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1
Introduction, personal background and research goals
2
How an APL approach to data can help with GPU/multi-core programming
3
Related on-going research in academia and industry
4
Abstract interpretation of computer programs to help find software bugs
5
An abstract academic concept of types
6
The language trilemma of performance, productivity and generality
7
Static semantics and rank polymorphism in array languages
8
Using shape analysis to build constraints which help computers see things how the APLer does
Description:
Explore the world of scheduling array operations in this 22-minute conference talk from Dyalog '22. Dive into Juuso Haavisto's research on static scheduling and its applications in optimizing execution across various hardware and compute infrastructure settings. Learn how type theory aids in parallelizing array operations for graphics processing units and distributed computing. Discover the APL approach to data and its benefits for GPU and multi-core programming. Examine ongoing research in academia and industry, abstract interpretation for software bug detection, and the concept of types in academic contexts. Understand the language trilemma of performance, productivity, and generality. Investigate static semantics and rank polymorphism in array languages, and explore how shape analysis can build constraints that align computer understanding with APL programmers' perspectives.

Scheduling Array Operations for GPU and Distributed Computing

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