Presentation
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AI-Driven Performance Metaprogramming
DescriptionRecent advances in artificial intelligence methods show the enormous potential of AI methods. The underlying concepts are embedding spaces to represent real-world information. These embedding spaces have been used to represent, transform, and work with complex information in large-language models but also many other domains such as climate sciences or automated driving systems. In this talk, we focus on embedding spaces for programs and use those primarily to assess, analyze, and improve program performance. We start by deriving a first embedding from textual LLWM internal representation (IR) and show that it successfully predicts GPU execution times of programs. We then show that textual representations bear the danger is missing context and being overly sensitive to specific strings. Using a graph-based representation, we improve the embedding to capture relationships such as data dependencies and flows in LLVM IR. Finally, we discuss DaCe's performance metaprogramming capabilities and it's programmable graph-based IR. We then demonstrate how a graph-neural network (GNN)-based embedding can capture general performance properties. Those properties form the concept of Performance Embeddings for Transfer Tuning and can be used to select optimization metaprograms to apply to transform the IR graph.
Presenter
Event Type
Workshop
TimeMonday, 13 November 20239:10am - 10am MST
Location601
Artificial Intelligence/Machine Learning
Software Engineering
W