Perturbative Coupled‐Cluster Methods on Graphics Processing Units: Single‐ and Multi‐Reference Formulations

Ma, Wei; Bhaskaran‐Nair, Kiran; Villa, Oreste; Aprà, Edoardo; Tumeo, Antonino; Krishnamoorthy, Sriram; Kowalski, Karol

doi:10.1002/9781118670712.ch14

Cited by 3 publications

(1 citation statement)

References 107 publications

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“…A large scale application of the NWChem GPU-enabled code on a leadership platform was reported by Ma et al: the all-electron (T) energy of a single pentacene molecule (cc-pVDZ basis) was evaluated on 96 nodes of the Titan supercomputer at OLCF, each node of which has one AMD Opteron 6274 CPU (281.6 GFLOPS peak) and one NVIDIA K20X GPU (1.3 TLOPS peak). The CPU-only computation utilizing all 16 cores (2 cores in the Opteron processor correspond to 1 floating-point unit) of the Opteron chip took 9240 s, while the hybrid execution took 1631 s, for an ≈5.7 speedup, which is slightly more than the theoretical speedup.…”

Section: Deployment Of Many-body Quantum Chemistry To Parallel Computersmentioning

confidence: 99%

Many-Body Quantum Chemistry on Massively Parallel Computers

et al. 2020

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The deployment of many-body quantum chemistry methods onto massively parallel high-performance computing (HPC) platforms is reviewed. The particular focus is on highly accurate methods that have become popular in predictive description of chemical phenomena, such as the coupled-cluster method. The account of relevant literature is preceded by a discussion of the modern and near-future HPC landscape and the relevant computational traits of the many-body methods, in their canonical and reduced-scaling formulations, that underlie the challenges in their HPC realization.

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Section: Deployment Of Many-body Quantum Chemistry To Parallel Computersmentioning

confidence: 99%

Many-Body Quantum Chemistry on Massively Parallel Computers

et al. 2020

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Double‐buffered, heterogeneous CPU + GPU integral digestion algorithm for single‐excitation calculations involving a large number of excited states

2018

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The most widely used quantum-chemical models for excited states are single-excitation theories, a category that includes configuration interaction with single substitutions, timedependent density functional theory, and also a recently developed ab initio exciton model. When a large number of excited states are desired, these calculations incur a significant bottleneck in the "digestion" step in which two-electron integrals are contracted with density or density-like matrices. We present an implementation that moves this step onto graphical processing units (GPUs), and introduce a double-buffer scheme that minimizes latency by computing integrals on the central processing units (CPUs) concurrently with their digestion on the GPUs. An automatic code generation scheme simplifies the implementation of high-performance GPU kernels. For the exciton model, which requires separate excited-state calculations on each electronically coupled chromophore, the heterogeneous implementation described here results in speedups of 2-6× versus a CPU-only implementation. For traditional time-dependent density functional theory calculations, we obtain speedups of up to 5× when a large number of excited states is computed.

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Distributed memory, GPU accelerated Fock construction for hybrid, Gaussian basis density functional theory

Williams‐Young

Asadchev

Popovici

et al. 2023

The Journal of Chemical Physics

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With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. In the present work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn–Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

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Perturbative Coupled‐Cluster Methods on Graphics Processing Units: Single‐ and Multi‐Reference Formulations

Cited by 3 publications

References 107 publications

Many-Body Quantum Chemistry on Massively Parallel Computers

Many-Body Quantum Chemistry on Massively Parallel Computers

Double‐buffered, heterogeneous CPU + GPU integral digestion algorithm for single‐excitation calculations involving a large number of excited states

Distributed memory, GPU accelerated Fock construction for hybrid, Gaussian basis density functional theory

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