| Autor: |
KLASHTORNY, ARTEM, ZHUANHAO WU, KAUSHIK, ANIRUDH MOHAN, PATEL, HIREN |
| Předmět: |
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| Zdroj: |
ACM Transactions on Embedded Computing Systems; 2023 Suppl5s, Vol. 22, p1-25, 25p |
| Abstrakt: |
We present a predictable wavefront splitting (PWS) technique for graphics processing units (GPUs). PWS improves the performance of GPU applications by reducing the impact of branch divergence while ensuring that worst-case execution time (WCET) estimates can be computed. This makes PWS an appropriate technique to use in safety-critical applications, such as autonomous driving systems, avionics, and space, that require strict temporal guarantees. In developing PWS on an AMD-based GPU, we propose microarchitectural enhancements to the GPU, and a compiler pass that eliminates branch serializations to reduce the WCET of a wavefront. Our analysis of PWS exhibits a performance improvement of 11% over existing architectures with a lower WCET than prior works in wavefront splitting. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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