Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers

Wei, Wei; Khan, Asad; Huerta, E. A.; Huang, Xiaobo; Tian, Minyang

doi:10.1016/j.physletb.2020.136029

Cited by 45 publications

(25 citation statements)

References 17 publications

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“…It is worth comparing this figure to other recent studies in the literature. For instance, in [65], it was reported that an ensemble of 2 AI models reported 1 misclassification for every 2.7 days of searched data, and more basic AI architectures reported one misclassification for every 200 seconds of searched advanced LIGO data [28]. For completeness, it is worth mentioning that the results we present in Figure 6 differ from those we computed with traditional TensorFlow models in less than 0.01% [66].…”

Section: Discussionmentioning

confidence: 74%

“…In this article, we build upon our recent work developing AI frameworks for production scale gravitational wave detection [65,66], and introduce an approach that consists of optimizing AI models for accelerated inference, leveraging NVIDIA TensorRT [67]. We describe how we deployed our TensorRT AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computing Facility, and developed the required software to optimally distribute inference using up to 20 nodes, which are equivalent to 160 NVIDIA A100 Tensor Core GPUs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inference-optimized AI and high performance computing for gravitational wave detection at scale

Chaturvedi,

Khan,

Tian

et al. 2022

Preprint

Self Cite

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We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble porcessed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 seconds. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

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Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Inference-optimized AI and high performance computing for gravitational wave detection at scale

Chaturvedi,

Khan,

Tian

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is worth comparing this figure to other recent studies in the literature. For instance, in Wei et al ( 2021b ), it was reported that an ensemble of 2 AI models reported 1 misclassification for every 2.7 days of searched data, and more basic AI architectures reported one misclassification for every 200 s of searched advanced LIGO data (George and Huerta, 2018a , b ). For completeness, it is worth mentioning that the results we present in Figure 6 differ from those we computed with traditional models in less than 0.01% (Huerta et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…When using a time-shifted advanced LIGO dataset that spans 5 years worth of data, we found that our AI ensemble reports 1 misclassification per month of searched data. This should be contrasted with the first generation of AI models that reported 1 misclassification for every 200 s of searched data (George and Huerta, 2018a , b ), and the other AI ensembles that reported 1 misclassifications for every 2.7 days of searched data (Wei et al, 2021b ).…”

Section: Discussionmentioning

confidence: 96%

“…In this article, we build upon our recent work developing AI frameworks for production scale gravitational wave detection (Huerta et al, 2021;Wei et al, 2021b), and introduce an approach that consists of optimizing AI models for accelerated inference, levering NVIDIA TensorRT (NVIDIA, 2021). We describe how we deployed our TensorRT AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computing Facility, and developed the required software to optimally distribute inference using up to 20 nodes, which are equivalent to 160 NVIDIA A100 Tensor Core GPUs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

Chaturvedi

Khan

Tian

et al. 2022

Front. Artif. Intell.

Self Cite

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We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

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Machine Learning Applications in Gravitational Wave Astronomy

Stergioulas

2024

Compact Objects in the Universe

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Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers

Cited by 45 publications

References 17 publications

Inference-optimized AI and high performance computing for gravitational wave detection at scale

Inference-optimized AI and high performance computing for gravitational wave detection at scale

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

Machine Learning Applications in Gravitational Wave Astronomy

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