Like no other programming language in the world, it supports the imperative procedural and object-oriented programming language. It is used for technical computation, all because of its powerful performance. It is one of the most potent statistics programming languages in the world. List of companies that use Matlab and Octave!.Which is more popular, Matlab or Octave?.If you want to be able to modify every detail, swap BLAS libraries and libm implementations, etc., you may want to look into using an open-source software instead, which as above I'd recommend Julia (or you may be able to do this with Octave, though I don't know and will refer to whatever documentation they have). That's just one problem among many with closed-source software. You can try swapping out backend BLAS implementations in MATLAB to learn, but it likely won't cause a performance change it may give difficulties because it's very undocumented.

The reason why you won't find a BLAS implementation that 'has both' is because the implementations have to be completely different to fully leverage the GPU, and so at that point they might as well be different libraries since the reason to bundle code is usually for some form of code reuse. You can Google around to reason some people saying this outperforms CUBLAS by like 10%, but the comments are usually old (2013) and blablabla: it's fast enough that it's likely the best option if you're in MATLAB (though if you really want performance, you should look at Julia with CUBLAS, which will have a lower interop overhead and faster user-compiled kernals). There's no reason to replace that.Īs for using GPUs, if you make your array a gpuArray (to do that, just do gpuArray(A)), then you can use MATLAB's matrix multiplication and it will use optimized kernals from MAGMA to perform the computation. MATLAB already comes with Intel MKL for its BLAS implementation. As for interfacing with Octave/MATLAB, I don't know because I don't use them, but hopefully someone else can answer about that. I don't know about ClBLAS, but the others are pretty easy to install on Linux the documentation is clear enough. NVIDIA's CuBLAS is an option for CUDA-enabled GPUs. It has more than just BLAS - it also includes many LAPACK functions and FFT, for instance. If closed-source proprietary BLAS implementations are okay, Intel MKL is a good option for use on multi-core CPUs and Xeon-Phi accelerators. Unfortunately I don't know of benchmarks for clBLAS. For GPUs, there's clBLAS ( ) that implements BLAS using OpenCL.

The library is threaded and written in C and assembly. The website has a DGEMM benchmark, comparing against MKL (see below) and the reference Fortran BLAS. Among open-source BLAS, as far as I know, OpenBLAS ( ) is the best option.

Gnu octave vs matlab