Phased Computation Graphs in the Polyhedral Model

We present a translation scheme that allows a broad class of dataflow graphs to be considered under the optimization framework of the polyhedral model. The input to our analysis is a Phased Computation Graph, which we define as a generalization of the most widely used dataflow representations, inclu...

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Main Authors: Thies, William, Lin, Jasper, Amarasinghe, Saman
Published: 2023
Online Access:https://hdl.handle.net/1721.1/149319
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author Thies, William
Lin, Jasper
Amarasinghe, Saman
author_facet Thies, William
Lin, Jasper
Amarasinghe, Saman
author_sort Thies, William
collection MIT
description We present a translation scheme that allows a broad class of dataflow graphs to be considered under the optimization framework of the polyhedral model. The input to our analysis is a Phased Computation Graph, which we define as a generalization of the most widely used dataflow representations, including synchronous dataflow, cyclo-static dataflow, and computation graphs. The output of our analysis is a System of Affine Recurrence Equations (SARE) that exactly captures the data dependencies between the nodes of the original graph. Using the SARE representation, one can apply many techniques from the scientific community that are new to the DSP domain. For example, we propose simple optimizations such as node splitting, decimation propagation, and stead-state invariant code motion that leverage the fine-grained dependence information of the SARE to perform novel transformations on a stream graph. We also propose ways in which the polyhedral model can offer new approaches to classic problems of the DSP community, such as minimizing buffer size, code size, and optimizing the schedule.
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spelling mit-1721.1/1493192023-03-30T03:04:49Z Phased Computation Graphs in the Polyhedral Model Thies, William Lin, Jasper Amarasinghe, Saman We present a translation scheme that allows a broad class of dataflow graphs to be considered under the optimization framework of the polyhedral model. The input to our analysis is a Phased Computation Graph, which we define as a generalization of the most widely used dataflow representations, including synchronous dataflow, cyclo-static dataflow, and computation graphs. The output of our analysis is a System of Affine Recurrence Equations (SARE) that exactly captures the data dependencies between the nodes of the original graph. Using the SARE representation, one can apply many techniques from the scientific community that are new to the DSP domain. For example, we propose simple optimizations such as node splitting, decimation propagation, and stead-state invariant code motion that leverage the fine-grained dependence information of the SARE to perform novel transformations on a stream graph. We also propose ways in which the polyhedral model can offer new approaches to classic problems of the DSP community, such as minimizing buffer size, code size, and optimizing the schedule. 2023-03-29T14:42:53Z 2023-03-29T14:42:53Z 2002-08 https://hdl.handle.net/1721.1/149319 MIT-LCS-TM-630 application/pdf
spellingShingle Thies, William
Lin, Jasper
Amarasinghe, Saman
Phased Computation Graphs in the Polyhedral Model
title Phased Computation Graphs in the Polyhedral Model
title_full Phased Computation Graphs in the Polyhedral Model
title_fullStr Phased Computation Graphs in the Polyhedral Model
title_full_unstemmed Phased Computation Graphs in the Polyhedral Model
title_short Phased Computation Graphs in the Polyhedral Model
title_sort phased computation graphs in the polyhedral model
url https://hdl.handle.net/1721.1/149319
work_keys_str_mv AT thieswilliam phasedcomputationgraphsinthepolyhedralmodel
AT linjasper phasedcomputationgraphsinthepolyhedralmodel
AT amarasinghesaman phasedcomputationgraphsinthepolyhedralmodel