QMCkl: A Unified Approach to Accelerating Quantum Monte Carlo Codes

Akila Gothandaraman

K. Murashima

E. Slootman, Vijay Gopal Chilkuri, Aurélien Delval +16 more

Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance kernels implementing the core building blocks of QMC calculations. It offers a C-compatible application programming interface, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, the Jastrow factor, and the necessary derivatives also to perform variational and structural optimization. QMCkl separates algorithmic development from hardware-specific tuning by combining human-readable reference implementations with performance-optimized kernels that produce identical numerical results. The library enables consistent, efficient, and reproducible simulations across different QMC codes and architectures and achieves substantial speedups in the evaluation of the energy and its derivatives. Beyond QMC, QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools, promoting cross-code interoperability and simplifying high-performance scientific software development.

Mohsen Doust Mohammadi

Anthony Scemama

There are three different Quantum Monte Carlo (QMC) codes in the Targeting REal chemical accuracy at the eXascale (TREX) Center of Excellence (CoE), TurboRVB, Champ, and QMC=Chem, each with its own strengths and weaknesses. Instead of optimizing the three codes independently for exascale architectures, or re-writing a new monolithic code, our strategy is instead to design a new library, Quantum Monte Carlo kernel library (QMCkl), containing the state-of-the-art expertise in the implementation of specific kernels present in each of the three codes. The functions available in this library will allow all the codes to benefit from the optimal implementation of the major kernels of QMC. The three main objectives driving the development of QMCkl are performance, productivity and portability.

Kaoru Ohno, Keivan Esfarjani, Yoshiyuki Kawazoe