Features of QMCPACK

Production-level features

The following list contains the main production-level features of QMCPACK. If you do not see a specific feature that you are interested in, see the remainder of this manual and ask whether that specific feature is available or can be quickly brought to the full production level.

  • Variational Monte Carlo (VMC)

  • Diffusion Monte Carlo (DMC)

  • Reptation Monte Carlo

  • Single and multideterminant Slater Jastrow wavefunctions

  • Wavefunction updates using optimized multideterminant algorithm of Clark et al.

  • Backflow wavefunctions

  • One, two, and three-body Jastrow factors

  • Excited state calculations via flexible occupancy assignment of Slater determinants

  • All electron and nonlocal pseudopotential calculations

  • Casula T-moves for variational evaluation of nonlocal pseudopotentials (non-size-consistent and size-consistent variants)

  • Wavefunction optimization using the “linear method” of Umrigar and coworkers, with arbitrary mix of variance and energy in the objective function

  • Blocked, low memory adaptive shift optimizer of Zhao and Neuscamman

  • Gaussian, Slater, plane-wave, and real-space spline basis sets for orbitals

  • Interface and conversion utilities for plane-wave wavefunctions from Quantum Espresso (Plane-Wave Self-Consistent Field package [PWSCF])

  • Interface and conversion utilities for Gaussian-basis wavefunctions from GAMESS

  • Easy extension and interfacing to other electronic structure codes via standardized XML and HDF5 inputs

  • MPI parallelism

  • Fully threaded using OpenMP

  • GPU (NVIDIA CUDA) implementation (limited functionality)

  • HDF5 input/output for large data

  • Nexus: advanced workflow tool to automate all aspects of QMC calculation from initial DFT calculations through to final analysis

  • Analysis tools for minimal environments (Perl only) through to Python-based environments with graphs produced via matplotlib (included with Nexus)

SoA optimizations and improved algorithms

The Structure-of-Arrays (SoA) optimizations [MLC+17] are a set of improved data layouts facilitating vectorization on modern CPUs with wide SIMD units. For many calculations and architectures, the SoA implementation more than doubles the speed of the code. This so-called SoA implementation replaces the older, less efficient Array-of-Structures (AoS) code and can be enabled or disabled at compile time. The memory footprint is also reduced in the SoA implementation by better algorithms, enabling more systems to be run.

The SoA build was made the default for QMCPACK v3.7.0. As described in Configuration Options, the SoA implementation can be disabled by configuring with -DENABLE_SOA=0.

The SoA code path currently does not support:

  • Backflow wavefunctions

  • Many observables

The code should abort with a message referring to AoS vs SoA features if any unsupported feature is invoked. In this case the AoS build should be used by configuring with -DENABLE_SOA=0. In addition, please inform the developers via GitHub or Google Groups so that porting these features can be prioritized.

Core features are heavily tested in both SoA and AoS versions. If using untested and noncore features in the SoA code, please compare the AoS and SoA results carefully.

Supported GPU features

The GPU implementation supports multiple GPUs per node, with MPI tasks assigned in a round-robin order to the GPUs. Currently, for large runs, 1 MPI task should be used per GPU per node. For smaller calculations, use of multiple MPI tasks per GPU might yield improved performance. Supported GPU features:

  • VMC, wavefunction optimization, DMC.

  • Periodic and open boundary conditions. Mixed boundary conditions are not yet supported.

  • Wavefunctions:

    1. Single Slater determinants with 3D B-spline orbitals. Twist-averaged boundary conditions and complex wavefunctions are fully supported. Gaussian type orbitals are not yet supported.

    2. Hybrid mixed basis representation in which orbitals are represented as 1D splines times spherical harmonics in spherical regions (muffin tins) around atoms and 3D B-splines in the interstitial region.

    3. One-body and two-body Jastrows represented as 1D B-splines. Three-body Jastrow functions are not yet supported.

  • Semilocal (nonlocal and local) pseudopotentials, Coulomb interaction (electron-electron, electron-ion), and model periodic Coulomb (MPC) interaction.

Beta test features

This section describes developmental features in QMCPACK that might be ready for production but that require additional testing, features, or documentation to be ready for general use. We describe them here because they offer significant benefits and are well tested in specific cases.

Auxiliary-Field Quantum Monte Carlo

The orbital-space Auxiliary-Field Quantum Monte Carlo (AFMQC) method is now available in QMCPACK. The main input for the code is the matrix elements of the Hamiltonian in a given single particle basis set, which must be produced from mean-field calculations such as Hartree-Fock or density functional theory. The code and many features are in development. Check the latest version of QMCPACK for an up-to-date description of available features. A partial list of the current capabilities of the code follows. For a detailed description of the available features, see Auxiliary-Field Quantum Monte Carlo.

  • Phaseless AFQMC algorithm of Zhang et al. (S. Zhang and H. Krakauer. 2003. “Quantum Monte Carlo Method using Phase-Free Random Walks with Slater Determinants.” PRL 90: 136401).

  • “Hybrid” and “local energy” propagation schemes.

  • Hamiltonian matrix elements from (1) Molpro’s FCIDUMP format (which can be produced by Molpro, PySCF, and VASP) and (2) internal HDF5 format produced by PySCF (see AFQMC section below).

  • AFQMC calculations with RHF (closed-shell doubly occupied), ROHF (open-shell doubly occupied), and UHF (spin polarized broken symmetry) symmetry.

  • Single and multideterminant trial wavefunctions. Multideterminant expansions with either orthogonal or nonorthogonal determinants.

  • Fast update scheme for orthogonal multideterminant expansions.

  • Distributed propagation algorithms for large systems. Enables calculations where data structures do not fit on a single node.

  • Complex implementation for PBC calculations with complex integrals.

  • Sparse representation of large matrices for reduced memory usage.

  • Mixed and back-propagated estimators.

  • Specialized implementation for solids with k-point symmetry (e.g. primitive unit cells with kpoints).

  • Efficient GPU implementation (currently limited to solids with k-point symmetry).

Sharing of spline data across multiple GPUs

Sharing of GPU spline data enables distribution of the data across multiple GPUs on a given computational node. For example, on a two-GPU-per-node system, each GPU would have half of the orbitals. This allows use of larger overall spline tables than would fit in the memory of individual GPUs and potentially up to the total GPU memory on a node. To obtain high performance, large electron counts or a high-performing CPU-GPU interconnect is required.

To use this feature, the following needs to be done:

  • The CUDA Multi-Process Service (MPS) needs to be used (e.g., on OLCF Summit/SummitDev use “-alloc_flags gpumps” for bsub). If MPI is not detected, sharing will be disabled.

  • CUDA_VISIBLE_DEVICES needs to be properly set to control each rank’s visible CUDA devices (e.g., on OLCF Summit/SummitDev create a resource set containing all GPUs with the respective number of ranks with “jsrun –task-per-rs Ngpus -g Ngpus”).

  • In the determinant set definition of the <wavefunction> section, the “gpusharing” parameter needs to be set (i.e., <determinantset gpusharing=“yes”>). See Spline basis sets.


Amrita Mathuriya, Ye Luo, Raymond C. Clay, III, Anouar Benali, Luke Shulenburger, and Jeongnim Kim. Embracing a new era of highly efficient and productive quantum monte carlo simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ‘17, 38:1–38:12. New York, NY, USA, 2017. ACM. URL: http://doi.acm.org/10.1145/3126908.3126952, doi:10.1145/3126908.3126952.