The art of parallel programming
The art of parallel programming
Applied numerical linear algebra
Applied numerical linear algebra
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems
Proceedings of the 23rd international conference on Supercomputing
Parallel data-locality aware stencil computations on modern micro-architectures
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
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We are witnessing the consolidation of the GPUs streaming paradigm in parallel computing. This paper explores stencil operations in CUDA to optimize on GPUs the Jacobi method for solving Laplace's differential equation. The code keeps constant the access pattern through a large number of loop iterations, that way being representative of a wide set of iterative linear algebra algorithms. Optimizations are focused on data parallelism, threads deployment and the GPU memory hierarchy, whose management is explicit by the CUDA programmer. Experimental results are shown on Nvidia Teslas C870 and C1060 GPUs and compared to a counterpart version optimized on a quadcore Intel CPU. The speed-up factor for our set of GPU optimizations reaches 3-4x and the execution times defeat those of the CPU by a wide margin, also showing great scalability when moving towards a more sophisticated GPU architecture and/or more demanding problem sizes.