Cuda św. Stanisława

Efficient CUDA

Recent work in deep learning has created a voracious demand for more compute cycles, but with the slowdown of Moore’s law, CPUs cannot keep up with the demand. Thus, attention has turned to massively-parallel hardware accelerators, ranging from new processing units designed specifically for deep learning to the repurposing of existing technologies like Fieldprogrammable gate arrays (FPGAs) and ...

Cuda Accelerated Ltl Model Checking Cuda Accelerated Ltl Model Checking *

Recent technological developments made available various many-core hardware platforms. For example, a SIMD-like hardware architecture became easily accessible for many users who have their computers equipped with modern NVIDIA GPU cards with CUDA technology. In this paper we redesign the maximal accepting predecessors algorithm [7] for LTL model checking in terms of matrix-vector product in ord...

Berkeley’s Dwarfs on CUDA

CUDA-acceleratedDelay Fault Simulation

Porting NAHUJ to CUDA

This white-paper reports on an enabling effort that involves porting a legacy 2D fluid dynamics Fortran code to NVIDIA GPUs. Given the complexity of both code and underlying (custom) numerical method, the natural choice was to use NVIDIA CUDA C to achieve the best possible performance. We achieved over 4.5x speed-up on a single K20 compared to the original code executed on a dual-socket E5-2687W.