Increasing the Degree of Parallelism Using Speculative Execution in Task-based Runtime Systems

TL;DR

This paper introduces a speculative execution approach in task-based runtime systems to increase parallelism, demonstrated through a new C++ runtime called SPETABARU applied to Monte Carlo simulations.

Contribution

It proposes a novel methodology for speculative execution in task graphs and implements it in a new runtime system to improve parallelism in scientific applications.

Findings

Enhanced parallelism in Monte Carlo simulations

Effective speculative execution reduces idle time

SPETABARU outperforms traditional runtimes in scalability

Abstract

Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and the assignment of the work to the computational units. However, some applications have a limited degree of parallelism such that no matter how efficient the RS implementation, they may not scale on modern multicore CPUs. In this paper, we propose using speculation to unleash the parallelism when it is uncertain if some tasks will modify data, and we formalize a new methodology to enable speculative execution in a graph of tasks. This description is partially implemented in our new C++ RS called SPETABARU, which is capable of executing tasks in advance if some others are not certain to modify the data. We study the behavior of our approach to compute Monte Carlo and replica exchange Monte Carlo simulations.