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.
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.
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.
We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers.
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.
Full configuration interaction (FCI) serves as a benchmark tool for evaluating the accuracy of approximate quantum chemical methods. However, its exponential computational cost limits its practical use. The inherently parallel and compute-intensive nature of matrix and vector operations in FCI is well-suited for acceleration on modern GPU architectures. Despite this potential, prior research has only focused on accelerating the matrix and vector multiplication, thus failing to fully exploit the power of GPUs. In this paper, we present a fully GPU-accelerated FCI implementation that uses a CUDA-enabled GPU for the entire computational workflow, achieving end-to-end acceleration. Our implementation incorporates and refines advanced optimization strategies from previous studies, while crucially eliminating redundant computational steps and minimizing CPU-GPU data transfers. To facilitate broad usability and integration, we offer both Python- and C-based implementations. Our C implementation achieves up to a 35.4× speedup over a multithreaded CPU implementation and up to a 15.9× speedup over the current leading GPU-based implementation, respectively. Our Python implementation also delivers substantial acceleration, reaching up to 18.9× and 8.7× speedups over CPU and prior GPU implementations, respectively.
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.