Description:

Description: Most machine learning algorithms requires matrix,tensor operations frequently. For example, Eq.(1) is a common SGD update rule, where the weight can be a vector, matrix or 3D tensor. Eq.(2) is the backpropagtion rule:

(1) weight = - eta * ( grad + lambda * weight ); (2) gradin = dot( gradout, netweight.T() ); These operations are not hard to implement, even in C++. The first one is elementwise operations, and can easily be written as

for( int index = 0; index < weight.length; index ++ ){ weight[index] = - eta * ( grad[index] + lambda * weight[index] ); } Eq.(2) is matrix product, and we can use standard BLAS packages such as Intel MKL. It will looklike

sgemm( CblasNoTrans, CblasTrans, n, m, k, 1.0, gradout.ptr, lda, netweight.ptr, ldb, 0.0, gradin.ptr, ldc ); However:

It is annoying to write these codes repeatively, and they are not intuitive. What if we want to port our code to GPU? We need to rewrite our code in CUDA mshadow is a unified C++/CUDA lib to to write Eq.(1) and Eq.(2) in C++, and translate them to the for loop and standard packages such as MKL, CuBLAS in compile time.

Feature:

Shadow instead of giant: mshadow does not implement all of the functions, it is more of a wrapper to translated easy-to-read code to standard 'giant' packages such as MKL Whitebox instead of blackbox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called Unified CPU/GPU code: write a code and it should run in both CPU and GPU Lightweight library: light amount of code to support frequently used functions in machine learning Extendable: user can write simple functions that plugs into mshadow and run on GPU/CPU, no experience in CUDA is required.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Mac Os X

Data Formats: Any

Tags: Mkl, Cuda, Tensor

Archive: download here

Comments

Bing Xu (on April 10, 2014, 02:58:32)

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