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The new barrier mode in Apache Spark allows embedding distributed deeplearning training as a Spark stage to simplify the distributed trainingworkflow. In Spark, a task in a stage does not depend on any other tasks in thesame stage, and hence it can be scheduled independently. However, severalalgorithms require more sophisticated inter-task communications, similar to theMPI paradigm. By combining distributed message passing (using asynchronousnetwork IO), OpenJDK's new auto-vectorization and Spark's barrier executionmode, we can add non-map/reduce based algorithms, such as Cannon's distributedmatrix multiplication to Spark. We document an efficient distributed matrixmultiplication using Cannon's algorithm, which improves significantly on theperformance of the existing MLlib implementation. Used within a barrier task,the algorithm described herein results in an up to 24 percent performanceincrease on a 10,000x10,000 square matrix with a significantly lower memoryfootprint. Applications of efficient matrix multiplication include, amongothers, accelerating the training and implementation of deep convolutionalneural network based workloads, and thus such efficient algorithms can play aground-breaking role in faster, more efficient execution of even the mostcomplicated machine learning tasks.

JAMPI: efficient matrix multiplication in Spark using Barrier Execution Mode