Kernel-Assisted Communication Engine for MPI on Emerging Manycore Processors

摘要

Manycore processors such as Intel Knights Landing (KNL), the second generation Xeon Phi many-core processor from Intel comes equipped with up to 288 threads and 16 gigabytes of high-bandwidth on-chip multi-channel DRAM (MCDRAM) that bear the potential to significantly improve the performance of both compute-bound and memory-bound applications. For this potential to be realized, it is imperative to exploit KNL's highly threaded environment and careful use of the limited MCDRAM resource. In this work, we focus on achieving effective utilization of KNL's resources through the design of a kernel-based communication engine that makes use of multiple kernel threads and generic work request abstraction scheme to accelerate MPI data movement operations. Being a kernel-based approach, our designs are application-pattern agnostic and aim to have minimal contention with the application's compute and memory requirements. We have compared our proposed designs with other prevalent schemes employed by modern MPI libraries. The experimental evaluation shows that the proposed designs provide up to 2.5X improvement at the microbenchmark-level and improve the total execution time of the MPI+OpenMP version of HPCG by up to 15% when compared with other approaches. Furthermore, using the CNTK Deep Learning framework, we demonstrate a significant improvement over existing approaches in the total training time (execution time) with the Multi-level perceptron (MLP) model and MNIST image recognition dataset.