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项目作者： faruknane

项目描述 ：
Numeric Library for Deep Learning purposes
高级语言： C#
项目地址: git://github.com/faruknane/PerformanceWork.git
创建时间： 2019-08-23T01:31:35Z
项目社区：https://github.com/faruknane/PerformanceWork
开源协议：MIT License
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This project is no longer maintained!

" class="reference-link">PerformanceWork

This library is dependent on Intel MKL, Nvidia Cuda 11.1, Cutensor 1.2.2.5. Works on both CPU and Nvidia GPUs. Currently, native codes are compiled for windows 10 only.

Hardware Requirements

For GPUs:

  • Nvidia compute_52, sm_52; compute_60, sm_60; compute_61, sm_61; compute_70, sm_70; compute_75, sm_75; compute_80, sm_80; compute_86, sm_86;

    For CPUs:

  • x86-64 assembly support (for intel mkl and native c++ codes)
  • Avx2 support

Supported Tensor Operations

  • Element-Wise Add / Subtract / Multiply / Divide
  • Element-Wise Power (CPU-Only)
  • Relu (CPU-Only)
  • Sigmoid (CPU-Only)
  • Softmax (CPU-Only)
  • Matrix Multiplication
  • Einsum Operation (GPU-Only)
  • Expand and Shrink Tensor (CPU-Only)

Supported Gradient Tensor Operations

  • Element-Wise Add / Subtract / Multiply / Divide
  • Element-Wise Power (CPU-Only)
  • Relu (CPU-Only)
  • Sigmoid (CPU-Only)
  • Softmax (CPU-Only)
  • Matrix Multiplication
  • Expand and Shrink Tensor (CPU-Only)