Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence
Conference contribution, 2021

Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel SGD for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We contribute with answering questions in this gap by studying a spectrum of parallel algorithmic implementations of AsyncSGD, aiming to understand how shared-data synchronization influences the convergence properties in fundamental DL applications. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyper-parameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. Leashed-SGD features a natural contention-regulating mechanism, as well as dynamic memory management, allocating space only when needed. We argue analytically about the dynamics of the algorithms, memory consumption, the threads’ progress over time, and the expected contention. The analysis further shows the contention-regulating mechanism that Leashed-SGD enables. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for two prominent deep learning (DL) applications: multilayer perceptrons (MLP) and convolutional neural networks (CNN). We observe the crucial impact of contention, staleness and consistency and show how, thanks to the aforementioned properties, Leashed-SGD provides significant improvements in stability as well as wall-clock time to convergence (from 20-80% up to 4x improvements) compared to the standard lock-based AsyncSGD algorithm and HOGWILD!, while reducing the overall memory footprint.


Karl Bäckström

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Ivan Walulya

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Philippas Tsigas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

35th IEEE International Parallel & Distributed Processing Symposium, 2021
Portland, Oregon, USA,

WASP SAS: Summaries and data-structures for continuous data processing and analysis systems

Wallenberg AI, Autonomous Systems and Software Program, 2018-01-01 -- 2023-01-01.

Subject Categories

Bioinformatics (Computational Biology)

Computer Science

Computer Systems

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