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convergence_bfloat

https://github.com/uisken/convergence_bfloat

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (0.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: UIsken
  • Language: C
  • Default Branch: main
  • Size: 49.4 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme Citation

README.md

Convergence_Bfloat

This is a tool to verify the convegence of the approximation of float32 points via multiple bfloat16 points. Its accuracy is checked in matrixkernels and measured.

Owner

  • Name: JuliusI
  • Login: UIsken
  • Kind: user

University of Jena

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Isken
    given-names: Julius Philip
  - name: "Convergence of Bfloat16 numbers"
title: "Convergence of Bfloat16 numbers"
version: 2.0.4
date-released: 2021-08-11
license: Apache-2.0

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Dependencies

submodules/libxsmm/samples/deeplearning/sparse_training/dlrm/Dockerfile docker
  • ${FROM_IMAGE_NAME} latest build
submodules/libxsmm/samples/deeplearning/conv1dopti_layer/Conv1dOpti-extension/setup.py pypi
submodules/libxsmm/samples/deeplearning/pytorch/xsmmptex/mlpcell/setup.py pypi
submodules/libxsmm/samples/deeplearning/sparse_training/dlrm/roc/setup.py pypi
submodules/libxsmm/samples/deeplearning/sparse_training/fairseq/fairseq/modules/dynamicconv_layer/setup.py pypi
submodules/libxsmm/samples/deeplearning/sparse_training/fairseq/fairseq/modules/lightconv_layer/setup.py pypi
submodules/libxsmm/samples/deeplearning/sparse_training/fairseq/pyproject.toml pypi
submodules/libxsmm/samples/deeplearning/sparse_training/fairseq/setup.py pypi
  • cffi *
  • cython *
  • numpy *
  • regex *
  • sacrebleu *
  • torch *
  • tqdm *
submodules/libxsmm/samples/deeplearning/sparse_training/pcl_mlp/setup.py pypi
submodules/libxsmm/samples/deeplearning/tf_lstm_ops/setup.py pypi