Parallelization of the algorithm WHAM with NVIDIA CUDA.

The aim of my thesis is to parallelize the Weighting Histogram Analysis Method (WHAM), which is a popular algorithm used to calculate the Free Energy of a molucular system in Molecular Dynamics simulations. WHAM works in post processing in cooperation with another algorithm called Umbrella Sampling. Umbrella Sampling has the purpose to add a biasing in the potential energy of the system in order to force the system to sample a specific region in the configurational space. Several N independent simulations are performed in order to sample all the region of interest. Subsequently, the WHAM algorithm is used to estimate the original system energy starting from the N atomic trajectories. The parallelization of WHAM has been performed through CUDA, a language that allows to work in GPUs of NVIDIA graphic cards, which have a parallel achitecture. The parallel implementation may sensibly speed up the WHAM execution compared to previous serial CPU imlementations. However, the WHAM CPU code presents some temporal criticalities to very high numbers of interactions. The algorithm has been written in C++ and executed in UNIX systems provided with NVIDIA graphic cards. The results were satisfying obtaining an increase of performances when the model was executed on graphics cards with compute capability greater. Nonetheless, the GPUs used to test the algorithm is quite old and not designated for scientific calculations. It is likely that a further performance increase will be obtained if the algorithm would be executed in clusters of GPU at high level of computational efficiency. The thesis is organized in the following way: I will first describe the mathematical formulation of Umbrella Sampling and WHAM algorithm with their apllications in the study of ionic channels and in Molecular Docking (Chapter 1); then, I will present the CUDA architectures used to implement the model (Chapter 2); and finally, the results obtained on model systems will be presented (Chapter 3).

Analisi quantitativa di alcuni recenti eventi temporaleschi registrati in Emilia-Romagna

Negli ultimi cinque anni, l’Emilia Romagna è stata interessata da 83 fenomeni temporaleschi, che hanno causato allagamenti, smottamenti e anche la perdita di vite umane a Sala Baganza l’11 giugno 2011 e a Rimini il 24 giugno 2013. Nonostante questi fenomeni siano protagonisti di eventi calamitosi, la loro previsione rimane ancora complessa poiché sono eventi localizzati, brevi e molto intesi. Il progetto di Tesi si inserisce in questo contesto e tratta due tematiche principali: la valutazione, quantitativa, della variazione di frequenza degli eventi intensi negli ultimi 18 anni (1995-2012), in relazione ad un periodo storico di riferimento, compreso tra il 1935 ed il 1989 e il confronto tra l’andamento spaziale delle precipitazioni convettive, ottenuto dalle mappe di cumulata di precipitazione oraria dei radar meteorologici e quello ottenuto mediante due tecniche di interpolazione spaziale deterministiche in funzione dei dati pluviometrici rilevati al suolo: Poligoni di Voronoi ed Inverse Distance Weighting (IDW). Si sono ottenuti risultati interessanti nella valutazione delle variazioni dei regimi di frequenza, che hanno dimostrato come questa sembrerebbe in atto per eventi di precipitazione di durata superiore a quella oraria, senza una direzione univoca di cambiamento. Inoltre, dal confronto degli andamenti spaziali delle precipitazioni, è risultato che le tecniche di interpolazione deterministiche non riescono a riprodurre la spazialità della precipitazione rappresentata dal radar meteorologico e che ogni cella temporalesca presenta un comportamento differente dalle altre, perciò non è ancora possibile individuare una curva caratteristica per i fenomeni convettivi. L’approfondimento e il proseguimento di questo ultimo studio potranno portare all’elaborazione di un modello che, applicato alle previsioni di Nowcasting, permetta di valutare le altezze di precipitazione areale, associate a delle celle convettive in formazione e stabilire la frequenza caratteristica dell’evento meteorico in atto a scala spaziale, fornendo indicazioni in tempo reale che possono essere impiegate nelle attività di Protezione Civile.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.