Dataset Open Access

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

Griebler; Hoffmann; Loff; Danelutto; Fernandes


Citation Style Language JSON Export

{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1194606", 
  "title": "High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications", 
  "issued": {
    "date-parts": [
      [
        2018, 
        3, 
        9
      ]
    ]
  }, 
  "abstract": "<p>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<br>\nwith 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.</p>", 
  "author": [
    {
      "family": "Griebler"
    }, 
    {
      "family": "Hoffmann"
    }, 
    {
      "family": "Loff"
    }, 
    {
      "family": "Danelutto"
    }, 
    {
      "family": "Fernandes"
    }
  ], 
  "type": "dataset", 
  "id": "1194606"
}
8
2
views
downloads
All versions This version
Views 88
Downloads 22
Data volume 7.6 MB7.6 MB
Unique views 88
Unique downloads 22

Share

Cite as