کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
524341 868615 2012 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic tuning of the sparse matrix vector product on GPUs based on the ELLR-T approach
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Automatic tuning of the sparse matrix vector product on GPUs based on the ELLR-T approach
چکیده انگلیسی

A wide range of applications in engineering and scientific computing are involved in the acceleration of the sparse matrix vector product (SpMV). Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for GPUs have already appeared on the scene. This work is focused on the ELLR-T algorithm to compute SpMV on GPU architecture, its performance is strongly dependent on the optimum selection of two parameters. Therefore, taking account that the memory operations dominate the performance of ELLR-T, an analytical model is proposed in order to obtain the auto-tuning of ELLR-T for particular combinations of sparse matrix and GPU architecture. The evaluation results with a representative set of test matrices show that the average performance achieved by auto-tuned ELLR-T by means of the proposed model is near to the optimum. A comparative analysis of ELLR-T against a variety of previous proposals shows that ELLR-T with the estimated configuration reaches the best performance on GPU architecture for the representative set of test matrices.


► ELLR-T algorithm is presented to accelerate sparse matrix vector on GPUs.
► Optimum ELLR-T configuration outperforms other proposals.
► Accurate model to determine the optimum ELLR-T configuration is analyzed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Parallel Computing - Volume 38, Issue 8, August 2012, Pages 408–420
نویسندگان
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