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Lecture from Professor Michelle Strout, University of Arizona on May 30

Date & Time 30 May 2017 (Tuesday) 13:00 - 14:30
Title The Sparse Polyhedral Framework: Composing compiler-generated inspector-executor code
Venue Green Computing Systems Research and Development Center, Waseda University(Map
Target participants All Waseda students, faculty members, and the general public
Participant fee None

Professor Michelle Strout

Michelle Strout
Department of Computer Science
University of Arizona
USA

"The Sparse Polyhedral Framework: Composing compiler-generated inspector-executor code"

Abstract:

Irregular applications such as big graph analysis, material simulations, molecular dynamics simulations, and finite element analysis have performance problems due to their use of sparse data structures. Inspector-executor strategies are used to improve sparse computation performance through parallelization and data locality optimizations. An inspector re-schedules and reorders data at runtime, and an executor is a transformed version of the original computation that uses the newly reorganized data structures and schedules. Inspector-executor transformations are commonly written in a domain-specific or even application-specific fashion. Significant progress has been made in incorporating such inspector-executor transformations into existing compiler transformation frameworks, thus enabling their use with compile-time only transformations. However, composing inspector-executor transformations with each other is done on a case-by-case basis so as to reduce inspector overhead.

The Sparse Polyhedral Framework (SPF) is a compiler loop and array transformation model that can generally compose inspector-executor transformations with each other. In this talk, I review the history and current state of the art for inspector-executor strategies, review how the SPF enables the composition of inspector-executor transformations, and present current research to better balance the generality-performance tradeoff that is being done with the Sparse Polyhedral Framework.

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