SANComSim: A Scalable, Adaptive and Non-intrusive Framework to Optimize Performance in Computational Science Applications

Abstract

Parallel processing has become the most common solution for developing and executing scientific computing applications. Actually, the best way to obtain good performance ratios is to exploit parallelism in both processing and communications. Although the study of computational performance has historically involved CPU power, currently the CPU is not the only concern in the overall performance. Due to the underlying design of parallel applications, communication networks play a very important role in the field of computational science. Despite the fact that networks used in multicore clusters are fast and have low latency, the amount of transferred data may cause a bottleneck in the communication system, as communicationintensive, parallel applications spend a significant amount of their total execution time exchanging data between processes. Moreover, in most cases, several users are executing different parallel applications at the same time in the cluster.In this paper we present SANComSim, a Scalable, Adaptive and Non-intrusive framework, based on simulation techniques, for optimizing the performance of the network system to execute complex applications. The main objective of this framework is to apply run-time compression, to reduce the data sent through the network, in order to increase the overall system performance. The main features of SANComSim are: adaptability, to dynamically adapt to the current state of the system; portability, the framework is neither focused on a specific programming language nor a platform; non-intrusive, since this framework is based on simulation techniques, which does not require exclusive access of the entire cluster system; scalability, any parallel application, independently of the number of processed and computing nodes, can use this framework to improve performance in cluster systems.

FORTIFIER: a FORmal disTrIbuted Framework to Improve the dEtection of thReatening objects in baggage

“…We would also like to use passive testing techniques to check the proposed framework by using more complex communicating requirements. A third line of work consists in increasing performance due to parallelization (Núñez, Filgueira, & Merayo, 2013;Núñez & Merayo, 2014). Finally, we would like to use learning techniques to improve the performance of our detection algorithms taking into account that an attacker might modify some of the components (López, Núñez, Rodríguez, & Rubio, 2002).…”