Double Precision Is Not Needed for Many-Body Calculations: New Conventional Wisdom

2018 ◽  
Author(s):  
Pavel Pokhilko ◽  
Evgeny Epifanovsky ◽  
Anna I. Krylov
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Coupled Cluster ◽  
Double Precision ◽  
Many Body ◽  
Single Precision ◽  
Parallel Performance ◽  
Point Representation ◽  
Electron Repulsion Integrals ◽  
Cluster Methods

Using single precision floating point representation reduces the size of data and computation time by a factor of two relative to double precision conventionally used in electronic structure programs. For large-scale calculations, such as those encountered in many-body theories, reduced memory footprint alleviates memory and input/output bottlenecks. Reduced size of data can lead to additional gains due to improved parallel performance on CPUs and various accelerators. However, using single precision can potentially reduce the accuracy of computed observables. Here we report an implementation of coupled-cluster and equation-of-motion coupled-cluster methods with single and double excitations in single precision. We consider both standard implementation and one using Cholesky decomposition or resolution-of-the-identity of electron-repulsion integrals. Numerical tests illustrate that when single precision is used in correlated calculations, the loss of accuracy is insignificant and pure single-precision implementation can be used for computing energies, analytic gradients, excited states, and molecular properties. In addition to pure single-precision calculations, our implementation allows one to follow a single-precision calculation by clean-up iterations, fully recovering double-precision results while retaining significant savings.

Double Precision Is Not Needed for Many-Body Calculations: New Conventional Wisdom

2018 ◽  
Author(s):  
Pavel Pokhilko ◽  
Evgeny Epifanovsky ◽  
Anna I. Krylov
Keyword(s):  
Large Scale ◽  
Computation Time ◽  
Coupled Cluster ◽  
Double Precision ◽  
Many Body ◽  
Single Precision ◽  
Parallel Performance ◽  
Point Representation ◽  
Electron Repulsion Integrals ◽  
Cluster Methods

Using single precision floating point representation reduces the size of data and computation time by a factor of two relative to double precision conventionally used in electronic structure programs. For large-scale calculations, such as those encountered in many-body theories, reduced memory footprint alleviates memory and input/output bottlenecks. Reduced size of data can lead to additional gains due to improved parallel performance on CPUs and various accelerators. However, using single precision can potentially reduce the accuracy of computed observables. Here we report an implementation of coupled-cluster and equation-of-motion coupled-cluster methods with single and double excitations in single precision. We consider both standard implementation and one using Cholesky decomposition or resolution-of-the-identity of electron-repulsion integrals. Numerical tests illustrate that when single precision is used in correlated calculations, the loss of accuracy is insignificant and pure single-precision implementation can be used for computing energies, analytic gradients, excited states, and molecular properties. In addition to pure single-precision calculations, our implementation allows one to follow a single-precision calculation by clean-up iterations, fully recovering double-precision results while retaining significant savings.

Towards large-scale calculations with State-Specific Multireference Coupled Cluster methods: Studies on dodecane, naphthynes, and polycarbenes

2012 ◽  
Vol 542 ◽  
pp. 128-133 ◽  
Author(s):  
Jiří Brabec ◽  
Kiran Bhaskaran-Nair ◽  
Karol Kowalski ◽  
Jiří Pittner ◽  
Hubertus J.J. van Dam
Keyword(s):  
Large Scale ◽  
Coupled Cluster ◽  
Coupled Cluster Methods ◽  
Cluster Methods

Accelerating NWChem Coupled Cluster through dataflow-based execution

2017 ◽  
Vol 32 (4) ◽  
pp. 540-551 ◽  
Author(s):  
Heike Jagode ◽  
Anthony Danalis ◽  
Jack Dongarra
Keyword(s):  
High Performance ◽  
Coupled Cluster ◽  
Many Body ◽  
Mass Conversion ◽  
Many Body Systems ◽  
Computationally Intensive ◽  
Scalable Computation ◽  
Cluster Methods ◽  
Performance Computing ◽  
Chemistry Community

Numerical techniques used for describing many-body systems, such as the Coupled Cluster methods (CC) of the quantum chemistry package NWChem, are of extreme interest to the computational chemistry community in fields such as catalytic reactions, solar energy, and bio-mass conversion. In spite of their importance, many of these computationally intensive algorithms have traditionally been thought of in a fairly linear fashion, or are parallelized in coarse chunks. In this paper, we present our effort of converting the NWChem’s CC code into a dataflow-based form that is capable of utilizing the task scheduling system PaRSEC (Parallel Runtime Scheduling and Execution Controller): a software package designed to enable high-performance computing at scale. We discuss the modularity of our approach and explain how the PaRSEC-enabled dataflow version of the subroutines seamlessly integrate into the NWChem codebase. Furthermore, we argue how the CC algorithms can be easily decomposed into finer-grained tasks (compared with the original version of NWChem); and how data distribution and load balancing are decoupled and can be tuned independently. We demonstrate performance acceleration by more than a factor of two in the execution of the entire CC component of NWChem, concluding that the utilization of dataflow-based execution for CC methods enables more efficient and scalable computation.

scholarly journals Some non-relativistic many electron theories: single reference case

2012 ◽  
Vol 10 (5) ◽  
pp. 1383-1390 ◽  
Author(s):  
Hüseyin Aksu
Keyword(s):  
Electron Correlation ◽  
Electron Pair ◽  
Cluster Method ◽  
Coupled Cluster ◽  
Many Body ◽  
Electron Theory ◽  
Reference Case ◽  
Substantial Progress ◽  
Electron Correlation Effects ◽  
Cluster Methods

AbstractThere has been substantial progress in the development of electron correlation methods with some benefits and drawbacks. In this paper, we give a review of electron correlation effects on many body particles system. We focus more on atoms and molecules rather than solid state and take into account single reference case only. We mainly discuss perturbation theory, coupled electron many electron theory and a few versions of coupled electron pair approximations with comparison configuration interaction and some coupled cluster methods in which coupled cluster method is crucially important for the crystalline solids, the electron gas and the heat of the reaction. We also show some results, reported by several authors, to fairly compare and judge the methods’ feasibility mentioned above.

scholarly journals Linear-Scaling Systematic Molecular Fragmentation Approach for High-Level Coupled-Cluster Methods: Coupled-Cluster Meets Macromolecules

2020 ◽  
Author(s):  
Ugur Bozkaya ◽  
Betül Ermiş
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Chemical System ◽  
Coupled Cluster ◽  
Linear Scaling ◽  
Chemical Systems ◽  
Molecular Fragmentation ◽  
Coupled Cluster Methods ◽  
High Level ◽  
Cluster Methods

<p>The coupled-cluster (CC) singles and doubles with perturbative triples [CCSD(T)] method is frequently referred to as the “gold standard" of modern computational chemistry. However, the high computational cost of CCSD(T) [O(N7)], where N is the number of basis functions, limits its applications to small-sized chemical systems. To address this problem, efficient implementations of linear-scaling coupled-cluster methods, which</p><p>employ the systematic molecular fragmentation (SMF) approach, are reported. In this study: (1) to achieve exact linear-scaling and to obtain a pure ab inito approach, we revise the handling of nonbonded interactions in the SMF approach (2) a new fragmentation algorithm, which yields smaller sized fragments; hence, better fits high-level CC methods is introduced (3) the new SMF approach is integrated with the high-level</p><p>CC methods, denoted by LSSMF-CC, for the first time. Performances of the LSSMF-CC approaches, such as LSSMF-CCSD(T), are compared with their canonical versions for a set of alkane molecules, CnH2n+2 (n=6–10), which includes 142 molecules. Our results demonstrate that the LSSMF approach introduces negligible errors compared with the canonical methods, mean absolute errors (MAEs) are between 0.20–0.59 kcal</p><p>mol-1 for LSSMF-CCSD(T). To further assess the accuracy of the LSSMF-CCSD(T) approach, we also consider several polyethylene (PE) models. For the PE set, the error of LSSMF-CCSD(T)/cc-pVDZ with respect to the experimental polymerization energies per unit are between 0.08–0.63 kcal/mol. To illustrate the efficiency and applicability of the LSSMF-CCSD(T) approach, we consider an alkane molecule with 10004 atoms. For this molecule, the LSSMF-CCSD(T)/cc-pVTZ energy computation on a Linux cluster with 100 nodes, 4 cores and 5 GB of memory are provided to each node, is performed just in ∼ 24 hours. As far as we know, this computation is an application of the CCSD(T) method on the largest chemical system to date. Overall, we conclude that (1) the LSSMF-CCSD(T) method can be reliably used for large scale chemical systems, where the canonical methods are not computationally affordable (2) the LSSMF-CCSD(T) method is very promising for accurate computation of energies in macromolecular systems (3) we believe that our study is a significant milestone in developing CC methods for large-scale chemical systems.</p>

Linear-Scaling Systematic Molecular Fragmentation Approach for High-Level Coupled-Cluster Methods: Coupled-Cluster Meets Macromolecules

2020 ◽  
Author(s):  
Ugur Bozkaya ◽  
Betül Ermiş
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Chemical System ◽  
Coupled Cluster ◽  
Linear Scaling ◽  
Chemical Systems ◽  
Molecular Fragmentation ◽  
Coupled Cluster Methods ◽  
High Level ◽  
Cluster Methods

<p>The coupled-cluster (CC) singles and doubles with perturbative triples [CCSD(T)] method is frequently referred to as the “gold standard" of modern computational chemistry. However, the high computational cost of CCSD(T) [O(N7)], where N is the number of basis functions, limits its applications to small-sized chemical systems. To address this problem, efficient implementations of linear-scaling coupled-cluster methods, which employ the systematic molecular fragmentation (SMF) approach, are reported. In this study: (1) to achieve exact linear-scaling and to obtain a pure ab inito approach, we revise the handling of nonbonded interactions in the SMF approach (2) a new fragmentation algorithm, which yields smaller sized fragments; hence, better fits high-level CC methods is introduced (3) the new SMF approach is integrated with the high-level CC methods, denoted by LSSMF-CC, for the first time. Performances of the LSSMF-CC approaches, such as LSSMF-CCSD(T), are compared with their canonical versions for a set of alkane molecules, CnH2n+2 (n=6–10), which includes 142 molecules. Our results demonstrate that the LSSMF approach introduces negligible errors compared with the canonical methods, mean absolute errors (MAEs) are between 0.20–0.59 kcal mol-1 for LSSMF-CCSD(T). To further assess the accuracy of the LSSMF-CCSD(T) approach, we also consider several polyethylene (PE) models. For the PE set, the error of LSSMF-CCSD(T)/cc-pVDZ with respect to the experimental polymerization energies per unit are between 0.08–0.63 kcal/mol. To illustrate the efficiency and applicability of the LSSMF-CCSD(T) approach, we consider an alkane molecule with 10004 atoms. For this molecule, the LSSMF-CCSD(T)/cc-pVTZ energy computation on a Linux cluster with 100 nodes, 4 cores and 5 GB of memory are provided to each node, is performed just in ∼ 24 hours. As far as we know, this computation is an application of the CCSD(T) method on the largest chemical system to date. Overall, we conclude that (1) the LSSMF-CCSD(T) method can be reliably used for large scale chemical systems, where the canonical methods are not computationally affordable (2) the LSSMF-CCSD(T) method is very promising for accurate computation of energies in macromolecular systems (3) we believe that our study is a significant milestone in developing CC methods for large-scale chemical systems.</p>

Linear-Scaling Systematic Molecular Fragmentation Approach for High-Level Coupled-Cluster Methods: Coupled-Cluster Meets Macromolecules

2020 ◽  
Author(s):  
Ugur Bozkaya ◽  
Betül Ermiş
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Chemical System ◽  
Coupled Cluster ◽  
Linear Scaling ◽  
Chemical Systems ◽  
Molecular Fragmentation ◽  
Coupled Cluster Methods ◽  
High Level ◽  
Cluster Methods

<p>The coupled-cluster (CC) singles and doubles with perturbative triples [CCSD(T)] method is frequently referred to as the “gold standard" of modern computational chemistry. However, the high computational cost of CCSD(T) [O(N7)], where N is the number of basis functions, limits its applications to small-sized chemical systems. To address this problem, efficient implementations of linear-scaling coupled-cluster methods, which employ the systematic molecular fragmentation (SMF) approach, are reported. In this study: (1) to achieve exact linear-scaling and to obtain a pure ab inito approach, we revise the handling of nonbonded interactions in the SMF approach (2) a new fragmentation algorithm, which yields smaller sized fragments; hence, better fits high-level CC methods is introduced (3) the new SMF approach is integrated with the high-level CC methods, denoted by LSSMF-CC, for the first time. Performances of the LSSMF-CC approaches, such as LSSMF-CCSD(T), are compared with their canonical versions for a set of alkane molecules, CnH2n+2 (n=6–10), which includes 142 molecules. Our results demonstrate that the LSSMF approach introduces negligible errors compared with the canonical methods, mean absolute errors (MAEs) are between 0.20–0.59 kcal mol-1 for LSSMF-CCSD(T). To further assess the accuracy of the LSSMF-CCSD(T) approach, we also consider several polyethylene (PE) models. For the PE set, the error of LSSMF-CCSD(T)/cc-pVDZ with respect to the experimental polymerization energies per unit are between 0.08–0.63 kcal/mol. To illustrate the efficiency and applicability of the LSSMF-CCSD(T) approach, we consider an alkane molecule with 10004 atoms. For this molecule, the LSSMF-CCSD(T)/cc-pVTZ energy computation on a Linux cluster with 100 nodes, 4 cores and 5 GB of memory are provided to each node, is performed just in ∼ 24 hours. As far as we know, this computation is an application of the CCSD(T) method on the largest chemical system to date. Overall, we conclude that (1) the LSSMF-CCSD(T) method can be reliably used for large scale chemical systems, where the canonical methods are not computationally affordable (2) the LSSMF-CCSD(T) method is very promising for accurate computation of energies in macromolecular systems (3) we believe that our study is a significant milestone in developing CC methods for large-scale chemical systems.</p>

The X40x10 Halogen Bonding Benchmark Revisited: Surprising Importance of (n-1)d Subvalence Correlation

2017 ◽  
Author(s):  
Manoj Kumar Kesharwani ◽  
Nitai Sylvetsky ◽  
Debashree Manna ◽  
Jan M.L. Martin
Keyword(s):  
Dissociation Energy ◽  
Noncovalent Interactions ◽  
Halogen Bonding ◽  
Coupled Cluster ◽  
Dissociation Energies ◽  
Iodine Species ◽  
Explicitly Correlated ◽  
High Level ◽  
Cluster Methods ◽  
Small Separation

<p>We have re-evaluated the X40x10 benchmark for halogen bonding using conventional and explicitly correlated coupled cluster methods. For the aromatic dimers at small separation, improved CCSD(T)–MP2 “high-level corrections” (HLCs) cause substantial reductions in the dissociation energy. For the bromine and iodine species, (n-1)d subvalence correlation increases dissociation energies, and turns out to be more important for noncovalent interactions than is generally realized. As in previous studies, we find that the most efficient way to obtain HLCs is to combine (T) from conventional CCSD(T) calculations with explicitly correlated CCSD-F12–MP2-F12 differences.</p>

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