Practical speculative parallelization of variable-length decompression algorithms

Jang, Hakbeom; Kim, Channoh; Lee, Jae Woo

doi:10.1145/2465554.2465557

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…The study in [12] discusses some of the prior work in the context of databases. To address block boundary problems, [19] explores the blocklevel parallelism by performing pattern matching on the delimiters to predict the block boundaries. However, this technique cannot be applied to Snappy decompression since Snappy does not use any delimiters but uses a fixed uncompressed block size (64KB) as the boundary.…”

Section: Related Workmentioning

confidence: 99%

An Efficient High-Throughput LZ77-Based Decompressor in Reconfigurable Logic

Fang

Chen

Lee

et al. 2020

J Sign Process Syst

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To best leverage high-bandwidth storage and network technologies requires an improvement in the speed at which we can decompress data. We present a "refine and recycle" method applicable to LZ77-type decompressors that enables efficient high-bandwidth designs and present an implementation in reconfigurable logic. The method refines the write commands (for literal tokens) and read commands (for copy tokens) to a set of commands that target a single bank of block ram, and rather than performing all the dependency calculations saves logic by recycling (read) commands that return with an invalid result. A single "Snappy" decompressor implemented in reconfigurable logic leveraging this method is capable of processing multiple literal or copy tokens per cycle and achieves up to 7.2GB/s, which can keep pace with an NVMe device. The proposed method is about an order of magnitude faster and an order of magnitude more power efficient than a state-of-the-art single-core software implementation. The logic and block ram resources required by the decompressor are sufficiently low so that a set of these decompressors can be implemented on a single FPGA of reasonable size to keep up with the bandwidth provided by the most recent interface technologies.

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Section: Related Workmentioning

confidence: 99%

An Efficient High-Throughput LZ77-Based Decompressor in Reconfigurable Logic

Fang

Chen

Lee

et al. 2020

J Sign Process Syst

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“…In addition, the implementation of the LZ77-based decompressor in FPGAs takes much more memory resources [141], especially the BRAMs, limiting the number of engines we can place in a single FPGA. Apart from that, the unpredictable block boundaries in a compressed file also bring challenges to decompressing multiple blocks in parallel [58]. As an alternative, researchers also look for intra-block paral- 2 Gzip is an implementation of DEFLATE [62].…”

Section: Lz77-basedmentioning

confidence: 99%

In-memory database acceleration on FPGAs: a survey

et al. 2019

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While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at mainmemory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.

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“…Speculative decoding of variable-length codes has been proposed by Jang et al 28 Their method performs pattern matching on the entropy bitstream to determine EOB codewords. EOB marks the end of a block and can thus be used as the starting position for parallel decoding.…”

Section: Related Workmentioning

confidence: 99%

JParEnt: Parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures

Sodsong

Jung

Park

et al. 2017

Concurrency and Computation

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Summary The JPEG format employs Huffman codes to compress the entropy data of an image. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. JParEnt conducts JPEG decompression in two steps: (1) an efficient sequential scan of the entropy data on the CPU to determine the start‐positions (boundaries) of coefficient blocks in the bitstream, followed by (2) a parallel entropy decoding step on the graphics processing unit (GPU). The block boundary scan constitutes a reinterpretation of the Huffman‐coded entropy data to determine codeword boundaries in the bitstream. We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU. This configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We leverage additional parallelism through pipelined execution across CPU and GPU. For systems providing a unified address space between CPU and GPU, we employ zero‐copy to completely eliminate the data transfer overhead. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For a selection of more than 1000 JPEG images, JParEnt outperforms the SIMD–implementation of the libjpeg‐turbo library by up to a factor of 4.3×, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2×. JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg‐turbo on average. Given this new ratio for the sequential part of JPEG decompression, JParEnt achieves up to 97% of the maximum attainable speedup (95% on average). On the IGP‐based desktop platform, JParEnt achieves energy savings of up to 45% compared to libjpeg‐turbo's SIMD‐implementation.

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Practical speculative parallelization of variable-length decompression algorithms

Cited by 8 publications

References 18 publications

An Efficient High-Throughput LZ77-Based Decompressor in Reconfigurable Logic

An Efficient High-Throughput LZ77-Based Decompressor in Reconfigurable Logic

In-memory database acceleration on FPGAs: a survey

JParEnt: Parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures

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