GPU Based Parallel Algorithms to Solve Bayesian Networks
Reference persons STEFANO DI CARLO
External reference persons Alessandro Vallero (firstname.lastname@example.org)
Research Groups TESTGROUP - TESTGROUP
Thesis type SPERIMENTALE
Description Bayesian Networks (BNs) are an efficient statistical model to represent multivariate statistical distribution functions.
BNs find application in several fields such as medical, artificial intelligence and reliability. Because of the great potential offered by BNs, solving them is extremely important. Unfortunately, find the exact solution of Bayesian network is an NP-hard problem, but to cope with this, in the literature, several approximation algorithms have been proposed.
The objectives of this thesis are:
- Analyzing algorithms for the approximate solution of BNs
- Parallelizing efficiently these algorithms by means of parallel computing
libraries, such as CUDA, OpenCL, OpenMPI, OpenMP, Posix, ...
- Analyzing performance of parallel algorithms
Don't miss the opportunity to work with one the most powerful GPGPUs ever built: NVIDIA Tesla K20 (3.52 TFLOPS, 2496 CUDA cores, 5 GB of GDDR5, Memory bandwidth 208 GB/sec)
Required skills C/C++ Programming
Deadline 04/07/2016 PROPONI LA TUA CANDIDATURA