Justin McKennon, Gary Forrester, Gaurav Khanna
There is a strong need for high-accuracy and efficient modeling of extreme-mass-ratio binary black hole systems because these are strong sources of gravitational waves that would be detected by future observatories. In this article, we present sample results from our Teukolsky EMRI code: a time-domain Teukolsky equation solver (a linear, hyperbolic, partial differential equation solver […]
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Bernd Bruegmann
We construct a pseudospectral method for the solution of time-dependent, non-linear partial differential equations on a three-dimensional spherical shell. The problem we address is the treatment of tensor fields on the sphere. As a test case we consider the evolution of a single black hole in numerical general relativity. A natural strategy would be the […]
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Chad R. Galley, Frank Herrmann, John Silberholz, Manuel Tiglio, Gustavo Guerberoff
We perform a statistical analysis of the binary black hole problem in the post-Newtonian approximation by systematically sampling and evolving the parameter space of initial configurations for quasi-circular inspirals. Through a principal component analysis of spin and orbital angular momentum variables we systematically look for uncorrelated quantities and find three of them which are highly […]
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Burkhard Zink
We present Horizon, a new graphics processing unit (GPU)-accelerated code to solve the equations of general relativistic magnetohydrodynamics in a given spacetime. We evaluate the code in several test cases, including magnetized Riemann problems and rapidly rotating neutron stars, and measure the performance benefits of the GPU acceleration in comparison to our CPU-based code Thor. […]
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Jens Breitbart, Gaurav Khanna
We present a detailed approach for making use of two new computer hardware architectures — CBEA and CUDA — for accelerating a scientific data-analysis application (Einstein@Home). Our results suggest that both the architectures suit the application quite well and the achievable performance in the same software developmental time-frame, is nearly identical.
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Shin Kee Chung, Linqing Wen, David Blair, Kipp Cannon, Amitava Datta
We report a novel application of graphics processing units (GPUs) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16 fold has been achieved compared with a single central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in […]
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Niket K. Choudhary, Rakesh Ginjupalli, Sandeep Navada, Gaurav Khanna
Currently there is considerable interest in making use of many-core processor architectures, such as Nvidia and AMD graphics processing units (GPUs) for scientific computing. In this work we explore the use of the Open Computing Language (OpenCL) for a typical Numerical Relativity application: a time-domain Teukolsky equation solver (a linear, hyperbolic, partial differential equation solver […]
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Gaurav Khanna, Justin McKennon
In this work, we make use of the OpenCL framework to accelerate an EMRI modeling application using the hardware accelerators – Cell BE and Tesla CUDA GPU. We describe these compute technologies and our parallelization approach in detail, present our performance results, and then compare them with those from our previous implementations based on the […]
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Gaurav Khanna, Justin McKennon
In this paper, we accelerate a gravitational physics numerical modelling application using hardware accelerators — Cell processor and Tesla CUDA GPU. We describe these new technologies and our approach in detail, and then present our final performance results. We obtain well over an order-of-magnitude performance gain in our application by making use of these many-core […]
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Frank Herrmann, John Silberholz, Matias Bellone, Gustavo Guerberoff, Manuel Tiglio
We report on early results of a numerical and statistical study of binary black hole inspirals. The two black holes are evolved using post-Newtonian approximations starting with initially randomly distributed spin vectors. We characterize certain aspects of the distribution shortly before merger. In particular we note the uniform distribution of black hole spin vector dot […]
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Rakesh Ginjupalli, Gaurav Khanna
Hardware accelerators (such as Nvidia’s CUDA GPUs) have tremendous promise for computational science, because they can deliver large gains in performance at relatively low cost. In this work, we focus on the use of Nvidia’s Tesla GPU for high-precision (double, quadruple and octal precision) numerical simulations in the area of black hole physics — more […]
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Leone B. Bosi
In this paper we report the prototype of the first coalescing binary detection pipeline fully implemented on NVIDIA GPU hardware accelerators. The code has been embedded in a GPU library, called cuInspiral and has been developed under CUDA framework. The library contains for example a PN gravitational wave signal generator, matched filtering/FFT and detection algorithms […]
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