{"title":"CULZSS-Bit:一种gpgpu上无损数据压缩的位矢量算法","authors":"Adnan Ozsoy","doi":"10.1109/DISCS.2014.9","DOIUrl":null,"url":null,"abstract":"In this paper, we describe an algorithm to improve dictionary based lossless data compression on GPGPUs. The presented algorithm uses bit-wise computations and leverages bit parallelism for the core part of the algorithm which is the longest prefix match calculations. Using bit parallelism, also known as bit-vector approach, is a fundamentally new approach for data compression and promising in performance for hybrid CPU-GPU environments.The implementation of the new compression algorithm on GPUs improves the performance of the compression process compared to the previous attempts. Moreover, the bit-vector approach opens new opportunities for improvement and increases the applicability of popular heterogeneous environments.","PeriodicalId":278119,"journal":{"name":"2014 International Workshop on Data Intensive Scalable Computing Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"CULZSS-Bit: A Bit-Vector Algorithm for Lossless Data Compression on GPGPUs\",\"authors\":\"Adnan Ozsoy\",\"doi\":\"10.1109/DISCS.2014.9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe an algorithm to improve dictionary based lossless data compression on GPGPUs. The presented algorithm uses bit-wise computations and leverages bit parallelism for the core part of the algorithm which is the longest prefix match calculations. Using bit parallelism, also known as bit-vector approach, is a fundamentally new approach for data compression and promising in performance for hybrid CPU-GPU environments.The implementation of the new compression algorithm on GPUs improves the performance of the compression process compared to the previous attempts. Moreover, the bit-vector approach opens new opportunities for improvement and increases the applicability of popular heterogeneous environments.\",\"PeriodicalId\":278119,\"journal\":{\"name\":\"2014 International Workshop on Data Intensive Scalable Computing Systems\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Workshop on Data Intensive Scalable Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DISCS.2014.9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Workshop on Data Intensive Scalable Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DISCS.2014.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CULZSS-Bit: A Bit-Vector Algorithm for Lossless Data Compression on GPGPUs
In this paper, we describe an algorithm to improve dictionary based lossless data compression on GPGPUs. The presented algorithm uses bit-wise computations and leverages bit parallelism for the core part of the algorithm which is the longest prefix match calculations. Using bit parallelism, also known as bit-vector approach, is a fundamentally new approach for data compression and promising in performance for hybrid CPU-GPU environments.The implementation of the new compression algorithm on GPUs improves the performance of the compression process compared to the previous attempts. Moreover, the bit-vector approach opens new opportunities for improvement and increases the applicability of popular heterogeneous environments.