vector-sum
Comparison of vector element sum using various data types.
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Comparison of vector element sum using various data types.
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README.md
Comparison of vector element sum using various data types.
For all experiments, each approach is attempted on a number of vector sizes, running each approach 5 times per size to get a good time measure. The experiments are done with guidance from Prof. Dip Sankar Banerjee and Prof. Kishore Kothapalli.
Comparision with Float and BFloat16 storage types
In this experiment (float-vs-bfloat16, main), we comparing the performance of finding the sum of numbers between, the number stored as float or bfloat16. While it seemed to me that bfloat16 method would be a clear winner because of reduced memory bandwidth requirement, for some reason it is only slightly faster. This is possibly because memory loads are anyway always 32-bit. The only reason using bfloat16 is slightly faster could possibly be because it allows data to be retained in cache for a longer period of time (because of its small size). Note that neither approach makes use of SIMD instructions which are available on all modern hardware.
References
- A Study of BFLOAT16 for Deep Learning Training
- Convert FP32 to Bfloat16 in C++
- Why is there no 2-byte float and does an implementation already exist?
- Is it safe to reinterpret_cast an integer to float?
Owner
- Name: puzzlef
- Login: puzzlef
- Kind: organization
- Website: https://puzzlef.github.io/
- Repositories: 10
- Profile: https://github.com/puzzlef
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/sum-float-vs-bfloat16: Performance of vector element sum using float vs bfloat16 as the storage type"
version: 1.0.0
doi: 10.5281/zenodo.6717282
date-released: 2022-06-24
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