Data-driven Mixed Precision Sparse Matrix Vector Multiplication for GPUs
نویسندگان
چکیده
منابع مشابه
Optimizing Sparse Matrix-Vector Multiplication on GPUs
We are witnessing the emergence of Graphics Processor units (GPUs) as powerful massively parallel systems. Furthermore, the introduction of new APIs for general-purpose computations on GPUs, namely CUDA from NVIDIA, Stream SDK from AMD, and OpenCL, makes GPUs an attractive choice for high-performance numerical and scientific computing. Sparse Matrix-Vector multiplication (SpMV) is one of the mo...
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Sparse matrix vector multiplication (SpMxV) is often one of the core components of many scientific applications. Many authors have proposed methods for its data distribution in distributed memory multiprocessors. We can classify these into four groups: Scatter, D-Way Strip, Recursive and Miscellaneous. In this work we propose a new method (Multiple Recursive Decomposition (MRD)), which partitio...
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ژورنال
عنوان ژورنال: ACM Transactions on Architecture and Code Optimization
سال: 2020
ISSN: 1544-3566,1544-3973
DOI: 10.1145/3371275