Implementation and evaluation of the HPC challenge benchmark in the XcalableMP PGAS language

Abstract

To improve productivity for developing parallel applications on high performance computing systems, the XcalableMP PGAS language has been proposed. XcalableMP supports both a typical parallelization under the ''global-view memory model'' which uses directives and a flexible parallelization under the ''local-view memory model'' which uses coarray features. The goal of the present paper is to clarify XcalableMP's productivity and performance. To do so, we implement and evaluate the high performance computing challenge benchmark, namely, EP STREAM Triad, High Performance Linpack, Global fast Fourier transform, and RandomAccess on the K computer using up to 16,384 compute nodes and a generic cluster system using up to 128 compute nodes. We found that we could more easily implement the benchmarks using XcalableMP rather than using MPI. Moreover, most of the performance results using XcalableMP were almost the same as those using MPI.

Implementation and Performance Evaluation of Omni Compiler

“…The K computer The COMA system Fig. 9 Performance results for STREAM [5] K computer, and 11.55 TB/s for 128 compute nodes on the COMA system. The values of the performance ratio are between 0.99 and 1.00 on both systems.…”

“…In order to evaluate the performance of XMP, we implemented the HPC Challenge (HPCC) benchmark (https://icl.utk.edu/hpcc/), namely, EP STREAM Triad (STREAM), High-Performance Linpack (HPL), Global fast Fourier transform (FFT), and RandomAccess [5]. While the HPCC benchmark is used to evaluate multiple attributes of HPC systems, the benchmark is also useful to evaluate the properties of a parallel language.…”

“…Fig. 7 Part of the STREAM code [5] calculates the performance on each node locally. In line 18, the reduction directive performs a reduction operation among nodes to calculate the total performance.…”

“…The K computer The COMA system Fig. 13 Performance results for HPL [5] cblas_dgemm(). Note that L_ld and A_ld remain unchanged from the beginning of the program, and so each xmp_array_lda() is called only once.…”

Multi-accelerator extension in OpenMP based on PGAS model

“…In this paper, we propose an extension that can handle PGAS for OpenMP to deal with multiple accelerators in ease. Some of the OpenMP extension refer to XcalableMP (XMP) [11,27] which is a directive-based PGAS language for distributed memory systems.…”