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Multinode Multi-GPU Two-Electron Integrals: Code Generation Using the Regent Language.

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We developed new parallel algorithms for calculating electron repulsion integrals (ERIs) in quantum chemistry. This enables faster simulations of large molecules on multiple computers and GPUs.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • High-Performance Computing

Background:

  • Two-electron repulsion integrals (ERIs) are computationally intensive in ab initio methods, scaling formally as O(N^4).
  • Previous GPU acceleration in TeraChem was limited to single nodes, hindering large-scale simulations.
  • Efficient computation of Coulomb (J) and exchange (K) matrices is crucial for Fock matrix construction.

Purpose of the Study:

  • To implement and evaluate multinode, multi-GPU algorithms for J- and K-matrix computations in TeraChem.
  • To overcome the bottleneck of single-node GPU limitations for large molecular simulations.
  • To leverage the Regent programming language for distributed, task-based computation across multiple nodes and GPUs.

Main Methods:

  • Implementation of multinode multi-GPU J- and K-matrix algorithms using the Regent programming language.
  • Utilizing Regent's task-based model for distributed computation and code generation for NVIDIA GPUs.
  • Metaprogramming in Regent to enable flexible code generation for integrals with varying angular momenta.

Main Results:

  • Demonstrated multinode scaling of the new algorithms up to 45 GPUs across 3 nodes.
  • Benchmarked the performance of the multinode multi-GPU implementation against existing hand-coded TeraChem integral code.
  • Successfully enabled distributed computation for large-scale quantum chemistry simulations.

Conclusions:

  • The developed multinode multi-GPU algorithms significantly enhance the scalability of ab initio electronic structure calculations.
  • Regent programming language provides an effective framework for developing high-performance, distributed scientific applications.
  • This work paves the way for more advanced simulations, such as excited state dynamics in large photoactive proteins.