Adaptive loss‐less data compression method optimized for GPU decompression

Funasaka, Shunji; Nakano, Koji; Ito, Yasuaki

doi:10.1002/cpe.4283

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…However, practically all LDC and decompression algorithms are shown to be extremely inefficient when parallelized, as they rely on sequentially updated dictionaries. The primary objective of [ 16 ] was to develop a novel LDC model called adaptive lossless (ALL) data compression. When utilized with graphics processing units (GPU), the model was constructed in such a way that the data compression ratio was reasonable, while decompression was efficient.…”

Section: Related Workmentioning

confidence: 99%

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Ramalingam

Mehbodniya

Webber

et al. 2022

Computational Intelligence and Neuroscience

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Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

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Section: Related Workmentioning

confidence: 99%

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Ramalingam

Mehbodniya

Webber

et al. 2022

Computational Intelligence and Neuroscience

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“…Instead, Gompresso [48] utilizes a slightly modified LZ77 algorithm and partition-based Huffman coding to improve the compression/decompression throughput on GPUs. Similarly, Adaptive Loss-Less (ALL) data compression [12] exploits run-length and adaptive dictionarybased coding for GPUs, as well as a partition-based compression/decompression scheme to improve throughput. However, both research target general-purpose applications; hence, they are sub-optimal for DNN training.…”

Section: Related Workmentioning

confidence: 99%

L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training

Bae¹,

Baek²,

Ham³

et al. 2022

Preprint

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The training process of deep neural networks (DNNs) is usually pipelined with stages for data preparation on CPUs followed by gradient computation on accelerators like GPUs. In an ideal pipeline, the end-to-end training throughput is eventually limited by the throughput of the accelerator, not by that of data preparation. In the past, the DNN training pipeline achieved a near-optimal throughput by utilizing datasets encoded with a lightweight, lossy image format like JPEG. However, as high-resolution, losslessly-encoded datasets become more popular for applications requiring high accuracy, a performance problem arises in the data preparation stage due to low-throughput image decoding on the CPU. Thus, we propose L3, a custom lightweight, lossless image format for high-resolution, high-throughput DNN training. The decoding process of L3 is effectively parallelized on the accelerator, thus minimizing CPU intervention for data preparation during DNN training. L3 achieves a 9.29× higher data preparation throughput than PNG, the most popular lossless image format, for the Cityscapes dataset on NVIDIA A100 GPU, which leads to 1.71× higher end-to-end training throughput. Compared to JPEG and WebP, two popular lossy image formats, L3 provides up to 1.77× and 2.87× higher end-to-end training throughput for ImageNet, respectively, at equivalent metric performance.

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“…The performance of compression is important for sensors, so the studies of parallel computing and hardware acceleration such as ASIC (application-specific integrated circuit) and FPGA (field-programmable gate array) [16][17][18] are valuable. A hotspot of researches is the GPU (graphics processing unit) acceleration [19][20][21]. But as we mentioned in [4], the problem is the parallel threads split the data window into 3 Journal of Sensors smaller slices and then reduce the compression ratio.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Block-Split Array Coding Algorithm for Long-Stream Data Compression

Qin

2020

Journal of Sensors

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With the advent of IR (Industrial Revolution) 4.0, the spread of sensors in IoT (Internet of Things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. Hence, the study of big data compression is valuable in the field of sensors. A problem is how to compress the long-stream data efficiently with the finite memory of a sensor. To maintain the performance, traditional techniques of compression have to treat the data streams on a small and incompetent scale, which will reduce the compression ratio. To solve this problem, this paper proposes a block-split coding algorithm named “CZ-Array algorithm,” and implements it in the shareware named “ComZip.” CZ-Array can use a relatively small data window to cover a configurable large scale, which benefits the compression ratio. It is fast with the time complexity O(N) and fits the big data compression. The experiment results indicate that ComZip with CZ-Array can obtain a better compression ratio than gzip, lz4, bzip2, and p7zip in the multiple stream data compression, and it also has a competent speed among these general data compression software. Besides, CZ-Array is concise and fits the hardware parallel implementation of sensors.

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Adaptive loss‐less data compression method optimized for GPU decompression

Cited by 16 publications

References 13 publications

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training

Block-Split Array Coding Algorithm for Long-Stream Data Compression

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