Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

TL;DR

Atos is a GPU dynamic scheduling framework that enhances concurrency and load balancing for irregular applications, achieving significant speedups over traditional BSP frameworks by allowing flexible task management and kernel strategies.

Contribution

The paper introduces Atos, a novel task-parallel GPU framework that improves irregular application performance through relaxed dependencies and flexible load balancing strategies.

Findings

Achieves up to 12.8x speedup over BSP implementations.

Provides flexible control over kernel strategy and task granularity.

Offers insights into optimal configuration selection for different applications.

Abstract

We present Atos, a task-parallel GPU dynamic scheduling framework that is especially suited to dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task-parallel formulations of applications with relaxed dependencies, achieving higher GPU utilization, which is particularly significant for problems with concurrency bottlenecks. Atos also offers implicit task-parallel load balancing in addition to data-parallel load balancing, providing users the flexibility to balance between them to achieve optimal performance. Finally, Atos allows users to adapt to different use cases by controlling the kernel strategy and task-parallel granularity. We demonstrate that each of these controls is important in practice. We evaluate and analyze the performance of Atos vs. BSP on three applications: breadth-first search, PageRank, and graph coloring. Atos implementations achieve geomean speedups of 3.44x, 2.1x, and 2.77x and peak speedups of 12.8x, 3.2x, and 9.08x across three case studies, compared to a state-of-the-art BSP GPU implementation. Beyond simply quantifying the speedup, we extensively analyze the reasons behind each speedup. This deeper understanding allows us to derive general guidelines for how to select the optimal Atos configuration for different applications. Finally, our analysis provides insights for future dynamic scheduling framework designs.