- By:
- Jin, Zheming; Bernholdt, David E; Wang, Feiyi ; Yin, Junqi
- Page Number:
- 149-156
- Book Title:
- 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Publication Date:
- August 25, 2026
- Conference Name:
- 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Conference Location:
- New Orleans, Louisiana, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/IPDPSW71298.2026.00025
Abstract
AI applications have been steadily increasing in the allocation portfolio among leadership computing facilities. These applications depend on deep learning frameworks with hardware acceleration and underlying software systems. With the rapid development of applications, software stacks, and hardware devices, it is essential to evaluate the performance of core operations in AI workloads for direction of optimizations and procurement of next-generation high-performance computing (HPC) infrastructures. Currently, most benchmarks lack scientific AI workloads. So, we present DeepKernelBench and the experimental results of evaluating the benchmark suite for early observations and performance comparisons on datacenter GPUs using representative workloads for scientific AI, including Attentions, General matrix multiplications, Geometrics and Fourier neural operations.
