Ab Initio Electronic Structure Calculations with QMCPACK: A Use Case of Good Software Practices

November 14, 2022

Nov. 14, 2022 — The quantum Monte Carlo methods package (QMCPACK) is a unique simulation code that can produce ab initio electronic structure solutions for a broad range of materials with an accuracy unreachable by other methods and packages. This parallel, many-core CPU and GPU accelerated software helps to overcome the time-to-solution challenges that quantum Monte Carlo (QMC) methods face, thus opening many new research opportunities with the software’s wide applicability, high accuracy, and a unique ability to improve the accuracy of solutions even to the point of approaching exact results for some systems. Portability and exascale-capable performance make QMCPACK a unique and valuable resource to the scientific community for electronic structure modeling and cross-validation.

Performance of the new batched offload implementation in QMCPACK now rivals the legacy CUDA version. Shown is throughput relative to this version vs. number of atoms for a broad range of problems. The different datasets show the progression made with new QMCPACK versions and with improvements in LLVM’s OpenMP implementation.

An Exemplar Use Case in Using Good Software Practices to Create a Performance-Portable Framework from Academic Code

QMCPACK is a use case worthy of detailed study by other high-performance computing (HPC) software projects.

The starting point for QMCPACK is a common one in the HPC community in that the team begin with a working research grade academic parallel code. In this case, the code had been optimized for the Intel Xeon Phi many-core coprocessors. To date, the QMCPACK team has created a performance-portable framework that runs well on systems from laptops to leadership-class supercomputers and that is expected to run well on exascale and future, beyond exascale systems.

While creating an exemplar performance portable framework, the QMCPACK team has been open about their thought process and development effort. This includes the following:

  • The software selection process
  • The collaborative use by many vendors and open-source teams of Exascale Computing Project (ECP) resources to help speed up the maturation of Open Multi-Processing (OpenMP) Offload implementations for CPUs and GPUs
  • The adoption of ECP continuous integration (CI) to ensure program correctness on the many platforms supported by QMCPACK
  • The adoption of the ECP standard Spack package manager and build process to support as many platforms as possible without overburdening their developments

The QMCPACK use case highlights the value of government support for the ECP effort and the value delivered by various ECP projects to bring to fruition a sustainable, cross-platform, and performant software ecosystem for the nation’s exascale systems.

Scientific Significance

In the past, the fundamental challenges with, and high computational cost of QMC methods prevented their mass adoption. Consequently, scientists utilized approximations in methods such as the popular density-functional theory (DFT), and more computationally expensive electronic structure methods such as the GW approximation and dynamical mean-field theory. The accuracy and applicability of these approximations varies widely for different materials and chemical systems and weakens the solutions found by the packages that use them. Obtaining systematically improvable and increasingly accurate results for general systems represents a major challenge. Alternatively, quantum chemistry methods that are accurate and systematically improvable scale poorly with system size. They are not well developed for periodic systems with hundreds of electrons and are not yet suitable for describing metals.

To address these issues, the QMCPACK methods use stochastic sampling to solve the many-body Schrödinger equation. Paul Kent—a distinguished member of R&D staff at Oak Ridge National Laboratory (ORNL) and PI of the ECP’s QMCPACK project—explains, “QMC methods are not magically accurate because they do make a few approximations in solving the Schrödinger equation.” However, unlike other approaches, these approximations can be systematically improved, and in some cases they can be improved sufficiently to give nearly exact results. The QMCPACK methods also scale to big atomic systems with thousands of electrons: “By using statistical methods to solve the Schrödinger equation, large and complex systems can be studied with unprecedented accuracy—even systems in which other electronic structure methods have difficulty.”

As with any other quantum electronic structure method, the QMCPACK methods can be directly applied to materials and chemical problems of interest and can provide unprecedented accuracy and broad support for a wide range of systems. Examples of such applications include semiconductors, metals, and a variety of molecular systems. This capability creates a wealth of new scientific opportunities, as Kent explains, “There are many examples of materials for which the results are very sensitive to the accuracy of the atomic models. Examples include 2D nano materials (owing to weak van der Waals force), quantum materials, and lithium-ion battery materials. Small differences in accuracy can have big effects.”

Most significantly, QMC methods enable cross-validation of electronic structure schemes to challenge chemical, physical, and materials problems and to help guide improvements in the methodology. Looking to the future, Kent notes, “Current electronic structure databases are based on lower-fidelity results, such as the approximate-in-practice DFT. The hope is to create AI systems models based on high accuracy, guaranteed accuracy QMC results.”

To continue reading and see citations, please click here.


Source: Rob Farber, ECP

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