Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Program optimization space pruning for a multithreaded gpu
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
CUDA-Lite: Reducing GPU Programming Complexity
Languages and Compilers for Parallel Computing
OpenMP to GPGPU: a compiler framework for automatic translation and optimization
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
A cross-input adaptive framework for GPU program optimizations
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
An adaptive performance modeling tool for GPU architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
A GPGPU compiler for memory optimization and parallelism management
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Many-Thread Aware Prefetching Mechanisms for GPGPU Applications
MICRO '43 Proceedings of the 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture
A unified optimizing compiler framework for different GPGPU architectures
ACM Transactions on Architecture and Code Optimization (TACO)
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In this paper we present our experience in developing an optimizing compiler for general purpose computation on graphics processing units (GPGPU) based on the Cetus compiler framework. The input to our compiler is a naïve GPU kernel procedure, which is functionally correct but without any consideration for performance optimization. Our compiler applies a set of optimization techniques to the naive kernel and generates the optimized GPU kernel. Our compiler supports optimizations for GPU kernels using either global memory or texture memory. The implementation of our compiler is facilitated with a source-to-source compiler infrastructure, Cetus. The code transformation in the Cetus compiler framework is called a pass. We classify all the passes used in our work into two categories: functional passes and optimization passes. The functional passes translate input kernels into desired intermediate representation, which clearly represents memory access patterns and thread configurations. A series of optimization passes improve the performance of the kernels by adapting them to the target GPGPU architecture. Our experiments show that the optimized code achieves very high performance, either superior or very close to highly fine-tuned libraries.