Solving general MRF MAP problems on heterogeneous systems (BSc / MSc)

Solving general MRF MAP problems on heterogeneous systems (BSc / MSc)

Our general-purpose MRF MAP solver “mapMAP” [1] solves large-scale, irregular shaped Markov Random Fields in short amounts of time by exploiting the massive parallelism available in today's hardware.

Originally designed with the GPU in mind [and implemented thereon], our current release uses SIMD units and multiple cores on the CPU.

The tree-decomposition scheme that mapMAP is based on decomposes the MRF graph in each iteration and solves each piece separately.

This natural “higher-level” parallelism enables us to make use of a relatively fresh area of computing: heterogeneous systems.

In this thesis, you will enbale mapMAP to do just that: using our CPU and your GPU code, you will dynamically distribute work between CPU and GPU and find optimal tradeoffs for large-scale MRF datasets.

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