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The inspector/executor paradigm permits using runtime information in concert with compiler optimization. An inspector collects information that is only available at runtime; this information is used by an optimized executor that was created at compile time. Inspectors are widely used in optimizing irregular computations, where information about data dependences, loop bounds, data structures, and memory access pa erns are collected at runtime and used to guide code transformation, parallelization, and data layout. Most research that uses inspectors relies on instantiating inspector templates, invoking inspector library code, or manually writing inspectors. is paper describes abstractions for generating inspectors for loop and data transformations for sparse matrix computations using the Sparse Polyhedral Framework (SPF). SPF is an extension of the polyhedral framework for transformation and code generation. SPF extends the polyhedral framework to represent runtime information with uninterpreted functions and inspector computations that explicitly realize such functions at runtime. It has previously been used to derive inspectors for data and iteration space reordering. is paper introduces data transformations into SPF, such as conversions between sparse matrix formats, and show how prior work can be supported by SPF. We also discuss possible extensions to support inspector composition and incorporate other optimizations. is work represents a step towards creating composable inspectors in keeping with the composability of a ne transformations on the executors.
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Shapes and flattening
Nesl is a first-order functional language with an apply-to-each construct and other parallel primitives that enables the expression of irregular nested data-parallel (NDP) algorithms. To compile Nesl, Blelloch and others developed a global flattening transformation that maps irregular NDP code into regular flat data parallel (FDP) code suitable for executing on SIMD or SIMT architectures, such as GPUs.While flattening solves the problem of mapping irregular parallelism into a regular model, it requires significant additional optimizations to produce performant code. Nessie is a compiler for Nesl that generates CUDA code for running on Nvidia GPUs. The Nessie compiler relies on a fairly complicated shape analysis that is performed on the FDP code produced by the flattening transformation. Shape analysis plays a key role in the compiler as it is the enabler of fusion optimizations, smart kernel scheduling, and other optimizations.In this paper, we present a new approach to the shape analysis problem for Nesl that is both simpler to implement and provides better quality shape information. The key idea is to analyze the NDP representation of the program and then preserve shape information through the flattening transformation.
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- Award ID(s):
- 1718540
- PAR ID:
- 10309644
- Date Published:
- Journal Name:
- IFL '19: Proceedings of the 31st Symposium on Implementation and Application of Functional Languages
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3412932.3412946
