Passel: Improved Scalability and Efficiency of Distributed SVM using a Cacheless PGAS Migrating Thread Architecture

2021 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)（2021）

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摘要

Stochastic Gradient Descent (SGD) is a valuable algorithm for large-scale machine learning, but has proven difficult to parallelize on conventional architectures because of communication and memory access issues. The HogWild series of mixed logically distributed and physically multi-threaded algorithms overcomes these issues for problems with sparse characteristics by using multiple local model ve...

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关键词

Support vector machines,Machine learning algorithms,Instruction sets,Scalability,Memory management,Stochastic processes,Computer architecture

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