Description

In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. Automatic differentiation is an alternative technique to Symbolic differentiation and Numerical differentiation (the method of finite differences). CLAD is based on Clang which will provide the necessary facilities for code transformation. The AD library is able to differentiate non trivial functions, to find a partial derivative for trivial cases and has good unit test coverage. There was a proof-of-concept implementation for computation offload using OpenCL.

Task ideas and expected results:

• The student should teach AD how to generate OpenCL/CUDA code automatically for a given derivative.
• The implementation should be very well tested and documented. Prepare a final poster of the work and be ready to present it.

Requirements: Advanced C++, Clang abstract syntax tree (AST), CUDA/OpenCL basic math.

Mentors:

• Vassil Vassilev

Corresponding Project

• ROOT

Participating Organizations

• Princeton

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