QMCChem

During the last three years we have been actively developing the QMC=Chem quantum Monte Carlo code. This code was initially designed for massively parallel simulations, and uses the manager/worker model.

When the program starts its execution, the manager runs on the master node and forks two other processes: a worker process and a data server. The worker is an efficient Fortran/MPI executable with minimal memory and disk space requirements (typically a few megabytes for each), where the only MPI communication is the broadcast of the input data (wave function parameters, initial positions in the 3N-space and random seed). The outline of the task of a worker is the following:

 while ( Running )
 {
    compute_a_block_of_data();
    Running = send_the_results_to_the_data_server();
 }

The data server is an XML-RPC server implemented in Python. When it receives the computed data of a worker, it replies to the worker the order given by the manager to compute another block or to stop. The received data is then stored in a database using an asynchronous I/O mechanism. The manager is always aware of the results computed by all the workers and controls the running/stopping state of the workers and the interaction of the user during the simulation.

QMC=Chem is very well suited to massive parallelism and cloud computing:

* All the implemented algorithms are CPU-bound
* All workers are totally independent
* The load balancing is optimal: the workers always work 100% of the time, independently of their respective CPU speeds
* The code was written to be as portable as possible: the manager is written in standard Python and the worker is written in standard Fortran90 with MPI.
* The network traffic is minimal and the amount of data transferred over the network can even be adjusted by the user
* The number of simultaneous worker nodes can be variable during a calculation
* Fault-tolerance can be easily implemented
* The input and output data are not presented as traditional input files and output files. All the input and output data are stored in a database and an API is provided to access the data. This allows different forms of interaction of the user: scripts, graphical user interfaces, command-line tools, web interfaces, etc.

Current Features

Methods

• VMC
• DMC
• Jastrow factor optimization
• CI coefficients optimization

Wave functions

• Single determinant
• Multi-determinant
• Multi-Jastrow
• Nuclear cusp correction

Properties

• The Electron Pair Localization Function

Links

• Easy development with the IRPF90 tool.
• The access to input/output files is provided via an API produced by the Easy Fortran I/O library generator
• Documentation

Parallel speed-up curve

Number of computed blocks in 140 seconds, where each block is composed of 50 walkers realizing 10,000 VMC steps (water molecule).

Number of processors	Number of computed blocks	Speed-up
1	5	1
8	41	8
16	81	16
32	170	32
64	321	64
128	644	128
256	1280	256
512	2417	483

Features under development

Properties

• Molecular Forces
• Moments (dipole, quadrupole,...)
• Electron density
• ZV-ZB EPLF estimator

Practical aspects

• Web interface for input and output

Input file creation

The QMC=Chem input file can be created using the web interface. Upload a Q5Cost file or an output file from GAMESS, Gaussian or Molpro, and you will download the QMC=Chem input directory.

Papers related to the QMC=Chem code

• Large-scale quantum Monte Carlo electronic structure calculations on the EGEE grid