Abstract — In this paper, I describe the methods I used for creating a Particle Swarm Optimizer (PSO) that runs under the Khronos Open Compute Language (OpenCL) on General Purpose Graphical Processing Units. The implementation is compared to similar work done in NVIDIA Common Unified Device

Branch divergence has a significant impact on the perfor-mance of GPU programs. We propose two novel software-based optimizations, called iteration delaying and branch distribution that aim to reduce branch divergence. Itera-tion delaying targets a divergent branch enclosed by a loop within a kernel. It improves performance by executing loop iterations that take the same branch direction and delay-ing those that take the other direction until later iterations. Branch distribution reduces the length of divergent code by factoring out structurally similar code from the branch paths. We conduct a preliminary evaluation of the two op-timizations using both synthetic benchmarks and a highly-optimized real-world application. Our evaluation shows that they improve the performance of the synthetic benchmarks by as much as 30 % and 80 % respectively, and that of the real-world application by 12 % and 16 % respectively.

Abstract. We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). For this purpose we exploit the fact that some of the basic algorithms for probabilistic model checking rely on matrix vector multiplication. Since this kind of linear

Graphics cards, traditionally designed as accelerators for computer graphics, have evolved to support more general-purpose computation. General Purpose Graphical Processing Units (GPGPUs) are now being used as highly efficient, cost-effective platforms for executing certain simulation applications

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statistical properties of the collective motion of fish whose dynamics are described by such a model, many realizations are typically required. This carries a very high computational cost. The current generation of graphics processing units is well-suited to this task. We describe our implementation

Abstract. We accelerate state space exploration for explicit-state model checking by executing complex operations on the graphics processing unit (GPU). In contrast to existing approaches enhancing model checking through performing parallel matrix operations on the GPU, we parallelize the breadth

conducted with the goal of integrating generated code into Fluidity, a general-purpose computational fluid dynamics code. We survey and evaluate CUDA implementations of finite element assembly, in particular examining the optimisations necessary for high performance, and examine the state of the art

Abstract. This work addresses the implementation of the direct version of the Boundary Element Method (BEM) for two-dimensional problems on general-purpose graphics hardware (GPGPU). Due to its architecture, the GPU is specially well-suited to address problems that can be expressed as data