Modeling and analyzing evaluation cost of CUDA kernels
Abstract
General-purpose programming on GPUs (GPGPU) is becoming increasingly in vogue as applications such as machine learning and scientific computing demand high throughput in vector-parallel applications. NVIDIA's CUDA toolkit seeks to make GPGPU programming accessible by allowing programmers to write GPU functions, called kernels, in a small extension of C/C++. However, due to CUDA's complex execution model, the performance characteristics of CUDA kernels are difficult to predict, especially for novice programmers.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 04, 2021
- Source ID
- 10.1145/3434306
Entities
People
- Jan Hoffmann
- Stefan K. Muller
Organizations
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Illinois Institute of Technology
- National Science Foundation