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Journal of Computational Chemistry

Full configuration interaction algorithm on a massively parallel architecture: Direct-list implementation

Elda Rossi, Gian Luigi Bendazzoli, Stefano Evangelisti

Published Apr 30, 1998• 18 citations
Publisher

Abstract

A parallel full configuration interaction (FCI) code, implemented on a distributed memory MPP computer, has been modified in order to use a direct algorithm to compute the lists of mono- and biexcitations each time they are needed. We were able to perform FCI calculations on the ground state of the acetylene molecule with two different basis sets, corresponding to more than 2.5 and 5 billion Slater determinants, respectively. The calculations were performed on a Cray-T3D and a Cray-T3E, both machines having 128 processors. Performance and comparison between the two computers are reported and discussed. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 658–672, 1998

DOI
10.1002/(sici)1096-987x(19980430)19:6<658::aid-jcc7>3.0.co;2-q

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A parallel full configuration interaction (FCI) code, implemented on a distributed memory MPP computer, has been modified in order to use a direct algorithm to compute the lists of mono- and biexcitations each time they are needed. We were able to perform FCI calculations on the ground state of the acetylene molecule with two different basis sets, corresponding to more than 2.5 and 5 billion Slater determinants, respectively. The calculations were performed on a Cray-T3D and a Cray-T3E, both machines having 128 processors. Performance and comparison between the two computers are reported and discussed. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 658–672, 1998
High Resolution Image Download MS PowerPoint Slide Full configuration interaction (FCI) can provide an exact molecular ground-state energy within a given basis set and serve as a benchmark for approximate methods in quantum chemical calculations, including the emerging variational quantum eigensolver. However, its exponential computational and memory requirements easily exceed the capability of a single server and limit its applicability to large molecules. In this paper, we present a distributed FCI implementation employing a hybrid parallelization scheme with multithreading and multiprocessing to expand FCI’s applicability. We optimize this scheme to minimize the bottlenecks arising from interprocess communications and interthread data management. Our implementation achieves higher scalability than the naive combination of prior works and successfully calculates the exact energy of C 3 H 8 /STO-3G with 1.31 trillion determinants, which is the largest FCI calculation to the best of our knowledge. Furthermore, we provide a comprehensive list of FCI results with 136 combinations of molecules and basis sets for future evaluation and development of approximate methods.
A full configuration interaction (FCI) algorithm is presented and discussed. It is an integral driven formalism based on the explicit construction of tables which realize the correspondence between the FCI vector x and the vector Hx, H being the Hamiltonian matrix of the system. In this way no decomposition of the identity is needed, and in the simplest implementation only the two vectors x and Hx need to be kept on disk. The main test has been done on the cyclic polyene C18H18 in the Pariser–Parr–Pople approximation, where the size of the FCI vector can be reduced to about 73 million components. Running on a CRAY Y-MP with 4 CPU and 32 MW of core memory, we obtained an elapsed CPU time per iteration of about 300 s and a total elapsed time of 1000 s, which correspond to about 4 and 14 s per million determinants, respectively. The parallel CPU speed-up obtained by running with the 4 CPU is greater than 3, without any substantial increasing of the memory or disk requirements.
We propose an efficient deterministic method to calculate the full configuration interaction (FCI) ground state energy. This method leverages the sparseness of the Lanczos basis vectors that span the Krylov subspace associated with the Hamiltonian to rapidly calculate the lowest eigenvalue of the effective Hamiltonian formed in this basis. By iteratively performing the spanning and diagonalization steps, this method is capable of rapidly reaching chemical accuracy for a variety of strongly correlated molecules, including stretched N2_22​ and C2_22​, in merely several tens of iterations. To accelerate our algorithm, we implement parallelized sparse matrix-sparse vector multiplication, which features a novel hashing method that establishes a one-to-one correspondence between the set of full configuration Slater determinants and a set of consecutive integers. A near-linear acceleration was observed.
Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute the exact energy of propane (\ce{C3H8}) in the minimal basis STO-3G. This formidable task involves handling an active space of 26 electrons in 23 orbitals or a Hilbert space of \SI{1.3d12} determinants. This is, by far, the largest FCI calculation reported to date. Here, we illustrate how, from a general point of view, selected configuration interaction (SCI) can achieve microhartree accuracy at a fraction of the computational and memory cost, via a sparse exploration of the FCI space. The present SCI calculations are performed with the \textit{Configuration Interaction using a Perturbative Selection made Iteratively} (CIPSI) algorithm, as implemented in a determinant-driven way in the \textsc{quantum package} software. The present study reinforces the common wisdom that among the exponentially large number of determinants in the FCI space, only a tiny fraction of them significantly contribute to the energy. More importantly, it demonstrates the feasibility of achieving comparable accuracy using more reasonable and sustainable computational resources, hence reducing the ever-growing carbon footprint of computational chemistry.
We develop a multithreaded parallel coordinate descent full configuration interaction algorithm (mCDFCI) for the electronic structure ground-state calculation in the configuration interaction framework. The FCI problem is reformulated as an unconstrained minimization problem and tackled by a modified block coordinate descent method with a deterministic compression strategy. mCDFCI is designed to prioritize determinants based on their importance, with block updates enabling efficient parallelization on shared-memory, multicore computing infrastructure. We demonstrate the efficiency of the algorithm by computing an accurate benchmark energy for the chromium dimer in the Ahlrichs SV basis (48e, 42o), which explicitly includes 2.07 × 10 9 variational determinants. We also provide the binding curve of the nitrogen dimer under the cc-pVQZ basis set (14e, 110o). Benchmarks show up to 79.3% parallel efficiency on 128 cores.
Both the replicated and distributed data parallel full configuration interaction (FCI) implementations are described. The implementation of the FCI algorithm is organized in a hybrid strings-integral driven approach. Redundant communication is avoided, and the network performance is further optimized by an improved distributed data interface library. Examples show linear scalability of the distributed data code on both PC and workstation clusters. The new parallel implementation greatly extends the hardware on which parallel FCI calculations can be performed. The timing data on the workstation cluster show great potential for using the new parallel FCI algorithm in expanding applications of complete active space self-consistent field applications.