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High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications

Griebler; Hoffmann; Loff; Danelutto; Fernandes


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  <dc:creator>Griebler</dc:creator>
  <dc:creator>Hoffmann</dc:creator>
  <dc:creator>Loff</dc:creator>
  <dc:creator>Danelutto</dc:creator>
  <dc:creator>Fernandes</dc:creator>
  <dc:date>2018-03-09</dc:date>
  <dc:description>The stream processing domain is present in several real-world applications that are running on multi-core systems. In this paper, we focus on data compression applications that are an important sub-set of this domain. Our main goal is to assess the programmability and efficiency of domain-specific language called SPar. It was specially designed for expressing stream parallelism and it promises higher-level parallelism abstractions without significant performance losses. Therefore, we parallelized Lzip and Bzip2 compressors
with SPar and compared with state-of-the-art frameworks. The results revealed that SPar is able to efficiently exploit stream parallelism as well as provide suitable abstractions with less code intrusion and code re-factoring.</dc:description>
  <dc:identifier>https://zenodo.org/record/1194606</dc:identifier>
  <dc:identifier>10.5281/zenodo.1194606</dc:identifier>
  <dc:identifier>oai:zenodo.org:1194606</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/644235/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1194605</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
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