Polyhedral Compilation: Applications, Approximations and GPU-specific Optimizations

Patwardhan, Abhishek A and Upadrasta, Ramakrishna (2018) Polyhedral Compilation: Applications, Approximations and GPU-specific Optimizations. Masters thesis, Indian Institute of Technology Hyderabad.

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Polyhedral compilation has been successful in analyzing, optimizing, automatically parallelizing a�ne computations for modern heterogenous target architectures. Many of the tools have been developed to automate the process of program analysis and transformations for a�ne control parts of programs including widely used open-source and production compilers such as GCC, LLVM, IBM/XL. This thesis makes contribution to the polyhedral model in three orthogonal dimensions as follows: • Applications: Applies polyhedral loop transformations on Deep learning computation kernel to demonstrate the e�ectiveness of complex loop transformations on these kernels. • Approximations: Developes two efficient algorithms to over-approximate convex polyhedra into U-TVPI polyhedra having applications in polyhedral compilation as well as automated program verification. • GPU-Specific Optimizations: Builds end-to-end fully automatic compiler framework to generate cache optimized CUDA code begining from sequential C program by using polyhedral modelling techniques. x

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IITH Creators:
IITH CreatorsORCiD
Upadrasta, RamakrishnaUNSPECIFIED
Item Type: Thesis (Masters)
Uncontrolled Keywords: Automatic Parallelization, Polyhedral Model
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 02 Jul 2018 10:13
Last Modified: 01 May 2019 05:45
URI: http://raiith.iith.ac.in/id/eprint/4129
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