225 research outputs found
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Lattice Quantum Chromodynamics on Intel Xeon Phi based supercomputers
Preface
The aim of this master\u2019s thesis project was to expand the QPhiX library for twisted-mass fermions with and without clover term. To this end, I continued work initiated by Mario Schr\uf6ck et al. [63]. In writing this thesis, I was following two main goals. Firstly, I wanted to stress the intricate interplay of the four pillars of High Performance Computing: Algorithms, Hardware, Software and Performance Evaluation. Surely, algorithmic development is utterly important in Scientific Computing, in particular in LQCD, where it even outweighed the improvements made in Hardware architecture in the last decade\u2014cf. the section about computational costs of LQCD. It is strongly influenced by the available hardware\u2014think of the advent of parallel algorithms\u2014but in turn also influenced the design of hardware itself. The IBM BlueGene series is only one of many examples in LQCD. Furthermore, there will be no benefit from the best algorithms, when one cannot implement the ideas into correct, performant, user-friendly, read- and maintainable (sometimes over several decades) software code. But again, truly outstanding HPC software cannot be written without a profound knowledge of its target hardware. Lastly, an HPC software architect and computational scientist has to be able to evaluate and benchmark the performance of a software program, in the often very heterogeneous environment of supercomputers with multiple software and hardware
layers. My second goal in writing this thesis was to produce a self-contained introduction into the computational aspects of LQCD and in particular, to the features of QPhiX, so the reader would be able to compile, read and understand the code of one truly amazing pearl of HPC [40]. It is a pleasure to thank S. Cozzini, R. Frezzotti, E. Gregory, B. Jo\uf3, B. Kostrzewa, S. Krieg,
T. Luu, G. Martinelli, R. Percacci, S. Simula, M. Ueding, C. Urbach, M. Werner, the Intel company for providing me with a copy of [55], and the J\ufclich Supercomputing Center for granting me access to their KNL test cluster DEE
Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms
EXCESS deliverable D2.3. More information at http://www.excess-project.eu/This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of Task 2.1 on investigating andmodeling the trade-off between energy and performance of concurrent data structuresand algorithms. The work has been conducted on two main EXCESS platforms: Intelplatforms with recent Intel multicore CPUs and Movidius Myriad platforms
PyPWA: A Software Toolkit for Parameter Optimization and Amplitude Analysis
PyPWA is a toolkit designed to optimize parametric models describing data and
generate simulated distributions according to a model. Its software has been
written within the python ecosystem with the goal of performing Amplitude or
Partial Wave Analysis (PWA) in nuclear and particle physics experiments. We
briefly describe the general features of amplitude analysis and the PyPWA
software design and usage. We provide benchmarks of the scaling and an example
of its application
Proceedings, MSVSCC 2014
Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia
Deep Model for Improved Operator Function State Assessment
A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation
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
Fast algorithm for real-time rings reconstruction
The GAP project is dedicated to study the application of GPU in several contexts in which
real-time response is important to take decisions. The definition of real-time depends on
the application under study, ranging from answer time of ÎĽs up to several hours in case
of very computing intensive task. During this conference we presented our work in low
level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and
specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6].
Apart from the study of dedicated solution to decrease the latency due to data transport
and preparation, the computing algorithms play an essential role in any GPU application.
In this contribution, we show an original algorithm developed for triggers application, to
accelerate the ring reconstruction in RICH detector when it is not possible to have seeds
for reconstruction from external trackers
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