Efficient Matrix Computation for SGD-Based Algorithms on Apache Spark

Abstract

With the increasing of matrix size in large-scale data analysis, a series of Spark-based distributed matrix computation systems have emerged. Typically, these systems split a matrix into matrix blocks and save these matrix blocks into a RDD. To implement matrix operations, these systems manipulate the matrices by applying coarse-grained RDD operations. That is, these systems load the entire RDD to get a part of matrix blocks. Hence, it may cause the redundant IO when running SGD-based algorithms, since SGD only samples a min-batch data. Moreover, these systems typically employ a hash scheme to partition matrix blocks, which is oblivious to the sampling semantics. In this work, we propose a sampling-aware data loading which uses fine-grained RDD operation to reduce the partitions without sampled data, so as to decrease the redundant IO. Moreover, we exploit a semantic-based partition scheme, which gathers sampled blocks into the same partitions, to further reduce the number of accessed partitions. We modify SystemDS to implement Emacs, efficient matrix computation for SGD-based algorithms on Apache Spark. Our experimental results show that Emacs outperforms existing Spark-based matrix computation systems by 37%.