### Abstract

The advent of systems biology requires the simulation of everlarger biomolecular systems, demanding a commensurate growth in computational power. This paper examines the use of the NVIDIA Tesla C870 graphics card programmed through the CUDA toolkit to accelerate the calculation of cutoff pair potentials, one of the most prevalent computations required by many different molecular modeling applications. We present algorithms to calculate electrostatic potential maps for cutoff pair potentials. Whereas a straightforward approach for decomposing atom data leads to low compute efficiency, a newer strategy enables fine-grained spatial decomposition of atom data that maps efficiently to the C870's memory system while increasing work-efficiency of atom data traversal by a factor of 5. The memory addressing flexibility exposed through CUDA's SPMD programming model is crucial in enabling this new strategy. An implementation of the new algorithm provides a greater than threefold performance improvement over our previously published implementation and runs 12 to 20 times faster than optimized CPU-only code. The lessons learned are generally applicable to algorithms accelerated by uniform grid spatial decomposition.

Original language | English (US) |
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Title of host publication | Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08 |

Pages | 273-282 |

Number of pages | 10 |

DOIs | |

State | Published - Dec 1 2008 |

Event | 2008 Conference on Computing Frontiers, CF'08 - Ischia, Italy Duration: May 5 2008 → May 7 2008 |

### Publication series

Name | Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08 |
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### Other

Other | 2008 Conference on Computing Frontiers, CF'08 |
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Country | Italy |

City | Ischia |

Period | 5/5/08 → 5/7/08 |

### Fingerprint

### Keywords

- CUDA
- GPGPU
- Graphics processors
- Molecular dynamics

### ASJC Scopus subject areas

- Computer Science Applications
- Hardware and Architecture
- Software
- Electrical and Electronic Engineering

### Cite this

*Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08*(pp. 273-282). (Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08). https://doi.org/10.1145/1366230.1366277

**GPU acceleration of cutoff pair potentials for molecular modeling applications.** / Rodrigues, Christopher I.; Hardy, David J.; Stone, John E.; Schulten, Klaus; Hwu, Wen Mei W.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08.*Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08, pp. 273-282, 2008 Conference on Computing Frontiers, CF'08, Ischia, Italy, 5/5/08. https://doi.org/10.1145/1366230.1366277

}

TY - GEN

T1 - GPU acceleration of cutoff pair potentials for molecular modeling applications

AU - Rodrigues, Christopher I.

AU - Hardy, David J.

AU - Stone, John E.

AU - Schulten, Klaus

AU - Hwu, Wen Mei W.

PY - 2008/12/1

Y1 - 2008/12/1

N2 - The advent of systems biology requires the simulation of everlarger biomolecular systems, demanding a commensurate growth in computational power. This paper examines the use of the NVIDIA Tesla C870 graphics card programmed through the CUDA toolkit to accelerate the calculation of cutoff pair potentials, one of the most prevalent computations required by many different molecular modeling applications. We present algorithms to calculate electrostatic potential maps for cutoff pair potentials. Whereas a straightforward approach for decomposing atom data leads to low compute efficiency, a newer strategy enables fine-grained spatial decomposition of atom data that maps efficiently to the C870's memory system while increasing work-efficiency of atom data traversal by a factor of 5. The memory addressing flexibility exposed through CUDA's SPMD programming model is crucial in enabling this new strategy. An implementation of the new algorithm provides a greater than threefold performance improvement over our previously published implementation and runs 12 to 20 times faster than optimized CPU-only code. The lessons learned are generally applicable to algorithms accelerated by uniform grid spatial decomposition.

AB - The advent of systems biology requires the simulation of everlarger biomolecular systems, demanding a commensurate growth in computational power. This paper examines the use of the NVIDIA Tesla C870 graphics card programmed through the CUDA toolkit to accelerate the calculation of cutoff pair potentials, one of the most prevalent computations required by many different molecular modeling applications. We present algorithms to calculate electrostatic potential maps for cutoff pair potentials. Whereas a straightforward approach for decomposing atom data leads to low compute efficiency, a newer strategy enables fine-grained spatial decomposition of atom data that maps efficiently to the C870's memory system while increasing work-efficiency of atom data traversal by a factor of 5. The memory addressing flexibility exposed through CUDA's SPMD programming model is crucial in enabling this new strategy. An implementation of the new algorithm provides a greater than threefold performance improvement over our previously published implementation and runs 12 to 20 times faster than optimized CPU-only code. The lessons learned are generally applicable to algorithms accelerated by uniform grid spatial decomposition.

KW - CUDA

KW - GPGPU

KW - Graphics processors

KW - Molecular dynamics

UR - http://www.scopus.com/inward/record.url?scp=53749106683&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=53749106683&partnerID=8YFLogxK

U2 - 10.1145/1366230.1366277

DO - 10.1145/1366230.1366277

M3 - Conference contribution

AN - SCOPUS:53749106683

SN - 9781605580777

T3 - Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08

SP - 273

EP - 282

BT - Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08

ER -