2017 IEEE International Conference on Cluster Computing (CLUSTER) (2017)
Honolulu, Hawaii, United States
Sept. 5, 2017 to Sept. 8, 2017
We evaluate the on-node interference caused when co-locating traditional high-performance computing applications with a big-data application. Using kernel benchmarks from the NPB suite and a state-of-art graph analytics code, we explore different process placements and effects they have on application performance. Our results show that the most memory intensive HPC application (MG) experienced the highest performance variation during co-location.
Benchmark testing, Big Data, Interference, Kernel, Resource management, Conferences, Standards
K. Brown and S. Matsuoka, "Co-locating Graph Analytics and HPC Applications," 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, Hawaii, United States, 2017, pp. 659-660.