Automatic block dimensioning on GPU-accelerated programs through particle swarm optimization

Optimization of heterogeneous systems with AI planning heuristics and machine learning: a performance and energy aware approach

“…Huang [17] no yes no yes yes no Haidar [15] no yes no no yes yes Kasichayanula [19] no yes no yes yes no Pereira [30] no yes yes no no yes Hong [16] no yes no no yes yes Cerotti [6] yes yes no no no yes Benkner [4] yes yes no yes no yes Ge [12] yes yes no no yes yes Ravi [35] yes yes no no no yes Grewe [13] yes yes no yes no yes This paper yes yes yes yes yes yes…”

“…They use small subset of real-time statistics, and are able to infer about the activity of other micro-components using a linear-regression based prediction model. Pereira et al [30] propose an approach for determining optimal CUDA block dimension for execution of a wind field calculation program on GPU. Authors use particle swarm optimization (PSO) heuristic to find the CUDA block dimension that results with minimal program execution time.…”

“…Pereira et al [17] propose an approach for determining optimal CUDA block dimension for execution of a wind field calculation program on GPU. Authors use particle swarm optimization (PSO) heuristic to find the CUDA block dimension that results with minimal program execution time.…”

“…In [26] authors provide a comprehensive review of literature that describes various approaches for using heuristics or machine learning for optimization of parallel computing systems from single-node systems [13,19] to large scale Grid and Cloud computing infrastructures [10,14,31,38,11,32]. The related work usually uses for system optimization either heuristics [30] or machine learning [13] and there is insufficient research in combining these two methods in particular for optimization of heterogeneous computing systems. Preliminary results of our optimization approach are discussed in [25].…”