1
1.1 SGD in Supervised Machine Learning .............................. 1
1.1.1 Training Data and Hypothesis ............................... 1
1.1.2 Empirical Risk Minimization ................................ 2
1.1.3 Gradient Descent .......................................... 2
1.1.4 Stochastic Gradient Descent ................................. 3
1.1.5 Mini-batch SGD ........................................... 3
1.1.6 Linear Regression .......................................... 4
1.1.7 Logistic Regression ........................................ 6
1.1.8 Neural Networks ........................................... 6
1.2 Distributed Stochastic Gradient Descent ............................. 7
1.2.1 The Parameter Server Framework ............................ 8
1.2.2 The System-Aware Design Philosophy ........................ 8
1.3 Scalable Distributed SGD Algorithms ............................... 9
1.3.1 Straggler-Resilient and Asynchronous SGD ................... 9
1.3.2 Communication-Efficient Distributed SGD .................... 10
1.3.3 Decentralized SGD ........................................ 10
References ........................................................... 11
2 Calculus,ProbabilityandOrderStatisticsReview ....................... 13
2.1 Calculus and Linear Algebra ....................................... 13
2.1.1 Norms and Inner Products .................................. 13
2.1.2 Lipschitz Continuity and Smoothness ......................... 14
2.1.3 Strong Convexity .......................................... 16
2.2 Probability Review ............................................... 17
2.2.1 Random Variable .......................................... 18
2.2.2 Expectation and Variance ................................... 18
2.2.3 Some Canonical Random Variables .......................... 19
2.2.4 Bayes Rule and Conditional Probability ...................... 20
ix
x Contents
2.3 Order Statistics .................................................. 22
2.3.1 Order Statistics of the Exponential Distribution ................ 22
2.3.2 Order Statistics of the Uniform Distribution ................... 24
2.3.3 Asymptotic Distribution of Quantiles ......................... 24
3 ConvergenceofSGDandVariance-ReducedVariants .................... 27
3.1 Gradient Descent (GD) Convergence ............................... 27
3.1.1 Effect of Learning Rate and Other Parameters ................. 28
3.1.2 Iteration Complexity ....................................... 29
3.2 Convergence Analysis of Mini-batch SGD ........................... 29
3.2.1 Effect of Learning Rate and Mini-batch Size .................. 32
3.2.2 Iteration Complexity ....................................... 32
3.2.3 Non-convex Objectives ..................................... 33
3.3 Variance-Reduced SGD Variants ................................... 34
3.3.1 Dynamic Mini-batch Size Schedule .......................... 34
3.3.2 Stochastic Average Gradient (SAG) .......................... 35
3.3.3 Stochastic Variance Reduced Gradient (SVRG) ................ 36
References ........................................................... 38
4 SynchronousSGDandStraggler-ResilientVariants ...................... 39
4.1 Parameter Server Framework ...................................... 39
4.2 Distributed Synchronous SGD Algorithm ........................... 40
4.3 Convergence Analysis ............................................ 41
4.3.1 Iteration Complexity ....................................... 42
4.4 Runtime per Iteration ............................................. 42
4.4.1 Gradient Computation and Communication Time .............. 43
4.4.2 Expected Runtime per Iteration .............................. 43
4.4.3 Error Versus Runtime Convergence .......................... 45
4.5 Straggler-Resilient Variants ........................................ 45
4.5.1 K-Synchronous SGD ....................................... 46
4.5.2 K-Batch-Synchronous SGD ................................. 47
References ........................................................... 49
5 AsynchronousSGDandStaleness-ReducedVariants .................... 51
5.1 The Asynchronous SGD Algorithm ................................. 51
5.1.1 Comparison with Synchronous SGD ......................... 52
5.2 Runtime Analysis ................................................ 53
5.2.1 Runtime Speed-Up Compared to Synchronous SGD ............ 53
5.3 Convergence Analysis ............................................ 54
5.3.1 Implications of the Asynchronous SGD Convergence Bound .... 57
5.4 Staleness-Reduced Variants of Asynchronous SGD ................... 57
5.4.1 K-Asynchronous SGD ..................................... 58