Optimizing LZSS compression on GPGPUs

Özsoy, Adnan; Swany, Martin; Chauhan, Arun

doi:10.1016/j.future.2013.06.022

Cited by 10 publications

(13 citation statements)

References 12 publications

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“…Algoritma ini telah dikembangkan dengan beberapa kombinasi untuk optimalisasi kompresi dengan algoritma lainnya, seperti LZW dan Huffman [2]- [5]. Selain itu, telah banyak perbaikan dari error yang dihasilkan dari algoritma kompresi LZSS untuk menghasilkan algoritma LZSS yang lebih optimal [3], [4], [6]- [12]. Algoritma ini juga telah dimodifikasi pada desain dan konsep yang baru [13], [14].…”

Section: Pendahuluanunclassified

Pengelolaan Arsip Secara Digital Menggunakan Algoritma LZSS Modifikasi untuk Kompresi File

Sujono¹,

Maxrizal²,

Novianto³

2020

JEPIN

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Dokumen yang menumpuk dalam bentuk hardcopy dapat menimbulkan suatu masalah, baik dalam penataan, keamanan dokumen maupun ruang penyimpanan dokumen. Selain itu perawatan ekstra diperlukan untuk mencegah kerusakan pada dokumen penting yang disebabkan oleh serangga dan lain-lain. Dengan teknologi sekarang ini dokumen dapat disimpan dalam bentuk file dan sekaligus ukuran file dapat diperkecil untuk meminimalkan media penyimpanan dan penyusunan dokumen dapat ditata dengan baik sehingga pada saat pencarian dokumen menjadi lebih mudah. File merupakan data digital yang berisi informasi. Untuk memperkecil ukuran file yaitu dengan cara dipadatkan atau biasa disebut dengan kompresi sehingga ukuran file menjadi lebih kecil dari ukuran semula. Pada penelitian ini, metode yang digunakan untuk kompres file adalah LZSS (Lempel Ziv Strorer-Szymanski) yang telah dimodifikasi untuk menghasilkan kompres yang lebih optimal. Algoritma kompresi LZSS modifikasi dikembangkan berdasarkan konsep algoritma kompresi LZSS (pengkodean (offset,len)) serta penambahan pengkodean (offset,-len). Dari 9 sampel file yang dipilih sebagai data simulasi, diperoleh rata-rata rasio kompresi algoritma LZSS adalah 75,40% dan rata-rata kompresi algoritma LZSS modifikasi adalah 67,86%. Hal ini menunjukkan bahwa algoritman LZSS modifikasi menghasilkan tingkat kompresi yang lebih baik.

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Section: Pendahuluanunclassified

Pengelolaan Arsip Secara Digital Menggunakan Algoritma LZSS Modifikasi untuk Kompresi File

Sujono¹,

Maxrizal²,

Novianto³

2020

JEPIN

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“…Methods devised for general image compression include parallelized variants of JPEG 2000 [18], LZ77 [2], LZSS [26], and 3D fast wavelets [5]. Multiple parallel algorithms targeted for medical images are available as well.…”

Section: Image Compressionmentioning

confidence: 99%

Compression of Image Sequences in Interactive Medical Teleconsultations

Malawski¹,

Czekierda²

2017

csci

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Interactive medical teleconsultations are an important tool in modern medical practice. Their applications include remote diagnostics, conferences, workshops, and classes for students. In many cases, standard medium or low-end machines are employed, and the teleconsultation systems must be able to provide a high quality of user experience with very limited resources. Particularly problematic are large datasets consisting of image sequences that need to be accessed fluently. The main issue is insufficient internal memory; therefore, proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed onthe-fly when displayed is difficult to implement due to performance issues. In this paper, we present methods for both lossy and lossless compression of medical image sequences that only require compatibility with the Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction, and performance, these methods have been proven to be suitable and beneficial for medical teleconsultation applications.

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“…A parallel algorithm for LZ decompression, depending on the type of data dependencies, does not guarantee efficient GPU memory access [18]. Huffman encoding is typically added to improve the compression ratio [19]. However, decoding is hard to parallelize because it has to identify codeword boundaries for variable-length coding schemes.…”

Section: Related Workmentioning

confidence: 99%

Massively-Parallel Lossless Data Decompression

Sitaridi

Mueller

Kaldewey

et al. 2016

2016 45th International Conference on Parallel Processing (ICPP)

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Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2× speed-up in a head-to-head comparison with several multi-core CPU-based libraries, while achieving a 17 % energy saving with comparable compression ratios.

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Optimizing LZSS compression on GPGPUs

Cited by 10 publications

References 12 publications

Pengelolaan Arsip Secara Digital Menggunakan Algoritma LZSS Modifikasi untuk Kompresi File

Pengelolaan Arsip Secara Digital Menggunakan Algoritma LZSS Modifikasi untuk Kompresi File

Compression of Image Sequences in Interactive Medical Teleconsultations

Massively-Parallel Lossless Data Decompression

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