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High Accuracy Gravitational Waveforms from Black Hole Binary Inspirals Using OpenCL

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Justin McKennon, Gary Forrester, Gaurav Khanna

General Relativity and Quantum Cosmology · Submitted 1 Jun 2012

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present the performance of our Teukolsky EMRI code on multiple modern processor architectures and demonstrate the high level of accuracy and performance it is able to achieve.

how it is stated · asserted 1.00 read the record →

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arXiv ID1206.0270 (version 1)
Pith IDTB5RKNFQ, Pith's permanent record of this paper and its review
CitationGary Forrester, Gaurav Khanna, Justin McKennon. "High Accuracy Gravitational Waveforms from Black Hole Binary Inspirals Using OpenCL." arXiv:1206.0270 (2012). https://pith.science/paper/TB5RKNFQ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,864 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.14 · human participants 0.01 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.57 · compares with prior results 0.17 · mentions code or data availability 0.05.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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 using finite-differencing), that takes advantage of several mathematical and computational enhancements to efficiently generate long-duration and high-accuracy EMRI waveforms. We emphasize here the computational advances made in the context of this code. 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. Our code uses the Open Computing Language (OpenCL) for taking advantage of the massive parallelism offered by modern GPU architectures. We present the performance of our Teukolsky EMRI code on multiple modern processor architectures and demonstrate the high level of accuracy and performance it is able to achieve. We also present the code's scaling performance on a large supercomputer i.e. NSF's XSEDE resource: Keeneland (a 201 TeraFLOP/s, 120-node HP SL390 system with 240 Intel Xeon 5660 CPUs and 360 NVIDIA Fermi M2070 graphics processors, with the nodes connected by an InfiniBand QDR network).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.05 0.05 asserted 1.00
s1 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 using finite-differencing), that takes advantage of several mathematical and computational enhancements to efficiently generate long-duration and high-accuracy EMRI waveforms. contribution 0.93 0.93 0.22 asserted 0.99
s2 We emphasize here the computational advances made in the context of this code. contribution 0.89 0.35 0.04 asserted 0.58
s3 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. background 1.00 0.04 0.02 asserted 0.98
s4 Our code uses the Open Computing Language (OpenCL) for taking advantage of the massive parallelism offered by modern GPU architectures. method 0.94 0.87 0.06 asserted 1.00
s5 We present the performance of our Teukolsky EMRI code on multiple modern processor architectures and demonstrate the high level of accuracy and performance it is able to achieve. contribution 0.63 0.96 0.50 asserted 1.00
s6 We also present the code's scaling performance on a large supercomputer i.e. NSF's XSEDE resource: Keeneland (a 201 TeraFLOP/s, 120-node HP SL390 system with 240 Intel Xeon 5660 CPUs and 360 NVIDIA Fermi M2070 graphics processors, with the nodes connected by an InfiniBand QDR network). contribution 0.47 0.93 0.09 asserted 0.99

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from simulation or computation 1.00.

What was examined, and what was not

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not examined
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  • figures and tables
  • references and citing sentences
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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 using finite-differencing), that takes advantage of several mathematical and computational enhancements to efficiently generate long-duration and high-accuracy EMRI waveforms. We emphasize here the computational advances made in the context of this code. 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. Our code uses the Open Computing Language (OpenCL) for taking advantage of the massive parallelism offered by modern GPU architectures. We present the performance of our Teukolsky EMRI code on multiple modern processor architectures and demonstrate the high level of accuracy and performance it is able to achieve. We also present the code's scaling performance on a large supercomputer i.e. NSF's XSEDE resource: Keeneland (a 201 TeraFLOP/s, 120-node HP SL390 system with 240 Intel Xeon 5660 CPUs and 360 NVIDIA Fermi M2070 graphics processors, with the nodes connected by an InfiniBand QDR network).

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3 arXiv papers cite this in 8 sentences

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