Comparing Cuda and Opencl: Unleashing Gpu Computational Power in Contemporary High-Performance Computing

Abstract

The contemporary landscape of high-performance computing has been irrevocably altered by the advent of parallel programming frameworks, CUDA and OpenCL. These frameworks, CUDA being NVIDIA's proprietary creation and OpenCL a platform-agnostic standard, have unlocked the immense computational potential latent within Graphics Processing Units (GPUs). In this article, we evaluate CUDA and OpenCL with the identical complicated kernels. We demonstrate that utilizing the NVIDIA compiler tools, very minor adjustments are required to successfully convert a CUDA kernel to an OpenCL kernel. Before this kernel can be created using ATI's tools, more tweaks are required. Our benchmarks compare CUDA with OpenCL in terms of data transfer rates to and from the GPU, the time it takes for the kernel to execute, and the total amount of time it takes for the application to run.