A GPU based multidimensional amplitude analysis to search for tetraquark candidates

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons.Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously.In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the B0 → J/ψKπ decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

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A GPU based multidimensional amplitude analysis to search for tetraquark candidates

10.21203/rs.3.rs-51185/v3 ◽

2020 ◽

Author(s):

Nairit Sur ◽

Leonardo Cristella ◽

Adriano Di Florio ◽

Vincenzo Mastrapasqua

Keyword(s):

Graphics Processing Units ◽

High Energy Physics ◽

High Energy ◽

Amplitude Analysis ◽

Hadron Spectroscopy ◽

Multiple Cores ◽

Analysis Strategies ◽

Computationally Intensive ◽

Computational Resources ◽

Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

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A GPU based multidimensional amplitude analysis to search for tetraquark candidates

10.21203/rs.3.rs-51185/v2 ◽

2020 ◽

Author(s):

Nairit Sur ◽

Leonardo Cristella ◽

Adriano Di Florio ◽

Vincenzo Mastrapasqua

Keyword(s):

Graphics Processing Units ◽

High Energy Physics ◽

High Energy ◽

Amplitude Analysis ◽

Hadron Spectroscopy ◽

Multiple Cores ◽

Analysis Strategies ◽

Computationally Intensive ◽

Computational Resources ◽

Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

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A GPU based multidimensional amplitude analysis to search for tetraquark candidates

Journal Of Big Data ◽

10.1186/s40537-020-00408-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Nairit Sur ◽

Leonardo Cristella ◽

Adriano Di Florio ◽

Vincenzo Mastrapasqua

Keyword(s):

Graphics Processing Units ◽

High Energy Physics ◽

High Energy ◽

Amplitude Analysis ◽

Hadron Spectroscopy ◽

Multiple Cores ◽

Analysis Strategies ◽

Computationally Intensive ◽

Computational Resources ◽

Graphics Processing

AbstractThe demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the $$B^0 \rightarrow J/\psi K \pi$$ B 0 → J / ψ K π decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

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A GPU Based Multidimensional Amplitude Analysis to Search for Tetraquark Candidates

10.21203/rs.3.rs-51185/v1 ◽

2020 ◽

Author(s):

Nairit Sur ◽

Leonardo Cristella ◽

Adriano Di Florio ◽

Vincenzo Mastrapasqua

Keyword(s):

Graphics Processing Units ◽

High Energy Physics ◽

High Energy ◽

Amplitude Analysis ◽

Hadron Spectroscopy ◽

Speed Up ◽

Computationally Intensive ◽

Quark Structure ◽

Computational Resources ◽

Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data while physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the quark structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays that can also be seamlessly adapted for other similar analyses. The GooFit fitter running on GPUs shows a remarkable speed-up in the computing performance when compared to a ROOT/RooFit implementation of the same, running on multicore CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

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Application of Deep Learning on Integrating Prediction, Provenance, and Optimization

EPJ Web of Conferences ◽

10.1051/epjconf/201921406007 ◽

2019 ◽

Vol 214 ◽

pp. 06007

Author(s):

Malachi Schram ◽

Nathan Tallent ◽

Ryan Friese ◽

Alok Singh ◽

Ilkay Altintas

Keyword(s):

Classification Accuracy ◽

Large Scale ◽

High Energy Physics ◽

Job Scheduling ◽

Short Term Memory ◽

High Energy ◽

Short Term ◽

Long Short Term Memory ◽

Computational Resources ◽

Energy Physics

In this research, we investigated two approaches to detect job anomalies and/or contention for large scale computing efforts: 1. Preemptive job scheduling using binomial classification long short-term memory networks 2. Forecasting intra-node computing loads from the active jobs and additional job(s) For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.

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