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Soumyajit De edited this page Jan 19, 2015 · 8 revisions

Internal linear algebra library

Table of Contents

Motivation

Linear algebra operations form the backbone for most of the computation components in any Machine Learning library. However, writing all of the required linear algebra operations from scratch is rather redundant and undesired, especially when we have some excellent open source alternatives. In Shogun, we prefer

  • Eigen3 for its speed and simplicity at the usage level,
  • ViennaCL version 1.5 for GPU powered linear algebra operations, and
  • Lapack for its robustness and maturity.

For Shogun maintainers, however, the usage of different external libraries for different operations can lead to a painful task.

  • For example, consider some part of an algorithm originally written using Eigen3 API. But a Shogun user wishes to use ViennaCL for that algorithm instead, hoping to obtain boosted performance utilizing a GPU powered platform. There is no way of doing that without having the algorithm rewritten by the developers using ViennaCL, which leads to duplication of code and effort.
  • Also, there is no way to do a performance comparison for the developers while using different external linear algebra libraries for the same algorithm in Shogun code.
  • It is also somewhat frustrating for a new developer who has to invest significant amount of time and effort to learn each of these external APIs just to add a new algorithm in Shogun.

Features of internal linear algebra library

Shogun's internal linear algebra library (will be referred as linalg hereinafter) is a work-in-progress attempt to overcome these issues. We designed linalg as a modularized internal header only library in order to

  • provide a uniform API for Shogun developers to choose any supported backend without having to worry about the syntactical differences in the external libraries' operations,
  • handle native Shogun types for linear algebra operations without having to convert to external types,
  • have the backend set for each operations at compile-time (for lesser runtime overhead) and therefore intended to be used internally by Shogun developers.

For Shogun developers

Using linalg operations

The operations can be used in two ways:

Relying on globally set linalg backend
  • Global setup is used whenever an operation does not specify a particular backend.
shogun::linalg::operation(arg1, ...);
  • Preferred global backend can be specified via cmake options which sets that backend for all the linalg operations used in this fashion.
# this will set Eigen3 as the global linalg backend for all modules.
# Use -DUSE_VIENNACL for setting ViennaCL as global linalg backend instead
#
cmake -DUSE_EIGEN3

[for other cmake options, please refer to the cmake wiki.]

  • Module specific global setups can also be done via cmake options. We presently have 2 modules - Core and Redux.
# this will set Eigen3 for Redux module's global linalg backend
# and ViennaCL for Core module's global linalg backend.
cmake -DUSE_EIGEN3_REDUX -DUSE_VIENNACL_CORE

[see below for a complete list of supported operations under these modules].

  • If no particular setup is specified in the cmake options, a DEFAULT backend is used as a global backend.
  • Also, if module specific setup is specified but not for all modules, then for the modules the setup is unspecified, DEFAULT backend is used. [see below for the DEFAULT backend settings].
# this will set ViennaCL for Redux module's global linalg backend
# and Default for the rest of the modules
cmake -DUSE_VIENNACL_REDUX
Using operation specific linalg backend
  • It is possible to use a specific backend for a particular operation, if desired, which then ignores the global or module specific setup and works with that particular backend. The backend can be specified using the enum Backend.
shogun::linalg::operation<Backend::backend_name>(arg1, ...);
  • Currently supported backends are EIGEN3, VIENNACL, NATIVE.

[The recommended practice for using linalg is to include just the file linalg.h (in src/shogun/mathematics/linalg) in the implementation of a Shogun algorithm (.cpp file). Please do not include this file in the .h file of an algorithm that is expected to be exposed to the modular API (exposed to SWIG). See CustomKernel.cpp for an example.]

For linalg developers

The structure of linalg consists of three basic components

  • backend identifier and module type specifier (in linalg.h)
  • the operations exposed as global functions (in linalg/internal/modules/)
  • the backend specific implementations (in linalg/internal/implementation/)

Understanding the backend setups

enum class Backend handles the backend identification in the operations. The DEFAULT backend would be the first constant defined. For example, if cmake finds all supported backends, the enum is defined as

// inside shogun::linalg namespace
enum class Backend
{
    EIGEN3, // will be 0
    VIENNACL, // will be 1
    NATIVE, // will be 2
    DEFAULT = 0 // therefore, will be EIGEN3
};

If only ViennaCL is found,

enum class Backend
{
    VIENNACL, // will be 0
    NATIVE, // will be 1
    DEFAULT = 0 // therefore, will be VIENNACL
};

If none of the supported backend libraries is found

enum class Backend
{
    NATIVE, // will be 0
    DEFAULT = 0 // therefore, will be NATIVE
};

NATIVE refers to Shogun's implementation which only provides a few operations. The operations specified with NATIVE backends work even in absence of any external linear algebra libraries. Therefore, while using the global backend, it is always safe to use those operations with a native implementation.

The default backend is Eigen3 (if found).

Understanding the module setups

As mentioned earlier, the supported operations are clustered into different modules in order to provide module specific setup is desired. These modules are defined as structs with the backend information, e.g

template <enum Backend>
struct ModuleName
{
    // assuming that Eigen3 was set as the backend of this module
    const static Backend backend = Backend::EIGEN3;
};

In generic code, the backend set for a module can be inquired with linalg_trait<ModuleName>::backend.

Understanding the global operations

Coming soon.

Understanding the backend specific implementation

Coming soon.

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