Malleable APGAS Programs and Their Support in Batch Job Schedulers

Malleability—the ability for applications to dynamically adjust their resource allocations at runtime—presents great potential to enhance the efficiency and resource utilization of modern supercomputers. However, applications are rarely capable of growing and shrinking their number of nodes at runtime, and batch job schedulers provide only rudimentary support for these features. While numerous approaches have been proposed for enabling application malleability, these typically focus on iterative computations and require complex code modifications. This amplifies the challenges for programmers, who already wrestle with the complexity of traditional MPI inter-node programming. Asynchronous Many-Task (AMT) programming presents a promising alternative. Computations are split into many fine-grained tasks, which are processed by workers. This way, AMT enables transparent task relocation via the runtime system, thus offering great potential for efficient malleability. In this paper, we propose an extension to an existing AMT system, namely APGAS for Java, that provides easy-to-use malleability. More specifically, programmers enable application malleability with only minimal code additions, thanks to the simple abstractions we provide. Runtime adjustments, such as process initialization and termination, are automatically managed. We demonstrate the ease of integration between our extension and future batch job schedulers through the implementation of a simplistic malleable batch job scheduler. Additionally, we validate our extension through the adaption of a load balancing library handling multiple benchmarks. Finally, we show that even a simplistic scheduling strategy for malleable applications improves resource utilization, job throughput, and overall job response time.