| Autor: |
Sarda, Giuseppe M., Shah, Nimish, Bhattacharjee, Debjyoti, Debacker, Peter, Verhelst, Marian |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
2023 IEEE International Symposium on Workload Characterization (IISWC) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/IISWC59245.2023.00017 |
| Popis: |
GPGPU execution analysis has always been tied to closed-source, proprietary benchmarking tools that provide high-level, non-exhaustive, and/or statistical information, preventing a thorough understanding of bottlenecks and optimization possibilities. Open-source hardware platforms offer opportunities to overcome such limits and co-optimize the full {hardware-mapping-algorithm} compute stack. Yet, so far, this has remained under-explored. In this work, we exploit micro-architecture parameter analysis to develop a hardware-aware, runtime mapping technique for OpenCL kernels on the open Vortex RISC-V GPGPU. Our method is based on trace observations and targets optimal hardware resource utilization to achieve superior performance and flexibility compared to hardware-agnostic mapping approaches. The technique was validated on different architectural GPU configurations across several OpenCL kernels. Overall, our approach significantly enhances the performance of the open-source Vortex GPGPU, contributing to unlocking its potential and usability. |
| Databáze: |
arXiv |
| Externí odkaz: |
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