Year: 2024
Type: preprint
Abstract: Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes of program performance issues are critical tasks to make the most of hardware resources. We provide an in-depth overview of performance bottlenecks in recent OoO microarchitectures and describe the difficulties of detecting them. Techniques that measure resources utilization can offer a good understanding of a program's execution, but, due to the constraints inherent to Performance Monitoring Units (PMU) of CPUs, do not provide the relevant metrics for each use case. Another approach is to rely on a performance model to simulate the CPU behavior. Such a model makes it possible to implement any new microarchitecture-related metric. Within this framework, we advocate for implementing modeled resources as parameters that can be varied at will to reveal performance bottlenecks. This allows a generalization of bottleneck analysis that we call sensitivity analysis. We present Gus, a novel performance analysis tool that combines the advantages of sensitivity analysis and dynamic binary instrumentation within a resource-centric CPU model. We evaluate the impact of sensitivity on bottleneck analysis over a set of high-performance computing kernels.
Source: arXiv (Cornell University)
Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Centre Inria de l'Université Grenoble Alpes, Laboratoire d'Informatique de Grenoble, Université Grenoble Alpes, École Normale Supérieure de Rennes, Orange (France), Institut Mines-Télécom, Institut Polytechnique de Paris
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6948189190
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