PARALLEL ALGORITHMS FOR NONLINEAR PROGRAMMING AND APPLICATIONS IN PHARMACEUTICAL MANUFACTURING

Effective manufacturing of pharmaceuticals presents a number of challenging optimization problems due to complex distributed, time-independent models and the need to handle uncertainty. These challenges are multiplied when real-time solutions are required. The demand for fast solution of nonlinear optimization problems, coupled with the emergence of new concurrent computing architectures, drives the need for parallel algorithms to solve challenging NLP problems. The goal of this work is the development of parallel algorithms for nonlinear programming problems on different computing architectures, and the application of large-scale nonlinear programming on challenging problems in pharmaceutical manufacturing

Multi-threaded Simulation of 4G Cellular Systems within the LTE-Sim Framework

Nowadays, an always increasing number of researchers and industries are putting a large effort in the design and the implementation of protocols, algorithms, and network architectures targeted at the the emerging 4G cellular technology. In this context, multi-core/multi-processor simulation tools can accelerate their activities by drastically reducing the time required to simulate complex scenarios. Unfortunately, today's available tools are mostly single-threaded and they cannot exploit the performance gain offered by parallel programming approaches. To bridge this gap, we have significantly upgraded the LTE-Sim framework by implementing a concurrent scheduling algorithm, namely the Multi-Master Scheduler, aimed at efficiently handling events in a parallel manner, while guaranteeing the correct execution of the simulation itself. Experimental results will demonstrate the effectiveness of our proposal and the performance gain that can be achieved with respect to other classical event scheduling algorithms