MapCG
2010pp. 217–226
Citations Over TimeTop 1% of 2010 papers
Abstract
Graphics Processing Units (GPU) have been playing an important role in the general purpose computing market recently. The common approach to program GPU today is to write GPU specific code with low level GPU APIs such as CUDA. Although this approach can achieve very good performance, it raises serious portability issues: programmers are required to write a specific version of code for each potential target architecture. It results in high development and maintenance cost.
Related Papers
- → HIPCL(2020)21 cited
- → On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation(2019)12 cited
- → Evaluating CUDA Portability with HIPCL and DPCT(2021)10 cited
- → Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs(2023)2 cited
- → Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs(2023)