GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Swarm's flight: accelerating the particles using C-CUDA
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
Accelerating Particle Swarm Algorithm with GPGPU
PDP '11 Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing
GPU-based asynchronous particle swarm optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
Information Sciences: an International Journal
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GPU-based parallel implementations of algorithms are usually compared against the corresponding sequential versions compiled for a single-core CPU machine, without taking advantage of the multi-core and SIMD capabilities of modern processors. This leads to unfair comparisons, where speed-up figures are much larger than what could actually be obtained if the CPU-based version were properly parallelized and optimized. The availability of OpenCL, which compiles parallel code for both GPUs and multi-core CPUs, has made it much easier to compare execution speed of different architectures fully exploiting each architecture's best features. We tested our latest parallel implementations of Particle Swarm Optimization (PSO), compiled under OpenCL for both GPUs and multi-core CPUs, and separately optimized for the two hardware architectures. Our results show that, for PSO, a GPU-based parallelization is still generally more efficient than a multi-core CPU-based one. However, the speed-up obtained by the GPU-based with respect to the CPU-based version is by far lower than the orders-of-magnitude figures reported by the papers which compare GPU-based parallel implementations to basic single-thread CPU code.