Presentation
Learning Generalizable Program and Architecture Representations for Performance Modeling
SessionPerformance Modeling
DescriptionPerformance modeling is an essential tool in many areas, including performance characterization/optimization, design space exploration, and resource allocation problems, to
name a few. However, existing performance modeling approaches have limitations, such as high computational cost for discrete-event simulators, narrow flexibility of hardware emulators, or
restricted accuracy/generality of analytical/data-driven models.
To address these limitations, this paper proposes PerfVec, a novel deep-learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches.
name a few. However, existing performance modeling approaches have limitations, such as high computational cost for discrete-event simulators, narrow flexibility of hardware emulators, or
restricted accuracy/generality of analytical/data-driven models.
To address these limitations, this paper proposes PerfVec, a novel deep-learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches.
Event Type
Paper
TimeWednesday, 20 November 20244:30pm - 5pm EST
LocationB309
Heterogeneous Computing
Linear Algebra
Network
Parallel Programming Methods, Models, Languages and Environments
Performance Evaluation and/or Optimization Tools
TP
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