2,444 research outputs found
Distributed N-body Simulation on the Grid Using Dedicated Hardware
We present performance measurements of direct gravitational N -body
simulation on the grid, with and without specialized (GRAPE-6) hardware. Our
inter-continental virtual organization consists of three sites, one in Tokyo,
one in Philadelphia and one in Amsterdam. We run simulations with up to 196608
particles for a variety of topologies. In many cases, high performance
simulations over the entire planet are dominated by network bandwidth rather
than latency. With this global grid of GRAPEs our calculation time remains
dominated by communication over the entire range of N, which was limited due to
the use of three sites. Increasing the number of particles will result in a
more efficient execution. Based on these timings we construct and calibrate a
model to predict the performance of our simulation on any grid infrastructure
with or without GRAPE. We apply this model to predict the simulation
performance on the Netherlands DAS-3 wide area computer. Equipping the DAS-3
with GRAPE-6Af hardware would achieve break-even between calculation and
communication at a few million particles, resulting in a compute time of just
over ten hours for 1 N -body time unit. Key words: high-performance computing,
grid, N-body simulation, performance modellingComment: (in press) New Astronomy, 24 pages, 5 figure
Iso-energy-efficiency: An approach to power-constrained parallel computation
Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making
Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance
The cubic Klein-Gordon equation is a simple but non-trivial partial
differential equation whose numerical solution has the main building blocks
required for the solution of many other partial differential equations. In this
study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve
the Klein-Gordon equation and strong scaling of the code is examined on
thirteen different machines for a problem size of 512^3. The results are useful
in assessing likely performance of other parallel fast Fourier transform based
programs for solving partial differential equations. The problem is chosen to
be large enough to solve on a workstation, yet also of interest to solve
quickly on a supercomputer, in particular for parametric studies. Unlike other
high performance computing benchmarks, for this problem size, the time to
solution will not be improved by simply building a bigger supercomputer.Comment: 10 page
Developing High Performance Computing Resources for Teaching Cluster and Grid Computing courses
High-Performance Computing (HPC) and the ability to process large amounts of data are of
paramount importance for UK business and economy as outlined by Rt Hon David Willetts
MP at the HPC and Big Data conference in February 2014. However there is a shortage of
skills and available training in HPC to prepare and expand the workforce for the HPC and
Big Data research and development. Currently, HPC skills are acquired mainly by students
and staff taking part in HPC-related research projects, MSc courses, and at the dedicated
training centres such as Edinburgh University’s EPCC. There are few UK universities teaching
the HPC, Clusters and Grid Computing courses at the undergraduate level. To address the
issue of skills shortages in the HPC it is essential to provide teaching and training as part of
both postgraduate and undergraduate courses. The design and development of such courses is
challenging since the technologies and software in the fields of large scale distributed systems
such as Cluster, Cloud and Grid computing are undergoing continuous change. The students
completing the HPC courses should be proficient in these evolving technologies and equipped
with practical and theoretical skills for future jobs in this fast developing area.
In this paper we present our experience in developing the HPC, Cluster and Grid modules
including a review of existing HPC courses offered at the UK universities. The topics covered in
the modules are described, as well as the coursework projects based on practical laboratory work.
We conclude with an evaluation based on our experience over the last ten years in developing
and delivering the HPC modules on the undergraduate courses, with suggestions for future work
Virtual Environments for multiphysics code validation on Computing Grids
We advocate in this paper the use of grid-based infrastructures that are
designed for seamless approaches to the numerical expert users, i.e., the
multiphysics applications designers. It relies on sophisticated computing
environments based on computing grids, connecting heterogeneous computing
resources: mainframes, PC-clusters and workstations running multiphysics codes
and utility software, e.g., visualization tools. The approach is based on
concepts defined by the HEAVEN* consortium. HEAVEN is a European scientific
consortium including industrial partners from the aerospace, telecommunication
and software industries, as well as academic research institutes. Currently,
the HEAVEN consortium works on a project that aims to create advanced services
platforms. It is intended to enable "virtual private grids" supporting various
environments for users manipulating a suitable high-level interface. This will
become the basis for future generalized services allowing the integration of
various services without the need to deploy specific grid infrastructures
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