On the Load Balancing Techniques for GPU Applications Based on Prefix-scan

Abstract

Prefix-scan is one of the most common operation and building block for a wide range of parallel applications for GPUs. It allows the GPU threads to efficiently find and access in parallel to the assigned data. Nevertheless, the workload decomposition and mapping strategies that make use of prefix-scan can have a significant impact on the overall application performance. This paper presents a classification of the mapping strategies at the state of the art and their comparison to understand in which problem they best apply. Then, it presents Multi-Phase Search, an advanced dynamic technique that addresses the workload unbalancing problem by fully exploiting the GPU device characteristics. In particular, the proposed technique implements a dynamic mapping of work-units to threads through an algorithm whose complexity is sensibly reduced with respect to the other dynamic approaches in the literature. The paper shows, compares, and analyses the experimental results obtained by applying all the mapping techniques to different datasets, each one having very different characteristics and structure