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ردیف | عنوان | نوع |
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1 |
Diagnostic Performance of a Novel Method for Fractional Flow Reserve Computed from Noninvasive Computed Tomography Angiography (NOVEL-FLOW Study)
کارایی تشخیصی یک روش جدید برای ذخایر جریان فراکشنال محاسبه شده از آنژیوگرافی توموگرافی کامپیوتری غیر تهاجمی (مطالعه NOVEL-FLOW)-2017 Coronary computed tomography angiography (CCTA)-derived fractional flow reserve from
computed tomography (CT-FFR) may provide better diagnostic performance over CCTA
alone, but the complexity of its method limits the use in clinical environment. The aim of the
present study is to validate a newly developed vessel-length based computational fluid
dynamics scheme for the computation of FFR based on CCTA data, compare them with
invasively measured FFR, and evaluate its diagnostic performance with that of CCTA. One
hundred seventeen patients from 4 medical institutions who had clinically indicated
invasive coronary angiography for suspected coronary artery disease (CAD) were enrolled.
Invasive FFR measurement was performed in 218 vessels and these measurements were
regarded as the reference standard. The accuracy, sensitivity, specificity, positive predictive
value, and negative predictive value of CT-FFR on a per-vessel basis were 85.8%, 86.2%,
85.5%, 79.8%, and 90.3%, respectively, for CT-FFR £0.80, and 66.1%, 75.9%, 59.5%,
55.5%, and 78.8%, respectively, for CCTA ‡50%. A higher area under the receiver operating characteristic curve for CT-FFR was observed compared with CCTA (0.93 vs 0.74,
p <0.0001). The CT-FFR and FFR correlated well (r [ 0.76, p <0.001) with slight
underestimation by CT-FFR (0.014 – 0.077, p [ 0.007). With a novel method of vessellength based computational fluid dynamics scheme, CT-FFR can be performed at a
personal computer enhancing its applicability in clinical situation. The diagnostic accuracy
of CT-FFR for the detection of functionally significant CAD was good and was superior to
that of CCTA within a population of suspected CAD. 2017 Elsevier Inc. All rights
reserved. (Am J Cardiol 2017;120:362e368)
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مقاله انگلیسی |
2 |
MPARD: A high-frequency wave-based acoustic solver for very large compute clusters
MPARD: یک حل کننده صوتی مبتنی بر موج فرکانس بالا برای خوشه های محاسباتی بسیار بزرگ-2017 We present a parallel time-domain wave solver designed for large and high frequency acoustic domains.
Our approach is based on a novel scalable method for dividing acoustic field computations specifically for
large-scale distributed memory clusters using parallel Adaptive Rectangular Decomposition (ARD).
In order to efficiently compute the acoustic field for large or high frequency domains, we need to take
full advantage of the compute resources of large clusters. This is done with new algorithmic contribu
tions, including a hypergraph partitioning scheme to reduce the communication cost between the cores
on the cluster, a novel domain decomposition scheme that reduces the amount of numerical dispersion
error introduced by the load balancing algorithm, and a revamped pipeline for parallel ARD computation
that increases memory efficiency and reduces redundant computations.
Our resulting parallel algorithm makes it possible to compute the sound pressure field for high fre
quencies in large environments that are thousands of cubic meters in volume. We highlight the perfor
mance of our system on large clusters with 16,000 cores on homogeneous indoor and outdoor
benchmarks up to 10 kHz. To the best of our knowledge, this is the first time-domain parallel acoustic
wave solver that can handle such large domains and frequencies.
Keywords: Large-scale | Wave-based methods | Massively parallel |
مقاله انگلیسی |
3 |
Automatic construction of parallel portfolios via algorithm configuration
ساخت اتوماتیک اوراق بهادار موازی از طریق پیکربندی الگوریتم-2017 Since 2004, increases in computational power described by Moore’s law have substantially
been realized in the form of additional cores rather than through faster clock speeds. To
make effective use of modern hardware when solving hard computational problems, it is
therefore necessary to employ parallel solution strategies. In this work, we demonstrate
how effective parallel solvers for propositional satisfiability (SAT), one of the most
widely studied NP-complete problems, can be produced automatically from any existing
sequential, highly parametric SAT solver. Our Automatic Construction of Parallel Portfolios
(ACPP) approach uses an automatic algorithm configuration procedure to identify a set of
configurations that perform well when executed in parallel. Applied to two prominent SAT
solvers, Lingeling and clasp, our ACPP procedure identified 8-core solvers that significantly
outperformed their sequential counterparts on a diverse set of instances from the
application and hard combinatorial category of the 2012 SAT Challenge. We further extended
our ACPP approach to produce parallel portfolio solvers consisting of several different
solvers by combining their configuration spaces. Applied to the component solvers of
the 2012 SAT Challenge gold medal winning SAT Solver pfolioUZK, our ACPP procedures
produced a significantly better-performing parallel SAT solver.
Keywords: Algorithm configuration | Parallel SAT solving | Algorithm portfolios | Programming by optimization | Automated parallelization |
مقاله انگلیسی |
4 |
SaaS enabled admission control for MCMC simulation in cloud computing infrastructures
SaaS کنترل پذیرش فعال برای شبیه سازی MCMC در زیرساخت های محاسبات ابری-2017 Markov Chain Monte Carlo (MCMC) methods are widely used in the field of simulation and modelling
of materials, producing applications that require a great amount of computational resources. Cloud
computing represents a seamless source for these resources in the form of HPC. However, resource over
consumption can be an important drawback, specially if the cloud provision process is not appropriately
optimized. In the present contribution we propose a two-level solution that, on one hand, takes advantage
of approximate computing for reducing the resource demand and on the other, uses admission control
policies for guaranteeing an optimal provision to running applications.
Keywords: Cloud computing | SaaS | PaaS | Admission control |
مقاله انگلیسی |
5 |
Crystal MD: The massively parallel molecular dynamics software for metal with BCC structure
Crystal MD: نرم افزار پویایی مولکولی موازی برای فلز با ساختار BCC-2017 Material irradiation effect is one of the most important keys to use nuclear power. However, the lack
of high-throughput irradiation facility and knowledge of evolution process, lead to little understanding
of the addressed issues. With the help of high-performance computing, we could make a further
understanding of micro-level-material. In this paper, a new data structure is proposed for the massively
parallel simulation of the evolution of metal materials under irradiation environment. Based on the
proposed data structure, we developed the new molecular dynamics software named Crystal MD. The
simulation with Crystal MD achieved over 90% parallel efficiency in test cases, and it takes more than 25%
less memory on multi-core clusters than LAMMPS and IMD, which are two popular molecular dynamics
simulation software. Using Crystal MD, a two trillion particles simulation has been performed on Tianhe-2
cluster.
Keywords: Irradiation environment | Molecular dynamics | Data structure | High-performance computing |
مقاله انگلیسی |
6 |
Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads
سازگاری ژنراتور تعامل با پارتون Alpgen سریال برای شبیه سازی برخورد LHC در میلیون ها نخ موازی-2017 As the LHC moves to higher energies and luminosity, the demand for computing resources increases
accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater
demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer
at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that
is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider.
This paper details the process by which Alpgen was adapted from a single-processor serial-application to
a large-scale parallel-application and the performance that was achieved.
Keywords: Supercomputer | HEP | Simulation | Parallel |
مقاله انگلیسی |
7 |
GPU-accelerated Tersoff potentials for massively parallel Molecular Dynamics simulations
پتانسیل Tersoff برای تسریع GPU برای شبیه سازی دینامیک مولکولی موازی-2017 The Tersoff potential is one of the empirical many-body potentials that has been widely used in simulation
studies at atomic scales. Unlike pair-wise potentials, the Tersoff potential involves three-body terms,
which require much more arithmetic operations and data dependency. In this contribution, we have
implemented the GPU-accelerated version of several variants of the Tersoff potential for LAMMPS, an
open-source massively parallel Molecular Dynamics code. Compared to the existing MPI implementation
in LAMMPS, the GPU implementation exhibits a better scalability and offers a speedup of 2.2X when run
on 1000 compute nodes on the Titan supercomputer. On a single node, the speedup ranges from 2.0 to
8.0 times, depending on the number of atoms per GPU and hardware configurations. The most notable
features of our GPU-accelerated version include its design for MPI/accelerator heterogeneous parallelism,
its compatibility with other functionalities in LAMMPS, its ability to give deterministic results and to
support both NVIDIA CUDA- and OpenCL-enabled accelerators. Our implementation is now part of the
GPU package in LAMMPS and accessible for public use.
Keywords: Tersoff | LAMMPS | GPU acceleration | Hybrid MPI/GPU | High-performance computing |
مقاله انگلیسی |
8 |
Parallel multiphase field simulations with OpenPhase
شبیه سازی زمینه چند فازی موازی با OpenPhase-2017 The open-source software project OpenPhase allows the three-dimensional simulation of microstructural
evolution using the multiphase field method. The core modules of OpenPhase and their implementation
as well as their parallelization for a distributed-memory setting are presented. Especially communication
and load-balancing strategies are discussed. Synchronization points are avoided by an increased halo-size,
i.e. additional layers of ghost cells, which allow multiple stencil operations without data exchange. Load
balancing is considered via graph-partitioning and sub-domain decomposition. Results are presented
for performance benchmarks as well as for a variety of applications, e.g. grain growth in polycrystalline
materials, including a large number of phase fields as well as Mg–Al alloy solidification.
Keywords: Material science | Phase field | Parallel computing | Load-balancing |
مقاله انگلیسی |
9 |
A hybrid algorithm for parallel molecular dynamics simulations
الگوریتم ترکیبی برای شبیه سازی دینامیک مولکولی موازی-2017 This article describes algorithms for the hybrid parallelization and SIMD vectorization of molecular dy
namics simulations with short-range forces. The parallelization method combines domain decomposition
with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very
large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or
thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method,
simulations of a variety of configurations with up to 74 million atoms have been performed. Results are
shown that were obtained on multi-core systems with Sandy Bridge and Haswell processors as well as
systems with Xeon Phi many-core processors.
Keywords: Molecular dynamics | Hybrid parallelization | SIMD | Xeon Phi |
مقاله انگلیسی |
10 |
A derivation and scalable implementation of the synchronous parallel kinetic Monte Carlo method for simulating long-time dynamics
اشتقاق و مقیاس پذیر اجرای روش همزمان موازی جنبشی مونت کارلو برای شبیه سازی دینامیک طولانی مدت-2017 Kinetic Monte Carlo (KMC) simulations are used to study long-time dynamics of a wide variety of systems.
Unfortunately, the conventional KMC algorithm is not scalable to larger systems, since its time scale
is inversely proportional to the simulated system size. A promising approach to resolving this issue is
the synchronous parallel KMC (SPKMC) algorithm, which makes the time scale size-independent. This
paper introduces a formal derivation of the SPKMC algorithm based on local transition-state and time
dependent Hartree approximations, as well as its scalable parallel implementation based on a dual linked
list cell method. The resulting algorithm has achieved a weak-scaling parallel efficiency of 0.935 on 1024
Intel Xeon processors for simulating biological electron transfer dynamics in a 4.2 billion-heme system,
as well as decent strong-scaling parallel efficiency. The parallel code has been used to simulate a lattice of
cytochrome complexes on a bacterial-membrane nanowire, and it is broadly applicable to other problems
such as computational synthesis of new materials.
Keywords: Kinetic Monte Carlo simulation | Divide-and-conquer algorithm | Parallel computing |
مقاله انگلیسی |