Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMAT: An Input Adaptive Sparse Matrix-Vector Multiplication Auto-Tuner   Sparse matrix vector multiplication (SpMV) is an important kernel in
scientific and engineering applications. The previous optimizations are sparse
matrix format specific and expose the choice of the best format to application
programmers. In this work we develop an auto-tuning framework to bridge gap
between the specific optimized kernels and their general-purpose use. We
propose an SpMV auto-tuner (SMAT) that provides an unified interface based on
compressed sparse row (CSR) to programmers by implicitly choosing the best
format and the fastest implementation of any input sparse matrix in runtime.
SMAT leverage a data mining model, which is formulated based on a set of
performance parameters extracted from 2373 matrices in UF sparse matrix
collection, to fast search the best combination. The experiments show that SMAT
achieves the maximum performance of 75 GFLOP/s in single-precision and 33
GFLOP/s in double-precision on Intel, and 41 GFLOP/s in single-precision and 34
GFLOP/s in double-precision on AMD. Compared with the sparse functions in MKL
library, SMAT runs faster by more than 3 times.

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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
3
The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph