Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework
  for Scientific Data   Error-bounded lossy compression is a state-of-the-art data reduction
technique for HPC applications because it not only significantly reduces
storage overhead but also can retain high fidelity for postanalysis. Because
supercomputers and HPC applications are becoming heterogeneous using
accelerator-based architectures, in particular GPUs, several development teams
have recently released GPU versions of their lossy compressors. However,
existing state-of-the-art GPU-based lossy compressors suffer from either low
compression and decompression throughput or low compression quality. In this
paper, we present an optimized GPU version, cuSZ, for one of the best
error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the
first error-bounded lossy compressor on GPUs for scientific data. Our
contributions are fourfold. (1) We propose a dual-quantization scheme to
entirely remove the data dependency in the prediction step of SZ such that this
step can be performed very efficiently on GPUs. (2) We develop an efficient
customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ
using CUDA and optimize its performance by improving the utilization of GPU
memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application
datasets from the Scientific Data Reduction Benchmarks and compare it with
other state-of-the-art methods on both CPUs and GPUs. Experiments show that our
cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x,
respectively, over the production version running on single and multiple CPU
cores, respectively, while getting the same quality of reconstructed data. It
also improves the compression ratio by up to 3.48x on the tested data compared
with another state-of-the-art GPU supported lossy compressor.

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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: hep-lat