Towards enabling I/O awareness in task-based programming models - Elshazly, Hatem; Ejarque, Jorge; Lordan, Francesc; Badia, Rosa M. (Future generation computer systems, 2021)
Storage systems have not kept the same technology improvement rate as computing systems. As applications produce more and more data, I/O becomes the limiting factor for increasing application performance. I/O congestion caused by concurrent access to storage devices is one of the main obstacles that cause I/O performance degradation and, consequently, total performance degradation. Although task-based programming models made it possible to achieve higher levels of parallelism by enabling the execution of tasks in large-scale distributed platforms, this parallelism only benefited the compute workload of the application. Previous efforts addressing I/O performance bottlenecks either focused on optimizing fine-grained I/O access patterns using I/O libraries or avoiding system-wide I/O congestion by minimizing interference between multiple applications. In this paper, we propose enabling I/O Awareness in task-based programming models for improving the total performance of applications. An I/O aware programming model is able to create more parallelism and mitigate the causes of I/O performance degradation. On the one hand, more parallelism can be created by supporting special tasks for executing I/O workloads, called I/O tasks, that can overlap with the execution of compute tasks. On the other hand, I/O congestion can be mitigated by constraining I/O tasks scheduling. We propose two approaches for specifying such constraints: explicitly set by the users or automatically inferred and tuned during application’s execution to optimize the execution of variable I/O workloads on a certain storage infrastructure. We implement our proposal using PyCOMPSs: a Task-based programming model for parallelizing Python applications. Our experiments on the MareNostrum 4 Supercomputer demonstrate that using I/O aware PyCOMPSs can achieve significant performance improvement in the total execution time of applications with different I/O workloads. This performance improvement can reach up to 43% of total application performance as compared to the I/O non-aware version of PyCOMPSs. •Programming model and runtime support to overlap I/O and compute workloads execution.•Programming model support and scheduling techniques to mitigate I/O congestion.•Mechanisms to automatically infer and tune I/O task bandwidth constraints at runtime.•Prototype implementation of I/O Awareness on an actual task-based programming model.•Evaluation of I/O awareness performance benefits on the MareNostrum4 supercomputer.