A Unified Optimizing Compiler Framework for Different GPGPU Architectures

作者：Yang Yi^*; Xiang Ping; Kong Jingfei; Mantor Mike; Zhou Huiyang

来源：ACM Transactions on Architecture and Code Optimization, 2012, 9(2): 9.

DOI：10.1145/2207222.2207225

• Performance
• Experimentation
• Languages
• GPGPU
• OpenCL
• CUDA
• CUBLAS
• GPU Computing

This article 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.

• 出版日期2012-6