Cloud computing is considered a new way to use on-demand computing resources. When executing a workflow process in such an environment, task scheduling, a well-known NP-hard problem is a very important step. Many heuristic algorithms have been proposed to solve this problem. In this article, the authors present a GPU-based approach for solving the workflow scheduling problem. The main idea of the approach is to implement a massively parallel version of the simulated annealing algorithm, in an asynchronous way where no information is exchanged among parallel runs. The proposed approach, called PSA algorithm, is against another well-established scheduling HEFT heuristic. Experiments with randomly generated graphs show a much better performance from the proposed approach.
User's Guide for TOUGH2-MP - A Massively Parallel Version of the TOUGH2 Code
