A Unified Optimizing Compiler Framework for Different GPGPU Architectures

Yi Yang, Ping Xiang, Jingfei Kong, Mike Mantor, Huiyang Zhou
North Carolina State University
ACM Transactions on Architecture and Code Optimization (TACO), 2012

   title={A unified optimizing compiler framework for different GPGPU architectures},

   author={Yang, Y. and Xiang, P. and Kong, J. and Mantor, M. and Zhou, H.},

   journal={ACM Transactions on Architecture and Code Optimization (TACO)},







This paper presents a novel optimizing compiler for general purpose computation on graphics processing units (GPGPU). It addresses two major challenges of developing high performance GPGPU programs: effective utilization of GPU memory hierarchy and judicious management of parallelism. The input to our compiler is a naive GPU kernel function, which is functionally correct but without any consideration for performance optimization. The compiler generates two kernels, one optimized for global memories and the other for texture memories. The proposed compilation process is effective for both AMD/ATI and NVIDIA GPUs. The experiments show that our optimized code achieves very high performance, either superior or very close to highly fine-tuned libraries.
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