Merge branch 'refactor'

CHANGES.md

@@ -0,0 +1,12 @@
+Change Log
+=====
+
+mshadow-1.0
+=====
+* Initial release
+
+mshadow-2.0: in progress
+=====
+* Support multiple data type
+* Great refactoring of code
+* Parameter server interface for MultiGPU and distributed learning

README.md

@@ -1,48 +1,38 @@
 mshadow: Matrix Shadow
 ======
 
-Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA
+MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support ***efficient***,
+***device invariant*** and ***simple*** tensor library for machine learning project that aims for both simplicity and performance.
 
-Creater: Bing Xu and Tianqi Chen
-
-
-Documentation and Tutorial: https://github.com/tqchen/mshadow/wiki
-
-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*. 
+MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.
 
+* [Contributors](https://github.com/tqchen/mshadow/graphs/contributors)
+* [Tutorial](guide)
+* [Documentation](doc)
+* [Parameter Server Interface for GPU Tensor](guide/mshadow-ps)
 
 Features
 =====
-* 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
+* Efficient: all the expression you write will be lazily evaluated and compiled into optimized code
+  - No temporal memory allocation will happen for expression you write
+  - mshadow will generate specific kernel for every expression you write in compile time.
+* Device invariant: you can write one code and it will run on both CPU and GPU
+* Simple: mshadow allows you to write machine learning code using expressions.
+* Whitebox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called
 * 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.
+* MultiGPU and Distributed ML: mshadow-ps interface allows user to write efficient MultiGPU and distributed programs in an unified way.
 
+Version
+======
+* This version mshadow-2.x, there are a lot of changes in the interface and it is not backward compatible with mshadow-1.0
+  - If you use older version of cxxnet, you will need to use the legacy mshadow code
+* For legacy code, refer to [Here](https://github.com/tqchen/mshadow/releases/tag/v1.1)
+* Change log in [CHANGES.md](CHANGES.md)
 
 Related Projects
 =====
-* CXXNET: neural network implementation based on mshadow: https://github.com/antinucleon/cxxnet
+* [CXXNET: large-scale deep learning backed by mshadow](https://github.com/antinucleon/cxxnet)
+* [Parameter Server](https://github.com/mli/parameter_server)
+  - Parameter server project provides distributed back-end for mshadow-ps
+  - mshadow-ps extends original parameter server to support async updates for GPU Tensor

doc/Doxyfile

@@ -8,7 +8,7 @@ PROJECT_NAME           = "mshadow"
 PROJECT_NUMBER         =
 PROJECT_BRIEF          =
 PROJECT_LOGO           =
-OUTPUT_DIRECTORY       = ../doc
+OUTPUT_DIRECTORY       = doc
 CREATE_SUBDIRS         = NO
 OUTPUT_LANGUAGE        = English
 BRIEF_MEMBER_DESC      = YES
@@ -95,13 +95,13 @@ WARN_LOGFILE           =
 #---------------------------------------------------------------------------
 # configuration options related to the input files
 #---------------------------------------------------------------------------
-INPUT                  =
+INPUT                  = mshadow mshadow-ps
 INPUT_ENCODING         = UTF-8
 FILE_PATTERNS          =
 RECURSIVE              = NO
 EXCLUDE                =
 EXCLUDE_SYMLINKS       = NO
-EXCLUDE_PATTERNS       = *-inl.hpp 
+EXCLUDE_PATTERNS       = *-inl.* utils.h thread_util.h thread.h kv_array.h
 EXCLUDE_SYMBOLS        = mshadow::expr::Plan* mshadow::expr::*Engine* 
 EXAMPLE_PATH           =
 EXAMPLE_PATTERNS       =