Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rapid Exploration of Optimization Strategies on Advanced Architectures
  using TestSNAP and LAMMPS   The exascale race is at an end with the announcement of the Aurora and
Frontier machines. This next generation of supercomputers utilize diverse
hardware architectures to achieve their compute performance, providing an added
onus on the performance portability of applications. An expanding fragmentation
of programming models would provide a compounding optimization challenge were
it not for the evolution of performance-portable frameworks, providing unified
models for mapping abstract hierarchies of parallelism to diverse
architectures. A solution to this challenge is the evolution of
performance-portable frameworks, providing unified models for mapping abstract
hierarchies of parallelism to diverse architectures. Kokkos is one such
performance portable programming model for C++ applications, providing back-end
implementations for each major HPC platform. Even with a performance portable
framework, restructuring algorithms to expose higher degrees of parallelism is
non-trivial. The Spectral Neighbor Analysis Potential (SNAP) is a
machine-learned inter-atomic potential utilized in cutting-edge molecular
dynamics simulations. Previous implementations of the SNAP calculation showed a
downward trend in their performance relative to peak on newer-generation CPUs
and low performance on GPUs. In this paper we describe the restructuring and
optimization of SNAP as implemented in the Kokkos CUDA backend of the LAMMPS
molecular dynamics package, benchmarked on NVIDIA GPUs. We identify novel
patterns of hierarchical parallelism, facilitating a minimization of memory
access overheads and pushing the implementation into a compute-saturated
regime. Our implementation via Kokkos enables recompile-and-run efficiency on
upcoming architectures. We find a $\sim$22x time-to-solution improvement
relative to an existing implementation as measured on an NVIDIA Tesla V100-16GB
for an important benchmark.

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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-ex