Efficient execution of electronic structure calculations on SMP clusters

Applications augmented with adaptive capabilities are becoming common in parallel computing environments. For large-scale scientific applications, dynamic adjustments to a computationally-intensive part may lead to a large pay-off in facilitating efficient execution of the entire application while aiming at avoiding resource contention. Application-specific knowledge, often best revealed during the run-time, is required to initiate and time these adjustments. In particular, General Atomic and Molecular Electronic Structure System (GAMESS) is a program for ab initio quantum chemistry that places significant demands on the high-performance computing platforms. Certain electronic structure calculations are characterized by high consumption of a particular resource, such as CPU, main memory, or disk I/O. This may lead to resource contention among concurrent GAMESS jobs and other programs in the dynamically changing environment. Thus, it is desirable to improve GAMESS calculations by means of dynamic adaptations. In this thesis, we show how an application- or algorithm-specific knowledge may play a significant role in achieving this goal. The choice of implementation is facilitated by a module-driven middleware easily integrated with GAMESS that assesses resource consumption and invokes GAMESS adaptations to the system environment. We show that the throughput of GAMESS jobs may be improved greatly as a result of such adaptations

Data-Driven Wildfire Propagation Modeling with FARSITE-EnKF

The goal of this study is to provide a framework for future researchers to understand and use the FARSITE wildfire-forecasting model with data assimilation. Current wildfire models lack the ability to provide accurate prediction of fire front position faster than real-time. When FARSITE is coupled with a recursive ensemble filter, the data assimilation forecast method improves. The scope includes an explanation of the standalone FARSITE application, technical details on FARSITE integration with a parallel program coupler called OpenPALM, and a model demonstration of the FARSITE-Ensemble Kalman Filter software using the FireFlux I experiment by Craig Clements. The results show that the fire front forecast is improved with the proposed data-driven methodology than with the standalone FARSITE model

Physical chemistry of the interactions between multicomponent solvents and biomass

Extraction of cellulose and lignin from plant biomass remains a major issue for enabling more economic and green production of lignocellulosic renewable fuels and byproducts. Although the use of multicomponent solvents has provided remarkable results in wood fractionation processes most of the currently used methods rely on empirically elaborated protocols. Understanding the physicochemical mechanisms of biomass breakdown and its interactions with solvent medium during fractionation will lead to more efficient use of biomass. This defined the focus in this thesis work on a systematic and detailed description of the interactions between ligoncellulose components with binary water-organic mixtures of ethanol and acetonitrile. Our results and their analysis were obtained predominantly with molecular dynamics (MD) simulations, and supported by additional studies of quantum chemical (Density Functional Theory) and mixed quantum mechanical (QM) and classical MD scheme (QM/MM). With these tools we first established a non-linear behavior of the mixed solvent structures, thermodynamic properties and dynamic hardness, as a measure for their global reactivity. The analysis of the average numbers of HBs with the liquid composition shows that alcohol molecules tend to substitute water molecules, allowing compensating for the loss of H-bonds in the water solvent domains. The role of organic component in water solvent mixtures on the conformational changes induced in the main wood components (cellulose, lignin and hemicellulose) is highlighted and their dependence on distinct solvent compositions is unveiled for each organic solvent component and its content in water. This dependence is explained by preferential solute-solvent interatomic interactions as a function of solvent compositions. Subsequently, the evolution of interaction forces in lignin-cellulose and lignin-xylan complexes are also found to have solvent-dependent profiles. All this supports the general conclusion about specific solvent actions on lignocellulose compounds being the driving factors in the observed macroscopic non-linear behavior in wood swelling in mixed water-organics

Using Numerical Relativity to Explore Strong Gravity and Develop Force-Free Electrodynamics Simulation Software with Best-Practice Development

In this dissertation, we explore the effects of extremely strong gravitational and electro- dynamic fields using the techniques of numerical relativity. We use the existing black hole simulation software in the Einstein Toolkit to compute the spin-up of two initially nonspin- ning black holes as they pass by each other in space. The angular momentum is imparted by the tidal interaction between the two black holes, in a parallel to Earth’s tides, as described by classical mechanics, which also transfer angular momentum between the Earth’s rotation and the Moon’s orbit. The largest observed dimensionless spin observed was 0.20 with an initial boost of 0.78c, and we conclude that higher spin-ups may be possible with larger initial boosts. We also use NRPy+ to generate C code for new simulations. The first two are written as thorns, or modules, for the Einstein Toolkit (ETK). The first is a simple scalar wave solver, which has proven useful as an introduction to NRPy+ for new users who wish to write their own ETK thorns. The second is an open-source alternative to the Kranc-generated WeylScal4, which calculates the Weyl scalars that are needed to extract gravitational wave information from a simulation. We also port GiRaFFE to NRPy+. While this was originally intended to be another ETK thorn, as the project progressed, it ultimately became a stan- dalone simulation. The individual modules of this code also have their own unit tests as an additional validation step. These unit tests also use continuous integration to ensure that bugs are not unknowingly introduced into the code. In the future, this code will be modified to be able to use arbitrary coordinate systems

Algorithmic skeletons for exact combinatorial search at scale

Exact combinatorial search is essential to a wide range of application areas including constraint optimisation, graph matching, and computer algebra. Solutions to combinatorial problems are found by systematically exploring a search space, either to enumerate solutions, determine if a specific solution exists, or to find an optimal solution. Combinatorial searches are computationally hard both in theory and practice, and efficiently exploring the huge number of combinations is a real challenge, often addressed using approximate search algorithms. Alternatively, exact search can be parallelised to reduce execution time. However, parallel search is challenging due to both highly irregular search trees and sensitivity to search order, leading to anomalies that can cause unexpected speedups and slowdowns. As core counts continue to grow, parallel search becomes increasingly useful for improving the performance of existing searches, and allowing larger instances to be solved. A high-level approach to parallel search allows non-expert users to benefit from increasing core counts. Algorithmic Skeletons provide reusable implementations of common parallelism patterns that are parameterised with user code which determines the specific computation, e.g. a particular search. We define a set of skeletons for exact search, requiring the user to provide in the minimal case a single class that specifies how the search tree is generated and a parameter that specifies the type of search required. The five are: Sequential search; three general-purpose parallel search methods: Depth-Bounded, Stack-Stealing, and Budget; and a specific parallel search method, Ordered, that guarantees replicable performance. We implement and evaluate the skeletons in a new C++ parallel search framework, YewPar. YewPar provides both high-level skeletons and low-level search specific schedulers and utilities to deal with the irregularity of search and knowledge exchange between workers. YewPar is based on the HPX library for distributed task-parallelism potentially allowing search to execute on multi-cores, clusters, cloud, and high performance computing systems. Underpinning the skeleton design is a novel formal model, MT^3 , a parallel operational semantics that describes multi-threaded tree traversals, allowing reasoning about parallel search, e.g. describing common parallel search phenomena such as performance anomalies. YewPar is evaluated using seven different search applications (and over 25 specific instances): Maximum Clique, k-Clique, Subgraph Isomorphism, Travelling Salesperson, Binary Knapsack, Enumerating Numerical Semigroups, and the Unbalanced Tree Search Benchmark. The search instances are evaluated at multiple scales from 1 to 255 workers, on a 17 host, 272 core Beowulf cluster. The overheads of the skeletons are low, with a mean 6.1% slowdown compared to hand-coded sequential implementation. Crucially, for all search applications YewPar reduces search times by an order of magnitude, i.e hours/minutes to minutes/seconds, and we commonly see greater than 60% (average) parallel efficiency speedups for up to 255 workers. Comparing skeleton performance reveals that no one skeleton is best for all searches, highlighting a benefit of a skeleton approach that allows multiple parallelisations to be explored with minimal refactoring. The Ordered skeleton avoids slowdown anomalies where, due to search knowledge being order dependent, a parallel search takes longer than a sequential search. Analysis of Ordered shows that, while being 41% slower on average (73% worse-case) than Depth-Bounded, in nearly all cases it maintains the following replicable performance properties: 1) parallel executions are no slower than one worker sequential executions 2) runtimes do not increase as workers are added, and 3) variance between repeated runs is low. In particular, where Ordered maintains a relative standard deviation (RSD) of less than 15%, Depth-Bounded suffers from an RSD greater than 50%, showing the importance of carefully controlling search orders for repeatability

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI