Characterizing Data Analytics Workloads on Intel Xeon Phi

Abstract

With the growing computation demands of data analytics, heterogeneous architectures become popular for their support of high parallelism. Intel Xeon Phi, a many-core coprocessor originally designed for high performance computing applications, is promising for data analytics workloads. However, to the best of knowledge, there is no prior work systematically characterizing the performance of data analytics workloads on Xeon Phi. It is difficult to design a benchmark suite to represent the behavior of data analytics workloads on Xeon Phi. The main challenge resides in fully exploiting Xeon Phi's features, such as long SIMD instruction, simultaneous multithreading, and complex memory hierarchy. To address this issue, we develop Big Data Bench-Phi, which consists of seven representative data analytics workloads. All of these benchmarks are optimized for Xeon Phi and able to characterize Xeon Phi's support for data analytics workloads. Compared with a 24-core Xeon E5-2620 machine, Big Data Bench-Phi achieves reasonable speedups for most of its benchmarks, ranging from 1.5 to 23.4X. Our experiments show that workloads working on high-dimensional matrices can significantly benefit from instruction- and thread-level parallelism on Xeon Phi.