A Multi-faceted Approach to Job Placement for Improved Performance on Extreme-Scale Systems

Job placement plays a pivotal role in application performance on supercomputers. We present a multi-faceted exploration to influence placement in extreme-scale systems, to improve network performance and decrease variability. In our first exploration, Scores, we developed a machine learning model that extracts features from a job's node-allocation and grades performance. This identified several important node-metrics that led to Dual-Ended scheduling, a means of reducing network contention without impacting utilization. In evaluations on the Titan supercomputer, we observed reductions in average hop-count by up to 50%. We also developed an improved node-layout strategy that targets a better balance between network latency and bandwidth, replacing the default ALPS layout on Titan that resulted in an average of 10% runtime improvement. Both of these efforts underscore the importance of a job placement strategy that is cognizant of workload mixture and network topology.