Regression Testing of GPU/MIC Systems for HPCC

Multicore GPU, Intel MIC, and FPGA supplemental parallel processors have become widely implemented in High Performance Computing Clusters (HPCCs). In HPCCs, Computing nodes are assembled with these supplemental processors for specific research applications; images are applied to do the research. Since HPCC computing nodes require completely different design configuration from one day to the next, System Administrators are being challenged to verify that each of these computing images work correctly, in all needed applications. Due to the large cost in man-hours that are expended with manual testing of each computing node and the entire HPCC system for defects, there is a need for automated regression testing on parallel, distributed, and heterogeneous computing nodes. Existing approaches at automated regression testing deals only with simple homogeneous HPCC topologies. What is needed is a regression testing technique to include heterogeneous HPCC topologies that deal with computing nodes containing supplemental GPUs, Intel MIC cards, FPGAs, etc. This paper presents a case-study to perform regression testing using Equivalence Class Partitioning (ECP) and Boundary Value testing techniques. The method has been employed to test HPCCs configured of heterogeneous computing nodes. More specifically, the computing nodes configured for this experiment include NVidia GPU and Intel MIC Xeon Phi cards deployed in HPCC clusters.