Data

Tools

Indexes

Applications

Experiments

Open Source Science

Accelerating Parallel Operation for Compacting Selected Elements on GPUs

Accelerating Parallel Operation for Compacting Selected Elements on GPUs - Published in JOSS (2022)

https://github.com/yogi-tud/space_gpu

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords from Contributors

drone pde
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: yogi-tud
  • License: apache-2.0
  • Language: Cuda
  • Default Branch: main
  • Size: 3.32 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.md

space_gpu

SPACE is a GPU centric C++ software for compaction experiments. It consists of data generators and a flexible experiment framework. Addtionally, scripts to visualize experiments are provided. For detailed information about compiling and running SPACE, see overview.pdf.

The binary takes a number of different runtime parameters that control the experiment. See run_full.py for a comprehensive example running multiple experiments.

An overview of all parameters: dataset , selectivity, datasize, cluster count, datatype

dataset controls the distribution of bits in the bit mask that is used to select items for write out. dataset values: (0 = uniform, 1= single cluster, 2= multiple cluster)

selectivity as % of 1 bits in mask. Ranging from 0 to 1.

datasize in MiB of input data column. Mask will be generated accordingly.

cluster: number of clusters to distribute across the mask if dataset multiple cluster is picked

datatypes: 1-uint8 2-uint16 3-uint32 4-int 5-float 6-double

Device string: writes a device string back into csv experiment output and generates file names accordingly

subset of algorithms: (1:cub + space 8, 0: all)

Examples:

./gpu_compressstore2 0 0.25 1024 0 3 A100 0

This example runs with a uniform mask distribution and 25% selected elements. The input column is made of 1024 MiB uint32 elements. Output files are named with A100 as device and all algorithms will be peformed.

In CONTRIBUTE a guide how to contribute to the software can be found. contribute

JOSS Paper about SPACE_GPU: DOI

Owner

  • Login: yogi-tud
  • Kind: user
  • Location: Dresden
  • Company: TU Dresden

JOSS Publication

Accelerating Parallel Operation for Compacting Selected Elements on GPUs
Published
August 03, 2022
DOI
Volume 7, Issue 75, Page 4589
Authors
Johannes Fett ORCID
TU Dresden, Germany
Urs Kober
TU Dresden, Germany
Christian Schwarz
TU Dresden, Germany
Dirk Habich
TU Dresden, Germany
Wolfgang Lehner
TU Dresden, Germany
Editor
Daniel S. Katz ORCID
Tags
Compacting GPU Optimization Parallel Euro-Par
View PDF Review Thread Software Archive

GitHub Events

Total
Last Year

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 66
  • Total Committers: 3
  • Avg Commits per committer: 22.0
  • Development Distribution Score (DDS): 0.045
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
yogi-tud y****d 63
Daniel S. Katz d****z@i****g 2
Arfon Smith a****n 1
Committer Domains (Top 20 + Academic)
ieee.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 3
  • Total pull requests: 2
  • Average time to close issues: 2 days
  • Average time to close pull requests: about 4 hours
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 3.67
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • robertszafa (3)
Pull Request Authors
  • arfon (1)
  • danielskatz (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

.github/workflows/main.yml actions
  • actions/checkout v2 composite
  • actions/upload-artifact v1 composite
  • openjournals/openjournals-draft-action master composite