mkl

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

Fast implementation of BERT inference directly on NVIDIA (CUDA, CUBLAS) and Intel MKL

Fast inference engine for Transformer models

Julia bindings to the FFTW library for fast Fourier transforms

monolish: MONOlithic LIner equation Solvers for Highly-parallel architecture

A package for Multiple Kernel Learning in Python

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

Fast Unit Root Tests and OLS regression in C++ with wrappers for R and Python

automatic color-grading

End-to-end speech recognition using TensorFlow

A simple cross platform .NET API for Intel MKL

Daany - .NET DAta ANalYtics .NET 5 library with the implementation of DataFrame, Time series decompositions and Linear Algebra routines BLASS and LAPACK.

Redistribute Intel MKL as a crate

NumPy-based Python interface to Intel (R) MKL FFT functionality

PyTorch implementation of the Complex Steerable Pyramid

Least squares adjustment software

Foundational library for Kernel methods in pattern analysis and machine learning

“云上炼丹师”中的云

Make available to Julia the sparse functionality in MKL

Python wrapper for Intel Math Kernel Library (MKL) matrix multiplication

Deep Learning With C++

NumPy-like API accelerated with SYCL

A third-party distribution of Multiwfn for gfortran, 100% free!

Optimized tensorflow wheels binaries build for macos

implement bert in pure c++

Basic Linear Algebra Subprograms for .Net

Tools to run and parse MKL verbose mode

The repository targets the OpenCL gemm function performance optimization. It compares several libraries clBLAS, clBLAST, MIOpenGemm, Intel MKL(CPU) and cuBLAS(CUDA) on different matrix sizes/vendor's hardwares/OS. Out-of-the-box easy as MSVC, MinGW, Linux(CentOS) x86_64 binary provided. 在不同矩阵大小/硬件/操作系统下比较几个BLAS库的sgemm函数性能，提供binary，开盒即用。

Numerical computing library for linear algebra and task-based parallelism.