Preprint Article Version 1 This version is not peer-reviewed

JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode

Version 1 : Received: 19 July 2020 / Approved: 19 July 2020 / Online: 19 July 2020 (21:22:01 CEST)

How to cite: Foldi, T.; von Csefalvay, C.; Perez, N.A. JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode. Preprints 2020, 2020070450 (doi: 10.20944/preprints202007.0450.v1). Foldi, T.; von Csefalvay, C.; Perez, N.A. JAMPI: Efficient Matrix Multiplication in Spark Using Barrier Execution Mode. Preprints 2020, 2020070450 (doi: 10.20944/preprints202007.0450.v1).

Abstract

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

Subject Areas

Apache Spark; distributed computing; distributed matrix algebra; deep learning; matrix primitives

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