Efficiently Computing {phi}-Nodes On-The-Fly

Recently, Static Single Assignment Form and Sparse Evaluation Graphs have been advanced for the efficient solution of program optimization problems. Each method is provided with an initial set of flow graph nodes that inherently affect a problem\u27s solution. Other relevant nodes are those where potentially disparate solutions must combine. Previously, these so-called {phi}-nodes were found by computing the iterated dominance frontiers of the initial set of nodes, a process that could take worst case quadratic time with respect to the input flow graph. In this paper we present an almost-linear algorithm for detemining exactly the same set of {phi}-nodes

Astrocomp: a web service for the use of high performance computers in Astrophysics

Astrocomp is a joint project, developed by the INAF-Astrophysical Observatory of Catania, University of Roma La Sapienza and Enea. The project has the goal of providing the scientific community of a web-based user-friendly interface which allows running parallel codes on a set of high-performance computing (HPC) resources, without any need for specific knowledge about parallel programming and Operating Systems commands. Astrocomp provides, also, computing time on a set of parallel computing systems, available to the authorized user. At present, the portal makes a few codes available, among which: FLY, a cosmological code for studying three-dimensional collisionless self-gravitating systems with periodic boundary conditions; ATD, a parallel tree-code for the simulation of the dynamics of boundary-free collisional and collisionless self-gravitating systems and MARA, a code for stellar light curves analysis. Other codes are going to be added to the portal.Comment: LaTeX with elsart.cls and harvard.sty (included). 7 pages. To be submitted to a specific journa

On-the-fly ab initio semiclassical evaluation of time-resolved electronic spectra

We present a methodology for computing vibrationally and time-resolved pump-probe spectra, which takes into account all vibrational degrees of freedom and is based on the combination of the thawed Gaussian approximation with on-the-fly ab initio evaluation of the electronic structure. The method is applied to the phenyl radical and compared with two more approximate approaches based on the global harmonic approximation - the global harmonic method expands both the ground- and excited-state potential energy surfaces to the second order about the corresponding minima, while the combined global harmonic/on-the-fly method retains the on-the-fly scheme for the excited-state wavepacket propagation. We also compare the spectra by considering their means and widths, and show analytically how these measures are related to the properties of the semiclassical wavepacket. We find that the combined approach is better than the global harmonic one in describing the vibrational structure, while the global harmonic approximation estimates better the overall means and widths of the spectra due to a partial cancellation of errors. Although the full-dimensional on-the-fly ab initio result seems to reflect the dynamics of only one mode, we show, by performing exact quantum calculations, that this simple structure cannot be recovered using a one-dimensional model. Yet, the agreement between the quantum and semiclassical spectra in this simple, but anharmonic model lends additional support for the full-dimensional ab initio thawed Gaussian calculation of the phenyl radical spectra. We conclude that the thawed Gaussian approximation provides a viable alternative to the expensive or unfeasible exact quantum calculations in cases, where low-dimensional models are not sufficiently accurate to represent the full system.Comment: Last 6 pages contain the Supplementary Materia

Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.Comment: This paper is published on IJCAI 2017. https://www.ijcai.org/proceedings/2017/64