🤖 AI Summary
This work addresses the lack of automated, efficient methods for evaluating how closely existing benchmarks resemble real-world high-performance computing (HPC) applications in terms of hardware performance characteristics. The authors propose a novel performance similarity metric based on hardware usage patterns, introducing for the first time two distinct classes of computational kernels that exhibit similar performance behavior. They develop a scalable, automated evaluation framework that integrates performance feature analysis, kernel classification, and cross-platform (CPU/GPU) similarity assessment. The effectiveness and practicality of this approach are demonstrated by accurately matching computational kernels from the Kripke proxy application to those in the RAJA Performance Suite, thereby validating the method’s capability to identify functionally analogous kernels across diverse hardware architectures.
📝 Abstract
As high-performance computing (HPC) systems rapidly evolve, with increasing on-node parallelism and widespread use of accelerators, understanding how the code maps to hardware is essential for reaching optimal performance. Benchmarks are commonly used for early assessment of emerging architectures (as well as for informing the design of future hardware), but it is often unknown how well the benchmarks represent the performance characteristics of simulation codes. Existing methods for evaluating how well our benchmarks represent our HPC codes are manual, labor intensive, and challenging to scale to many benchmarks. In this paper, we propose performance similarity metrics based on how the code uses the compute hardware. We define and characterize two broad categories of kernels that exhibit similar performance characteristics. We evaluate the pairwise similarity metrics on kernels in the Kripke proxy application and the RAJA Performance Suite, using both a CPU-only system and a GPU-accelerated system. We validate that our similarity metrics correctly match a kernel in the Kripke proxy app to a kernel in the RAJA Performance Suite. Our proposed similarity metrics enable assessment of the similarity of computational kernels in our codes and the proxy applications we use to represent the codes.