Abstract
Performance modeling of GPU kernels is a significant challenge. In this paper, we develop a novel approach to performance modeling for GPUs through abstract kernel emulation along with latency/gap modeling of resources. Experimental results on all benchmarks from the Rodinia suite demonstrate good accuracy in predicting execution time on multiple GPU platforms.
- S. S. Baghsorkhi, M. Delahaye, S. J. Patel, W. D. Gropp, and W.-m. W. Hwu. An adaptive performance modeling tool for gpu architectures. In PPOPP '10. Google Scholar
Digital Library
- M.-M. Papadopoulou, M. Sadooghi-Alvandi, and H. Wong. Micro-benchmarking the gt200 gpu. Computer Group, ECE, University of Toronto, Tech. Rep, 2009.Google Scholar
- Rodinia. Accelerating compute-intensive applications with accelerators, 2009. URL https://www.cs.virginia.edu/skadron/wiki/rodinia.Google Scholar
- T. G. Rogers, M. O'Connor, and T. M. Aamodt. Cache-conscious wavefront scheduling. In MICRO '12. Google Scholar
Digital Library
- J. Sim, A. Dasgupta, H. Kim, and R. Vuduc. A performance analysis framework for identifying potential benefits in gpgpu applications. In PPOPP '12. Google Scholar
Digital Library
Index Terms
Performance modeling for GPUs using abstract kernel emulation
Recommendations
Performance modeling for GPUs using abstract kernel emulation
PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel ProgrammingPerformance modeling of GPU kernels is a significant challenge. In this paper, we develop a novel approach to performance modeling for GPUs through abstract kernel emulation along with latency/gap modeling of resources. Experimental results on all ...
A Performance Model for GPUs with Caches
To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, ...
Efficient Convex Optimization on GPUs for Embedded Model Predictive Control
GPGPU-10: Proceedings of the General Purpose GPUsGPU applications have traditionally run on PCs or in larger scale systems. With the introduction of the Tegra line of mobile processors, NVIDIA expanded the types of systems that can exploit the massive parallelism offered by GPU computing ...







Comments