vector-max-cuda

Performance of sequential vs CUDA-based vector element max.

https://github.com/puzzlef/vector-max-cuda

Science Score: 41.0%

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basics cuda element experiment max vector
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Performance of sequential vs CUDA-based vector element max.

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  • Host: GitHub
  • Owner: puzzlef
  • License: mit
  • Language: C++
  • Default Branch: main
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  • Size: 57.6 KB
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basics cuda element experiment max vector
Created over 3 years ago · Last pushed about 3 years ago
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README.md

Comparing performance of sequential vs CUDA-based vector element max.

For each experiment given below, we attempt each approach on a number of vector sizes, running each approach 5 times per size to get a good time measure. Note that time taken to copy data back and forth from the GPU is not measured, and the sequential approach does not make use of SIMD instructions. The experiments are done with guidance from Prof. Kishore Kothapalli and Prof. Dip Sankar Banerjee.


Comparison with Sequential approach

This experiment (compare-sequential, main) compares the performance between finding max(x) using a single thread (sequential) and using CUDA (not power-of-2 and power-of-2 reduce). Here x is a 32-bit integer vector. While it might seem that CUDA approach would be a clear winner, the results indicate it is dependent upon the workload. Results indicate that from 10^5 elements, CUDA approach performs better than sequential. Both CUDA approaches (not power-of-2/power-of-2 reduce) seem to have similar performance. All outputs are saved in a gist. Some charts are also included below, generated from sheets.



References




ORG DOI

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  • Name: puzzlef
  • Login: puzzlef
  • Kind: organization

A summary of experiments.

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Sahu
    given-names: Subhajit
    orcid: https://orcid.org/0000-0001-5140-6578
title: "puzzlef/max-sequential-vs-cuda: Performance of sequential vs CUDA-based vector element max "
version: 1.0.0
doi: 10.5281/zenodo.7256199
date-released: 2022-10-27

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