Computing Optimal Cycle Mean in Parallel on CUDA

TL;DR

This paper presents a CUDA-based data-parallel algorithm for computing the optimal cycle mean in directed weighted graphs, significantly accelerating the process for applications like program analysis.

Contribution

It introduces a novel parallel algorithm decomposing the problem into CUDA-optimized primitives, enabling efficient GPU acceleration.

Findings

Achieved a fivefold speedup over sequential algorithms.

Demonstrated effectiveness on graphs modeling distributed systems.

Optimized graph primitives for CUDA implementation.

Abstract

Computation of optimal cycle mean in a directed weighted graph has many applications in program analysis, performance verification in particular. In this paper we propose a data-parallel algorithmic solution to the problem and show how the computation of optimal cycle mean can be efficiently accelerated by means of CUDA technology. We show how the problem of computation of optimal cycle mean is decomposed into a sequence of data-parallel graph computation primitives and show how these primitives can be implemented and optimized for CUDA computation. Finally, we report a fivefold experimental speed up on graphs representing models of distributed systems when compared to best sequential algorithms.