DMR API: Improving cluster productivity by turning applications into malleable

Adaptive workloads can change on-the-fly the configuration of their jobs, in terms of number of processes. To carry out these job reconfigurations, we have designed a methodology which enables a job to communicate with the resource manager and, through the runtime. to change its number of MPI ranks. The collaboration between both the workload manager-aware of the queue of jobs and the resources allocation-and the parallel runtime-able to transparently handle the processes and the program data-is crucial for our throughput-aware malleability methodology. Hence, when a job triggers a reconfiguration, the resource manager will check the cluster status and return the appropriate action: i) expand, if there are spare resources; ii) shrink, if queued jobs can be initiated; or iii) none, if no change can improve the global productivity. In this paper, we describe the internals of our framework and demonstrate how it reduces the global workload completion time along with providing a more efficient usage of the underlying resources. For this purpose, we present a thorough study of the adaptive workloads processing by showing the detailed behavior of our framework in representative experiments. (C) 2018 Elsevier B.V. All rights reserved.