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