Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Input-Aware Auto-Tuning of Compute-Bound HPC Kernels   Efficient implementations of HPC applications for parallel architectures
generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While
these libraries provide highly optimized routines for certain characteristics
of inputs (e.g., square matrices), they generally do not retain optimal
performance across the wide range of problems encountered in practice. In this
paper, we present an input-aware auto-tuning framework for matrix
multiplications and convolutions, ISAAC, which uses predictive modeling
techniques to drive highly parameterized PTX code templates towards not only
hardware-, but also application-specific kernels. Numerical experiments on the
NVIDIA Maxwell and Pascal architectures show up to 3x performance gains over
both cuBLAS and cuDNN after only a few hours of auto-tuning.

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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: hep-ex