PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

A fast Bayesian Network library for modeling and optimization tasks

keywords HYBRID COMPUTATION, OPTIMIZATION ALGORITHMS, PARALLEL COMPUTING, ARCHITECTURE SOFTWARE DISTRIBU, STATISTICAL ANALYSIS

Reference persons STEFANO DI CARLO, ALESSANDRO SAVINO

Research Groups TESTGROUP - TESTGROUP

Thesis type RESEARCH / EXPERIMENTAL

Description Goals: Build a C++ Bayesian Network library resorting to all modern programming paradigms including advanced modelling features and an extremal optimization engine.
Description:
The library is going to be composed by two set of tools:
1. The Bayesian Network core: it provides all programming tools to generate and analyze BNs. The core is going to be build resorting to all available programming paradigm: multi-threading, mixed CPU-GPU computations, etc.
2. The Extremal Optimization engine: build above the core, the engine must resort to all analysis capabilities to exploit any kind of multi-objective optimization. The engine will include a meta-programming layer to describe the optimization rules to support the optimization with the maximum flexibility.

Learned Outcomes: Advanced C/C++ programming, multi-thread and mixed computation, Bayesian Theory, Optimization algorithms.

Required skills C/C++ programming, basic parallel computing (from Operating Systems lectures)


Deadline 27/07/2021      PROPONI LA TUA CANDIDATURA




© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti