Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A fast vectorized sorting implementation based on the ARM scalable
  vector extension (SVE)   The way developers implement their algorithms and how these implementations
behave on modern CPUs are governed by the design and organization of these. The
vectorization units (SIMD) are among the few CPUs' parts that can and must be
explicitly controlled. In the HPC community, the x86 CPUs and their
vectorization instruction sets were de-facto the standard for decades. Each new
release of an instruction set was usually a doubling of the vector length
coupled with new operations. Each generation was pushing for adapting and
improving previous implementations. The release of the ARM scalable vector
extension (SVE) changed things radically for several reasons. First, we expect
ARM processors to equip many supercomputers in the next years. Second, SVE's
interface is different in several aspects from the x86 extensions as it
provides different instructions, uses a predicate to control most operations,
and has a vector size that is only known at execution time. Therefore, using
SVE opens new challenges on how to adapt algorithms including the ones that are
already well-optimized on x86. In this paper, we port a hybrid sort based on
the well-known Quicksort and Bitonic-sort algorithms. We use a Bitonic sort to
process small partitions/arrays and a vectorized partitioning implementation to
divide the partitions. We explain how we use the predicates and how we manage
the non-static vector size. We explain how we efficiently implement the sorting
kernels. Our approach only needs an array of O(log N) for the recursive calls
in the partitioning phase, both in the sequential and in the parallel case. We
test the performance of our approach on a modern ARMv8.2 and assess the
different layers of our implementation by sorting/partitioning integers, double
floating-point numbers, and key/value pairs of integers. Our approach is faster
than the GNU C++ sort algorithm by a speedup factor of 4 on average.

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The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an