TITLE: LS-CAT: A Large-Scale CUDA AutoTuning Dataset 

ABSTRACT: The effectiveness of Machine Learning (ML) methods depend on access to large
suitable datasets. In this article, we present how we build the LS-CAT
(Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of
training NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused
on linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5
028 536 associated runtimes, with different combinations of kernels, block
sizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980
and Nvidia T4 systems. This information creates a foundation upon which
NLP-based models can find correlations between source-code features and optimal
choice of thread block sizes.
  There are several results that can be drawn out of our LS-CAT database. E.g.,
our experimental results show that an optimal choice in thread block size can
gain an average of 6% for the average case. We thus also analyze how much
performance increase can be achieved in general, finding that in 10% of the
cases more than 20% performance increase can be achieved by using the optimal
block. A description of current and future work is also included.
The effectiveness of Machine Learning (ML) methods depend on access to large
suitable datasets. In this article, we present how we build the LS-CAT
(Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of
training NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused
on linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5
028 536 associated runtimes, with different combinations of kernels, block
sizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980
and Nvidia T4 systems. This information creates a foundation upon which
NLP-based models can find correlations between source-code features and optimal
choice of thread block sizes.
  There are several results that can be drawn out of our LS-CAT database. E.g.,
our experimental results show that an optimal choice in thread block size can
gain an average of 6% for the average case. We thus also analyze how much
performance increase can be achieved in general, finding that in 10% of the
cases more than 20% performance increase can be achieved by using the optimal
block. A description of current and future work is also included.
LS-CAT: A Large-Scale CUDA AutoTuning Dataset
http://arxiv.org/abs/2103.14409v1