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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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs
More and more HPC applications require fast and effective compression
techniques to handle large volumes of data in storage and transmission. Not
only do these applications need to compress the data effectively during
simulation, but they also need to perform decompression efficiently for post
hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and
cuSZ is a version of SZ designed to take advantage of the GPU's power. At
present, cuSZ's compression performance has been optimized significantly while
its decompression still suffers considerably lower performance because of its
sophisticated lossless compression step---a customized Huffman decoding. In
this work, we aim to significantly improve the Huffman decoding performance for
cuSZ, thus improving the overall decompression performance in turn. To this
end, we first investigate two state-of-the-art GPU Huffman decoders in depth.
Then, we propose a deep architectural optimization for both algorithms.
Specifically, we take full advantage of CUDA GPU architectures by using shared
memory on decoding/writing phases, online tuning the amount of shared memory to
use, improving memory access patterns, and reducing warp divergence. Finally,
we evaluate our optimized decoders on an Nvidia V100 GPU using eight
representative scientific datasets. Our new decoding solution obtains an
average speedup of 3.64X over cuSZ's Huffman decoder and improves its overall
decompression performance by 2.43X on average.
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- NSF-PAR ID:
- 10313936
- Date Published:
- Journal Name:
- The 36th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS53621.2022.00075
