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GSoC_2015_project_linalg
Soumyajit De edited this page Feb 17, 2015
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- Rahul (github: lambday, IRC: lambday)
- Sergey (github: lisitsyn, IRC: lisitsyn)
- Heiko (github: karlnapf, IRC: HeikoS)
Medium. You need knowledge on
- Advanced C/C++ (knowledge about C++11 is a plus). A reasonable level of comfort with templates, template specialization, partial specialization, SFINAE, ability to read complex templated code
- Numerical Linear algebra
- Eigen3, LAPACK (src level familiarity is a plus)
- GPU programming (ViennaCL) ( -do- )
- Shogun algorithms
- OpenMP [Optional]
This project aims to finalize and polish Shogun's internal framework for linear algebra. The goal is to offer our algorithm coders a backend independent interface that they can use for their linear algebra calls, such as
- factorizing matrices
- linear solves
- eigen solvers
- applying the same operation to every element of a large matrix
- dot products
- various utilities
Rather than writing implementation against particular backends (we currently use Eigen3, Lapack, ViennaCL and Shogun's native implementation), algorithms should be written against
linalg. This allows to change backends at compile time, and in particular makes our algorithms independent of the current trend in linear algebra software. It also will allow to put many operations on GPUs without changing the implementation (to an extend).
Rahul, this is for you
- What has been done?
- What is to be done?
- Where do we want to get?
- A large part will be to read through Shogun's core algorithms and make them use the new interface
- Create github issues on using the existing linalg calls in algorithms
- Rahul, can you spell out the steps that have to be done? Like which type of operations, that we want them all in Eigen3/LAPACK/OpenMP/Vienna/native, etc?
- ...
Rahul, maybe do an openmp backend for the independent jobs? Not sure here though. What do you think?
This project will massively increase both performance and sustainability of Shogun. We will get parallelism of many algorithms for free and at the same time open up ways for using different, better, backends (such as GPUs) in the future. It will furthermore allow to write algorithms without knowledge of backend internals. Nobody wants to deal with Lapack directly ;)