Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

HIGH PERFORMANCE AGENT-BASED MODELS WITH REAL-TIME IN SITU VISUALIZATION OF INFLAMMATORY AND HEALING RESPONSES IN INJURED VOCAL FOLDS

The introduction of clusters of multi-core and many-core processors has played a major role in recent advances in tackling a wide range of new challenging applications and in enabling new frontiers in BigData. However, as the computing power increases, the programming complexity to take optimal advantage of the machine's resources has significantly increased. High-performance computing (HPC) techniques are crucial in realizing the full potential of parallel computing. This research is an interdisciplinary effort focusing on two major directions. The first involves the introduction of HPC techniques to substantially improve the performance of complex biological agent-based models (ABM) simulations, more specifically simulations that are related to the inflammatory and healing responses of vocal folds at the physiological scale in mammals. The second direction involves improvements and extensions of the existing state-of-the-art vocal fold repair models. These improvements and extensions include comprehensive visualization of large data sets generated by the model and a significant increase in user-simulation interactivity. We developed a highly-interactive remote simulation and visualization framework for vocal fold (VF) agent-based modeling (ABM). The 3D VF ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed. The physiologically representative human VF ABM consisted of more than 15 million mobile biological cells. The model maintained and generated 1.7 billion signaling and extracellular matrix (ECM) protein data points in each iteration. The VF ABM employed HPC techniques to optimize its performance by concurrently utilizing the power of multi-core CPU and multiple GPUs. The optimization techniques included the minimization of data transfer between the CPU host and the rendering GPU. These transfer minimization techniques also reduced transfers between peer GPUs in multi-GPU setups. The data transfer minimization techniques were executed with a scheduling scheme that aims to achieve load balancing, maximum overlap of computation and communication, and a high degree of interactivity. This scheduling scheme achieved optimal interactivity by hyper-tasking the available GPUs (GHT). In comparison to the original serial implementation on a popular ABM framework, NetLogo, these schemes have shown substantial performance improvements of 400x and 800x for the 2D and 3D model, respectively. Furthermore, the combination of data footprint and data transfer reduction techniques with GHT achieved high-interactivity visualization with an average framerate of 42.8 fps. This performance enabled the users to perform real-time data exploration on large simulated outputs and steer the course of their simulation as needed

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

FLAME GPU 2: a framework for flexible and performant agent based simulation on GPUs

Agent based modelling (ABM) offers a powerful abstraction for scientific study in a broad range of domains. The use of agent based simulators encourages good software engineering design such as separation of concerns, that is, the uncoupling of the model description from its implementation detail. A major limitation in current approaches to ABM simulation is that of the trade off between simulator flexibility and performance. It is common that highly optimised simulations, such as those which target graphics processing units (GPU) hardware, are implemented as standalone software. This work presents a software framework (FLAME GPU 2) which balances flexibility with performance for general purpose ABM. Methods for ensuring high computational efficacy are demonstrated by, minimising data movement, and ensuring high device utilisation by exploiting opportunities for concurrent code execution within a model and through the use of ensembles of simulations. A novel hierarchical sub-modelling approach is also presented which can be used to model certain types of recursive behaviours. This feature is shown to be essential in providing a mechanism to resolve competition for resources between agents within a parallel environment which would otherwise introduce race conditions. To understand the performance characteristics of the software, a benchmark model with millions of agents is used to explore the use of simulation ensembles and to parametrically investigate concurrent code execution within a model. Performance speedups are demonstrated of 3.5 and 10 respectively over a baseline GPU implementation. Our hierarchical sub-modelling approach is used to demonstrate the implementation of a recursive algorithm to resolve competition of agent movement which occurs as a result of agent desire to simultaneously occupy discrete areas high in a ‘resource’. The algorithm is used to implement a classical socio-economics model, Sugarscape, with populations of up to 16M agents