Faster makespan estimation for GPU threads on a single streaming multiprocessor

Graphics Processing Units (GPUs) are widely used to reduce the load on CPUs and liberate other resources of a given computer system. The recent trend of utilizing GPUs in embedded systems necessitates the development of timing analysis techniques for finding the joint worst-case execution time for a group of GPU threads of the same parallel application, on a streaming multiprocessor. The state-of-the-art approaches for computing the exact maximum makespan of GPU threads running on a single streaming multiprocessor are computationally expensive and even pessimistic approximations usually take a long time to complete. We therefore develop a technique for finding an estimate of the maximum makespan using metaheuristics. Its simplicity, flexibility and ability for massive parallelization, determine a potential of usage for soft real-time systems.