Using Machine Affinity to Increase Science Throughput (Machine Affinity Characterization of the HPCMP Workload)

摘要

Machine affinity is the observed phenomena that some applications benefit more than others from features of high performance computing (HPC) architectures. When considering a diverse portfolio of HPC machines manufactured by different vendors and of different ages, such as the set of all supercomputers currently operated by the Department of Defense High Performance Computing Modernization Program, it should be obvious that some run a given application faster than others do. Therefore, almost every user would request to run on the fastest machines. But an important insight is that some applications benefit more from the features of the faster machines than others do. If allocations are done in such a way that applications that benefit the most from the features of the fastest machines are assigned to those machines then overall throughput across all machines is boosted by more than 10%. We exhibit exemplary empirical analysis and provide a simple algorithm for doing allocations based on machine affinity. The net effect is like adding a new $10M supercomputer to the portfolio without paying for it.