Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
Auto-generation and auto-tuning of 3D stencil codes on GPU clusters
Proceedings of the Tenth International Symposium on Code Generation and Optimization
clSpMV: A Cross-Platform OpenCL SpMV Framework on GPUs
Proceedings of the 26th ACM international conference on Supercomputing
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Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and select CUDA parameters for SpMV to obtain the optimal performance on specific GPUs. The framework is evaluated on two NVIDIA GPU platforms, GeForce 9500 GTX and GeForce GTX 295.