The use of hardware accelerators in high-performance computing has grown increasingly prevalent, particularly due to the growth of graphics processing units (GPUs) as generalpurpose (GPGPU) accelerators. Much of this growth has been driven by NVIDIA’s CUDA ecosystem for developing GPGPU applications on NVIDIA hardware. However, with the increasing diversity of GPUs (including those from AMD, AR...

This paper analyzes several aspects regarding the improvement of software performance for applications written in the Compute Unified Device Architecture (CUDA). We address an issue of great importance when programming a CUDA application: the Graphics Processing Unit’s (GPU’s) memory management through transpose kernels. We also benchmark and evaluate the performance for progressively optimizin...

Ultra high definition (UHD) game scenes have caused the memory bandwidth problem. The lossless DPCM-GR based compression algorithm [12] using NVIDIA CUDA(Compute Unified Device Architecture) like general purpose GPU (GPGPU) computing relieves the bandwidth problem without sacrificing image quality, which supports bit parallel pipelining. This paper increases the memory bandwidth efficiency usin...

We present a novel method of computing line-projection operations along sets of randomly oriented lines with CUDA and its application to positron emission tomography (PET) image reconstruction. The new approach addresses challenges that include compute thread divergence and random memory access by exploiting GPU capabilities such as shared memory and atomic operations. The benefits of the CUDA ...

OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC...

Solving problems in bioinformatics often needs extensive computational power. Current trends in processor architecture, especially massive multi-core processors for graphic cards, combine a large number of cores into a single chip to improve the overall performance. The Compute Unified Device Architecture (CUDA) provides programming interfaces to make full use of the computing power of graphics...