4,542 research outputs found
Mixing multi-core CPUs and GPUs for scientific simulation software
Recent technological and economic developments have led to widespread availability of
multi-core CPUs and specialist accelerator processors such as graphical processing units
(GPUs). The accelerated computational performance possible from these devices can be very
high for some applications paradigms. Software languages and systems such as NVIDIA's
CUDA and Khronos consortium's open compute language (OpenCL) support a number of
individual parallel application programming paradigms. To scale up the performance of some
complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and
very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica-
tions using threading approaches and multi-core CPUs to control independent GPU devices.
We present speed-up data and discuss multi-threading software issues for the applications
level programmer and o er some suggested areas for language development and integration
between coarse-grained and ne-grained multi-thread systems. We discuss results from three
common simulation algorithmic areas including: partial di erential equations; graph cluster
metric calculations and random number generation. We report on programming experiences
and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs;
a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and
trends in multi-core programming for scienti c applications developers
MECA: A Multi-agent Environment for Cognitive Agents
Many fully functional multi-agent systems have been developed and put to use over the past twenty years, but few of them have been developed to succesfully facilitate social research through the use of social agents. There are three important difficulties that must be dealt with to successfully create a social system for use in social research. First, the system must have an adaptable agent framework that can successfully make intuitive and deliberative decisions much like a human participant would. Secondly, the system must have a robust architecture that not only ensures its functioning no matter the simulation, but also provides an easily understood interface that researchers can interact with while running their simulations. Finally, the system must be effectively distributed to handle the necessary number of agents that social research requires to obtain meaningful results. This paper presents our work on creating a multi-agent simulation for social agents that overcomes these three difficulties
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
The precise modeling of subatomic particle interactions and propagation
through matter is paramount for the advancement of nuclear and particle physics
searches and precision measurements. The most computationally expensive step in
the simulation pipeline of a typical experiment at the Large Hadron Collider
(LHC) is the detailed modeling of the full complexity of physics processes that
govern the motion and evolution of particle showers inside calorimeters. We
introduce \textsc{CaloGAN}, a new fast simulation technique based on generative
adversarial networks (GANs). We apply these neural networks to the modeling of
electromagnetic showers in a longitudinally segmented calorimeter, and achieve
speedup factors comparable to or better than existing full simulation
techniques on CPU (-) and even faster on GPU (up to
). There are still challenges for achieving precision across
the entire phase space, but our solution can reproduce a variety of geometric
shower shape properties of photons, positrons and charged pions. This
represents a significant stepping stone toward a full neural network-based
detector simulation that could save significant computing time and enable many
analyses now and in the future.Comment: 14 pages, 4 tables, 13 figures; version accepted by Physical Review D
(PRD
Event-Driven Network Model for Space Mission Optimization with High-Thrust and Low-Thrust Spacecraft
Numerous high-thrust and low-thrust space propulsion technologies have been
developed in the recent years with the goal of expanding space exploration
capabilities; however, designing and optimizing a multi-mission campaign with
both high-thrust and low-thrust propulsion options are challenging due to the
coupling between logistics mission design and trajectory evaluation.
Specifically, this computational burden arises because the deliverable mass
fraction (i.e., final-to-initial mass ratio) and time of flight for low-thrust
trajectories can can vary with the payload mass; thus, these trajectory metrics
cannot be evaluated separately from the campaign-level mission design. To
tackle this challenge, this paper develops a novel event-driven space logistics
network optimization approach using mixed-integer linear programming for space
campaign design. An example case of optimally designing a cislunar propellant
supply chain to support multiple lunar surface access missions is used to
demonstrate this new space logistics framework. The results are compared with
an existing stochastic combinatorial formulation developed for incorporating
low-thrust propulsion into space logistics design; our new approach provides
superior results in terms of cost as well as utilization of the vehicle fleet.
The event-driven space logistics network optimization method developed in this
paper can trade off cost, time, and technology in an automated manner to
optimally design space mission campaigns.Comment: 38 pages; 11 figures; Journal of Spacecraft and Rockets (Accepted);
previous version presented at the AAS/AIAA Astrodynamics Specialist
Conference, 201
Parallelizing Navier-Stokes Computations on a Variety of Architectural Platforms
We study the computational, communication, and scalability characteristics of a Computational Fluid Dynamics application, which solves the time accurate flow field of a jet using the compressible Navier-Stokes equations, on a variety of parallel architectural platforms. The platforms chosen for this study are a cluster of workstations (the LACE experimental testbed at NASA Lewis), a shared memory multiprocessor (the Cray YMP), distributed memory multiprocessors with different topologies-the IBM SP and the Cray T3D. We investigate the impact of various networks, connecting the cluster of workstations, on the performance of the application and the overheads induced by popular message passing libraries used for parallelization. The work also highlights the importance of matching the memory bandwidth to the processor speed for good single processor performance. By studying the performance of an application on a variety of architectures, we are able to point out the strengths and weaknesses of each of the example computing platforms
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Artificial intelligence (AI) is rapidly emerging as an enabling tool for
solving various complex materials design problems. This paper aims to review
recent advances in AI-driven materials-by-design and their applications to
energetic materials (EM). Trained with data from numerical simulations and/or
physical experiments, AI models can assimilate trends and patterns within the
design parameter space, identify optimal material designs (micro-morphologies,
combinations of materials in composites, etc.), and point to designs with
superior/targeted property and performance metrics. We review approaches
focusing on such capabilities with respect to the three main stages of
materials-by-design, namely representation learning of microstructure
morphology (i.e., shape descriptors), structure-property-performance (S-P-P)
linkage estimation, and optimization/design exploration. We provide a
perspective view of these methods in terms of their potential, practicality,
and efficacy towards the realization of materials-by-design. Specifically,
methods in the literature are evaluated in terms of their capacity to learn
from a small/limited number of data, computational complexity,
generalizability/scalability to other material species and operating
conditions, interpretability of the model predictions, and the burden of
supervision/data annotation. Finally, we suggest a few promising future
research directions for EM materials-by-design, such as meta-learning, active
learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge
the gap between machine learning research and EM research
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