Name
Abstract | ||
|---|---|---|
BSTRACTWhen modern heterogeneous HPC systems perform numerical computations, floating-point exceptional quantities such as NaN and infinity in the GPU context, remain insufficiently handled. This is because commonly used GPUs and the CUDA language have no inherent exception detection capabilities. Existing compiler-based approaches for this problem are tied to a given compiler and cannot detect exceptions generated by binaries and precompiled libraries. This paper contributes BinFPE, a unique tool that addresses these challenges. BinFPE uses the NVBit dynamic binary instrumentation framework to check the machine registers after each calculation to recognize exceptions, and conveys this information to the CPU for final reporting. We demonstrate the effectiveness of BinFPE on 42 CUDA programs, reporting previously unreported exceptions. We also present the limitations of BinFPE and our perspective on building GPU tools via binary instrumentation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3520313.3534655 | Programming Language Design and Implementation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Laguna | 1 | 0 | 0.34 |
| Xinyi Li | 2 | 0 | 0.34 |
| Ganesh Gopalakrishnan | 3 | 1619 | 130.11 |