Mario Rosati

Efficient particle labeling in atomistic simulations

The authors develop an efficient particle labeling procedure based on a linked cell algorithm which is shown to reduce the computing time for a molecular dynamics simulation by a factor of 3. They prove that the improvement of performance is due to the efficient fulfillment of both spatial and temporal locality principles, as implemented by the contiguity of labels corresponding to interacting atoms. Finally, they show that the present label reordering procedure can be used to devise an efficient parallel one-dimensional domain decomposition molecular dynamics scheme.

Computational Materials Science application programming interface (CMSapi): a tool for developing applications for atomistic simulations

We describe our computational library for atomistic simulations: CMSapi. CMSapi provides building blocks for either molecular dynamics, molecular mechanics and other kinds of atomistic simulation techniques. CMSapi features a set of routines which allow for building a MD program for short ranged interactions, running NVE, NVT and NPT ensembles. In CMSapi it is implemented an improved algorithm to apply Minimum Image Convention. We also introduced on-the-fly reordering of atomic labeling to improve locality on memory access. Computer performances of CMSapi are discussed.

Parallel tight-binding molecular dynamics simulations on symmetric multi-processing platforms

We introduce the tight-binding molecular dynamics formalism and describe its numerical implementation on superscalar workstations. We discuss and benchmark a parallelization strategy based on the symmetric multi-processing paradigm.

A performance evaluation of TopHat RNA sequences alignment tool on openstack-based cloud environments

Despite its great promises, current Cloud offering has not been fully exploited for the management of Next-Generation Sequencing technologies. In fact, while dynamic resource allocation is typically required to ensure efficient and effective usage of the Cloud resources, Cloud providers have to deal with complex services, usually treated as black-boxes; hence, the estimation of the maximum number of resources that could improve service execution is a big challenge. This paper proposes and explores the benefits of Cloud deployment when operating a processor-hungry RNA alignment tool. The goal is to show the advantages of the virtualized and Cloud-aware approach compared to a typical bare-metal deployment. Extensive results demonstrate that our approach is as a viable first step toward easing the deployment and improving run-time service scaling.

Search of Molecular Ground State via Genetic Algorithm; Implementation on a Hybrid SIMD+MIMD Platform

A genetic algorithm for ground-state structure optimization of a Palladium atomic cluster has been developed and ported on a SIMDMIMD parallel platform. The SIMD part of the parallel platform is represented by a Quadrics/APE100 consisting of 512 floating point units while the MIMD part is formed by a cluster of workstations. The proposed algorithm contains a part where the genetic operators are applied to the elements of the population and a part which performs a further local relaxation and the fitness calculation via Molecular Dynamics. These parts have been implemented on the MIMD part and on the SIMD one, respectively. Results have been compared to those generated by using a Simulated Annealing technique.

The Pricing of Risk Rate Derivatives and the Implications for Risk Manager

The volume and complexity of financial derivatives traded over the counter (OTC) showed in the last two decades a increasing trend. In this context, the interest rate derivatives have a great growth because they are widely used in speculation, arbitrage and hedging strategies. But when the risk rate sensitive derivatives are not a plain vanilla derivatives is not very simple to price them. The second/third generation of interest rate derivatives have, in fact, a complex payoff and a risk profile difficult to quantify. The pricing of risk rate sensitive derivatives of the second/third generation require the use of models able to describe the possible evolution of the term structure of interest rates, during the term of the contract (Short Rate Model and Market Model). In this perspective, the paper: examine the characteristics and limits of applicability of different models of the Short Rate framework for the pricing of interest rate derivatives [Black, 1973, Hull J.C and White 1990]; analyze the theoretical framework of the Market Model and related techniques used to calibrate the model parameters to market data [Stacey A., Joshi 2007,Damiano Brigo, Fabio Mercurio, 2001/2006; Rebonato 2003, Daniel J. Stapleton and Richard C. Stapleton 2003; A. Brace, D. Gatarek, Musiela M. 1996]; analyze, finally, the implications for risk managers involved in managing a interest rate derivatives portfolio.

Silicon Self-Interstitial Clusters

A tight-binding molecular dynamics investigation on the structure and energetics of self-interstitial clusters in silicon is presented. The authors discuss how a small number of self-interstitial atoms give rise to the formation of tetrahedrally-shaped clusters, while a larger number of defects exhibit a self-organization mechanism driving the system to form rod-like defects.

Large Scale Atomistic Simulations using the Tight Binding Approach

Atomistic modelling of Materials Science problems often requires the simulation of systems with an irreducibly-large unit cell, such as amorphous materials, fullerites, or systems containing extended defects, such as dislocations, cracks or grain boundaries. Large-scale simulations with the Tight-Binding approach must face the computational obstacle represented by the O( N 3 )-scaling of the diagonalization of the Hamiltonian matrix. This bottleneck can be overcome by parallel computing techniques and/or the introduction of faster, O( N )-scaling algorithms. We report the activities performed in the frame of a collaboration among several research groups on the porting of TBMD codes on parallel computers. In particular, we describe the porting of a O( N 3 ) TBMD code on different MIMD computers, with either distributed or shared memory, by using appropriate software tools. Furthermore, preliminary results obtained in the porting of an O( N ) TBMD code on an experimental, hybrid MIMD-SIMD computer architecture are reported. The new perspective of using specialized platforms to deal with large-scale TBMD simulation is discussed.