Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Engineering for Real and Complex Tall & Skinny Matrix
  Multiplication Kernels on GPUs   General matrix-matrix multiplications with double-precision real and complex
entries (DGEMM and ZGEMM) in vendor-supplied BLAS libraries are best optimized
for square matrices but often show bad performance for tall & skinny matrices,
which are much taller than wide. NVIDIA's current CUBLAS implementation
delivers only a fraction of the potential performance as indicated by the
roofline model in this case. We describe the challenges and key characteristics
of an implementation that can achieve close to optimal performance. We further
evaluate different strategies of parallelization and thread distribution, and
devise a flexible, configurable mapping scheme. To ensure flexibility and allow
for highly tailored implementations we use code generation combined with
autotuning. For a large range of matrix sizes in the domain of interest we
achieve at least 2/3 of the roofline performance and often substantially
outperform state-of-the art CUBLAS results on an NVIDIA Volta GPGPU.

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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex