Huffman Coding with Gap Arrays for GPU Acceleration

Yamamoto, Naoya; Nakano, Koji; Ito, Yasuaki; Takafuji, Daisuke; Kasagi, Akihiko; Tabaru, Tsuguchika

doi:10.1145/3404397.3404429

Cited by 14 publications

(27 citation statements)

References 21 publications

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“…To address the self-synchronization's performance issue, Yamamoto et al work proposed a new data structure called a gap array to eliminate this decoding bottleneck, in exchange for some extra encoding overhead [45]. Note that although the work also proposes an optimized encoding scheme, our main focus is its decoding scheme.…”

Section: Gap Arraysmentioning

confidence: 99%

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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Rivera¹,

Di²,

Tian³

et al. 2022

Preprint

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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-ofthe-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 3.64× over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43× on average.

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Section: Gap Arraysmentioning

confidence: 99%

“…The gap array is used in combination with a technique called Single Kernel Soft Synchronization (SKSS) to determine output indices, decode the codewords, and write output symbols to memory. We refer readers to [45] for more details.…”

Section: Gap Arraysmentioning

confidence: 99%

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Rivera¹,

Di²,

Tian³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The deflate compression can be processed in parallel and the research for accelerating compression is performed [ 20 , 21 , 22 , 23 ]. In the case of decompression, there are several challenges for accelerating owing to the serial nature of deflate format [ 24 ]. The deflate data are compressed with the LZ77 compression and Huffman encoding.…”

Section: Related Workmentioning

confidence: 99%

“…In order to address the limitation of decompression accelerating, the technique for parallel decoding has been researched [ 24 , 25 , 26 , 27 ]. Jang et al [ 25 ] proposed speculative parallelization for deflate decompression.…”

Section: Related Workmentioning

confidence: 99%

“…The authors utilized the self-synchronization property that the decoding process is synchronized after the block boundary. Yamamoto et al [ 24 ] proposed a gap-array data structure, which is an array of the gaps of segments in the Huffman encoding process. The gap array is attached to the Huffman encoded data for accelerating Huffman decoding.…”

Section: Related Workmentioning

confidence: 99%

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Lossless Decompression Accelerator for Embedded Processor with GUI

Hwang

Cho

et al. 2021

Micromachines

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The development of the mobile industry brings about the demand for high-performance embedded systems in order to meet the requirement of user-centered application. Because of the limitation of memory resource, employing compressed data is efficient for an embedded system. However, the workload for data decompression causes an extreme bottleneck to the embedded processor. One of the ways to alleviate the bottleneck is to integrate a hardware accelerator along with the processor, constructing a system-on-chip (SoC) for the embedded system. In this paper, we propose a lossless decompression accelerator for an embedded processor, which supports LZ77 decompression and static Huffman decoding for an inflate algorithm. The accelerator is implemented on a field programmable gate array (FPGA) to verify the functional suitability and fabricated in a Samsung 65 nm complementary metal oxide semiconductor (CMOS) process. The performance of the accelerator is evaluated by the Canterbury corpus benchmark and achieved throughput up to 20.7 MB/s at 50 MHz system clock frequency.

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GPU implementations of deflate encoding and decoding

Takafuji

Nakano

Ito

et al. 2022

Concurrency and Computation

Self Cite

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Summary Deflate coding is a very popular lossless data compression method used in zlib, gzip (GNU zip), and zip, which performs the LZSS compression algorithm with Huffman coding. Deflate encoding and decoding involve sequential operations and their parallel acceleration using a GPU is quite hard. The main purpose of this paper is to present GPU implementations for encoding and decoding of Deflate coding. For efficient GPU implementations of Deflate coding, we have used multiple small hash tables for finding matching subsequences in the dictionary by multiple threads in parallel and applied the Single Kernel Soft Synchronization (SKSS) technique to fully utilize GPU computing resources. We have also adopted Huffman coding with gap arrays to accelerate parallel Huffman decoding. We have evaluated the performance of our GPU implementations using an NVIDIA A100 GPU and compared them with parallel/sequential Deflate encoding and decoding on the Intel X86 multicore CPUs using multiple threads/a single thread. Our GPU implementation of Deflate decoding is 1.66x–8.33x faster than the multiple thread implementation and 4.13x–36.56x faster than the single thread implementation.

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Huffman Coding with Gap Arrays for GPU Acceleration

Cited by 14 publications

References 21 publications

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Lossless Decompression Accelerator for Embedded Processor with GUI

GPU implementations of deflate encoding and decoding

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