Towards Enabling I/O Awareness in Task-based Programming Models

TL;DR

This paper introduces an I/O aware task-based programming model that creates more parallelism and reduces I/O congestion, significantly improving application performance on large-scale systems.

Contribution

It proposes a novel I/O aware programming model with explicit and automatic I/O task scheduling constraints to enhance total application performance.

Findings

Achieved up to 43% performance improvement on MareNostrum 4 supercomputer.

Demonstrated effectiveness of I/O-aware scheduling in mitigating I/O congestion.

Showed that I/O awareness enables better overlap of I/O and compute tasks.

Abstract

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. Our experiments on the MareNostrum 4 Supercomputer demonstrate that using I/O aware programming model can achieve up to 43% total performance improvement as compared to the I/O non-aware implementation.