View in EDS

A novel method of heterogeneous parallel machine learning by CPU–TPU for molecular dynamics.

Saved in:
Bibliographic Details
Title: A novel method of heterogeneous parallel machine learning by CPU–TPU for molecular dynamics.
Authors: Zhang, Yujia1 (AUTHOR), Zhang, Xin2 (AUTHOR) zhangxin2302@hnu.edu.cn, Zheng, Gang1 (AUTHOR), Mo, Pinghui2 (AUTHOR), Zhao, Zhuoying1 (AUTHOR), Li, Chenyang2 (AUTHOR), Tang, Kai1 (AUTHOR), Liu, Jie2 (AUTHOR)
Source: Neural Computing & Applications. Sep2025, Vol. 37 Issue 26, p21949-21967. 19p.
Subjects: Molecular dynamics, Parallel computers, Load balancing (Computer networks), Central processing units
Abstract: In this paper, a heterogeneous parallel machine learning molecular dynamics (MLMD) calculation method based on both central processing unit (CPU) and SOPHON BM1684X tensor processing unit (TPU) is proposed. The method aims to offer a new hardware deployment approach for advanced MLMD algorithms, alleviating the constraints imposed by the severe "memory wall" and "power wall" bottlenecks caused by the separation of storage units and computing units inherent in von Neumann architecture-based machines at the hardware level. By decomposing complex MD simulation tasks into subtasks that can be processed in parallel on both CPU and TPU, this method enhances computational efficiency while maintaining high precision. Specifically, the potential energy surface fitting task in MD simulation is deployed on the TPU, leveraging its parallel processing capabilities to accelerate computations. Meanwhile, load balancing between the CPU and TPU is achieved by executing other computational tasks on the CPU. Experimental results demonstrate significant improvements in computational speed, energy efficiency, and the size of computable systems compared to the non-heterogeneous CPU-only system, indicating that heterogeneous parallel computing is an effective method for accelerating MD simulations. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 187668509
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A novel method of heterogeneous parallel machine learning by CPU–TPU for molecular dynamics.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yujia%22">Zhang, Yujia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xin%22">Zhang, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhangxin2302@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Gang%22">Zheng, Gang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mo%2C+Pinghui%22">Mo, Pinghui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zhuoying%22">Zhao, Zhuoying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chenyang%22">Li, Chenyang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Kai%22">Tang, Kai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jie%22">Liu, Jie</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Sep2025, Vol. 37 Issue 26, p21949-21967. 19p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computers%22">Parallel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Load+balancing+%28Computer+networks%29%22">Load balancing (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Central+processing+units%22">Central processing units</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, a heterogeneous parallel machine learning molecular dynamics (MLMD) calculation method based on both central processing unit (CPU) and SOPHON BM1684X tensor processing unit (TPU) is proposed. The method aims to offer a new hardware deployment approach for advanced MLMD algorithms, alleviating the constraints imposed by the severe "memory wall" and "power wall" bottlenecks caused by the separation of storage units and computing units inherent in von Neumann architecture-based machines at the hardware level. By decomposing complex MD simulation tasks into subtasks that can be processed in parallel on both CPU and TPU, this method enhances computational efficiency while maintaining high precision. Specifically, the potential energy surface fitting task in MD simulation is deployed on the TPU, leveraging its parallel processing capabilities to accelerate computations. Meanwhile, load balancing between the CPU and TPU is achieved by executing other computational tasks on the CPU. Experimental results demonstrate significant improvements in computational speed, energy efficiency, and the size of computable systems compared to the non-heterogeneous CPU-only system, indicating that heterogeneous parallel computing is an effective method for accelerating MD simulations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187668509
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00521-025-11498-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 21949
    Subjects:
      – SubjectFull: Molecular dynamics
        Type: general
      – SubjectFull: Parallel computers
        Type: general
      – SubjectFull: Load balancing (Computer networks)
        Type: general
      – SubjectFull: Central processing units
        Type: general
    Titles:
      – TitleFull: A novel method of heterogeneous parallel machine learning by CPU–TPU for molecular dynamics.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Yujia
      – PersonEntity:
          Name:
            NameFull: Zhang, Xin
      – PersonEntity:
          Name:
            NameFull: Zheng, Gang
      – PersonEntity:
          Name:
            NameFull: Mo, Pinghui
      – PersonEntity:
          Name:
            NameFull: Zhao, Zhuoying
      – PersonEntity:
          Name:
            NameFull: Li, Chenyang
      – PersonEntity:
          Name:
            NameFull: Tang, Kai
      – PersonEntity:
          Name:
            NameFull: Liu, Jie
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 11
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09410643
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 26
          Titles:
            – TitleFull: Neural Computing & Applications
              Type: main
ResultId 1