12,690 research outputs found
NASA Thesaurus supplement: A four part cumulative supplement to the 1988 edition of the NASA Thesaurus (supplement 3)
The four-part cumulative supplement to the 1988 edition of the NASA Thesaurus includes the Hierarchical Listing (Part 1), Access Vocabulary (Part 2), Definitions (Part 3), and Changes (Part 4). The semiannual supplement gives complete hierarchies and accepted upper/lowercase forms for new terms
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
GPUs as Storage System Accelerators
Massively multicore processors, such as Graphics Processing Units (GPUs),
provide, at a comparable price, a one order of magnitude higher peak
performance than traditional CPUs. This drop in the cost of computation, as any
order-of-magnitude drop in the cost per unit of performance for a class of
system components, triggers the opportunity to redesign systems and to explore
new ways to engineer them to recalibrate the cost-to-performance relation. This
project explores the feasibility of harnessing GPUs' computational power to
improve the performance, reliability, or security of distributed storage
systems. In this context, we present the design of a storage system prototype
that uses GPU offloading to accelerate a number of computationally intensive
primitives based on hashing, and introduce techniques to efficiently leverage
the processing power of GPUs. We evaluate the performance of this prototype
under two configurations: as a content addressable storage system that
facilitates online similarity detection between successive versions of the same
file and as a traditional system that uses hashing to preserve data integrity.
Further, we evaluate the impact of offloading to the GPU on competing
applications' performance. Our results show that this technique can bring
tangible performance gains without negatively impacting the performance of
concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201
Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU
Monte Carlo simulations of the Ising model play an important role in the
field of computational statistical physics, and they have revealed many
properties of the model over the past few decades. However, the effect of
frustration due to random disorder, in particular the possible spin glass
phase, remains a crucial but poorly understood problem. One of the obstacles in
the Monte Carlo simulation of random frustrated systems is their long
relaxation time making an efficient parallel implementation on state-of-the-art
computation platforms highly desirable. The Graphics Processing Unit (GPU) is
such a platform that provides an opportunity to significantly enhance the
computational performance and thus gain new insight into this problem. In this
paper, we present optimization and tuning approaches for the CUDA
implementation of the spin glass simulation on GPUs. We discuss the integration
of various design alternatives, such as GPU kernel construction with minimal
communication, memory tiling, and look-up tables. We present a binary data
format, Compact Asynchronous Multispin Coding (CAMSC), which provides an
additional speedup compared with the traditionally used Asynchronous
Multispin Coding (AMSC). Our overall design sustains a performance of 33.5
picoseconds per spin flip attempt for simulating the three-dimensional
Edwards-Anderson model with parallel tempering, which significantly improves
the performance over existing GPU implementations.Comment: 15 pages, 18 figure
Including the workload effect in the parallel program signature
Performance prediction and application behavior modeling have been the subject of exten- sive research that aim to estimate applications performance with an acceptable precision. A novel approach to predict the performance of parallel applications is based in the con- cept of Parallel Application Signatures that consists in extract an application most relevant parts (phases) and the number of times they repeat (weights). Executing these phases in a target machine and multiplying its exeuction time by its weight an estimation of the application total execution time can be made. One of the problems is that the performance of an application depends on the program workload. Every type of workload affects differently how an application performs in a given system and so affects the signature execution time. Since the workloads used in most scientific parallel applications have dimensions and data ranges well known and the behavior of these applications are mostly deterministic, a model of how the programs workload affect its performance can be obtained. We create a new methodology to model how a program's workload affect the parallel application signature. Using regression analysis we are able to generalize each phase time execution and weight function to predict an application performance in a target system for any type of workload within predefined range. We validate our methodology using a synthetic program, benchmarks applications and well known real scientific applications.La predicción del rendimiento y el modelado del comportamiento de las aplicaciones son tópicos ampliamente estudiados y se cuentan con numerosos trabajos de investigación que pretenden estimar el rendimiento de la aplicaciones con una precisión aceptable. Un nuevo enfoque para predecir el rendimiento de aplicaciones paralelas es el basado en el concepto de las firmas de aplicaciones paralelas que consiste en extraer las partes mas relevantes de una aplicación (fases) y el número de veces que se repiten (pesos). Ejecutando estas fases en una máquina destino y multiplicando su tiempo de ejecución por su peso, se puede obtener una estimación del tiempo total de ejecución de la aplicación. Uno de los problemas es que el rendimiento de una aplicación depende de la carga de trabajo de esta. Cada tipo de carga de trabajo afecta de manera distinta el rendimiento que tiene una aplicación en un sistema determinado y por lo tanto el tiempo de ejecución de la firma. Dado que las cargas de trabajo de la mayoría de las aplicaciones científicas paralelas, tienen dimensiones y rango de datos bien conocidos y que el comportamiento de estas aplicaciones es generalmente determinista, se puede obtener un modelo de cómo la carga de trabajo de un programa afecta su rendimiento. Hemos creado una nueva metodología para modelar cómo la carga de trabajo de un programa afecta a la firma de la aplicación paralela. Usando análisis de regresión, hemos podido generalizar las funciones de tiempo de ejecución y peso para cada fase para predecir el rendimiento de una aplicación en un sistema destino para cualquier tipo de carga de trabajo dentro de un rango predefinido. Hemos validado nuestra metodología utilizando un programa sintético, aplicaciones de benchmarks y aplicaciones reales científicas bien conocidas
Black-hole binaries, gravitational waves, and numerical relativity
Understanding the predictions of general relativity for the dynamical
interactions of two black holes has been a long-standing unsolved problem in
theoretical physics. Black-hole mergers are monumental astrophysical events,
releasing tremendous amounts of energy in the form of gravitational radiation,
and are key sources for both ground- and space-based gravitational-wave
detectors. The black-hole merger dynamics and the resulting gravitational
waveforms can only be calculated through numerical simulations of Einstein's
equations of general relativity. For many years, numerical relativists
attempting to model these mergers encountered a host of problems, causing their
codes to crash after just a fraction of a binary orbit could be simulated.
Recently, however, a series of dramatic advances in numerical relativity has
allowed stable, robust black-hole merger simulations. This remarkable progress
in the rapidly maturing field of numerical relativity, and the new
understanding of black-hole binary dynamics that is emerging is chronicled.
Important applications of these fundamental physics results to astrophysics, to
gravitational-wave astronomy, and in other areas are also discussed.Comment: 54 pages, 42 figures. Some typos corrected & references updated.
Essentially final published versio
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