Details

Autor(en) / Beteiligte

• Arafa, Yehia
• Badawy, Abdel-Hameed A.
• Chennupati, Gopinath
• Santhi, Nandakishore
• Eidenbenz, Stephan

Titel

PPT-GPU: Scalable GPU Performance Modeling

Ist Teil von

• IEEE computer architecture letters, 2019-01, Vol.18 (1), p.55-58

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Performance modeling is a challenging problem due to the complexities of hardware architectures. In this paper, we present PPT-GPU, a scalable and accurate simulation framework that enables GPU code developers and architects to predict the performance of applications in a fast, and accurate manner on different GPU architectures. PPT-GPU is part of the open source project, Performance Prediction Toolkit (PPT) developed at the Los Alamos National Laboratory. We extend the old GPU model in PPT that predict the runtimes of computational physics codes to offer better prediction accuracy, for which, we add models for different memory hierarchies found in GPUs and latencies for different instructions. To further show the utility of PPT-GPU, we compare our model against real GPU device(s) and the widely used cycle-accurate simulator, GPGPU-Sim using different workloads from RODINIA and Parboil benchmarks. The results indicate that the predicted performance of PPT-GPU is within a 10 percent error compared to the real device(s). In addition, PPT-GPU is highly scalable, where it is up to 450x faster than GPGPU-Sim with more accurate results.