Embracing a new era of highly efficient and productive quantum Monte Carlo simulations (original) (raw)
Related papers
Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters
Computing in Science & Engineering, 2000
Continuum quantum Monte Carlo (QMC) has proved to be an invaluable tool for predicting the properties of matter from fundamental principles. By solving the many-body Schrödinger equation through a stochastic projection, it achieves greater accuracy than mean-field methods and much better scalability than quantum chemical methods, enabling scientific discovery across a broad spectrum of disciplines. The multiple forms of parallelism afforded by QMC algorithms make them ideal candidates for acceleration in the many-core paradigm. We present the results of our effort to port the QMCPACK simulation code to the NVIDIA CUDA GPU platform. We restructure the CPU algorithms to express additional parallelism, minimize GPU-CPU communication, and efficiently utilize the GPU memory hierarchy. Using mixed precision on GT200 GPUs and MPI for intercommunication and load balancing, we observe typical full-application speedups of approximately 10x to 15x relative to quad-core Xeon CPUs alone, while reproducing the double-precision CPU results within statistical error.
FPGA acceleration of a quantum Monte Carlo application
Parallel Computing, 2008
Quantum Monte Carlo methods enable us to determine the ground-state properties of atomic or molecular clusters. Here, we present a reconfigurable computing architecture using Field Programmable Gate Arrays (FPGAs) to accelerate two computationally intensive kernels of a Quantum Monte Carlo (QMC) application applied to N-body systems. We focus on two key kernels of the QMC application: acceleration of potential energy and wave function calculations. We compare the performance of our application on two reconfigurable platforms. Firstly, we use a dual-processor 2.4 GHz Intel Xeon augmented with two reconfigurable development boards consisting of Xilinx Virtex-II Pro FPGAs. Using this platform, we achieve a speedup of 3Â over a software-only implementation. Following this, the chemistry application is ported to the Cray XD1 supercomputer equipped with Xilinx Virtex-II Pro and Virtex-4 FPGAs. The hardware-accelerated application on one node of the high performance system equipped with a single Virtex-4 FPGA yields a speedup of approximately 25Â over the serial reference code running on one node of the dual-processor dual-core 2.2 GHz AMD Opteron. This speedup is mainly attributed to the use of pipelining, the use of fixedpoint arithmetic for all calculations and the fine-grained parallelism using FPGAs. We can further enhance the performance by operating multiple instances of our design in parallel.
Adaptive Computing Library for Quantum Monte Carlo Simulations
International Journal of Computer Theory and Engineering, 2014
Quantum Monte Carlo (QMC) methods are used in many scientific computer simulation as their core kernels. The implementation of QMC for distributed NUMA clusters may have load balancing issues at petascale level because of its random nature. We are studying on a simulation for inhomogeneous ultra-cold atoms on optical lattice, for which we developed a QMC algorithm with hybrid MPI+OpenMP programming model. This hybrid model uses the nested parallelism such that the outer loops are parallelized by MPI, while the inner loop relies on OpenMP parallelism. In this work, we presented an adaptive computing approach which learns the system work load dynamically by using our Adaptive Computing Library at run-time and then creates sufficient amount of OpenMP threads based on the availability of the system resources during the execution. The implementation shows that our adaptive approach can get very good load balancing without unnecessary overheads and can significantly provide performance increases up to 20% increases in comparison to MPI-only implementation on a XE6m Cray super computer.
QWalk: A quantum Monte Carlo program for electronic structure
Journal of Computational Physics, 2009
We describe QWalk, a new computational package capable of performing Quantum Monte Carlo electronic structure calculations for molecules and solids with many electrons. We describe the structure of the program and its implementation of Quantum Monte Carlo methods. It is opensource, licensed under the GPL, and available at the web site
2016 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), 2016
An extremely scalable linear-algebraic algorithm was developed for quantum material simulation (electronic state calculation) with 10$^8$ atoms or 100-nm-scale materials. The mathematical foundation is generalized shifted linear equations ((zB - A) x = b), instead of conventional generalized eigenvalue equations. The method has a highly parallelizable mathematical structure. The fundamental theory is mathematical and is applicable also to other scientific fields. The benchmark shows an extreme strong scaling and a qualified time-to-solution on the full system of the K computer. The method was demonstrated in a real material research for ultra-flexible (organic) devices, key devices of next-generation IoT products. The present paper shows that an innovative scalable algorithm for a real research can appear by the co-design among application, algorithm and architecture.
Journal of physics. Condensed matter : an Institute of Physics journal, 2018
QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit (CPU) and graphical processing unit (GPU) systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The p...
Zori 1.0: A parallel quantum Monte Carlo electronic structure package
Journal of Computational Chemistry, 2005
The Zori 1.0 package for electronic structure computations is described. Zori performs variational and diffusion Monte Carlo computations as well as correlated wave function optimization. This article presents an overview of the implemented methods and code capabilities.
qGMC-Analyzer -- Quantum Simulation on Multicore Architectures
2013 2nd Workshop-School on Theoretical Computer Science, 2013
This paper presents an extension of the VPE-qGM execution environment in order to support parallel execution using OpenMP. As a relevant part of a broader project represented by the simulation framework D-GM which aims to achieve quantum simulation on hybrid architectures (clusters, multicore CPUs and GPUs), this work enables the control and execution of the simulation from multicore architectures. This proposal is shown by providing relevant management simulation on multicore CPUs, which will be later combined with GPU targeted implementations. From the synergy between these implementations, this work aims to establish a general platform for hybrid simulation, exploring different architectures usually found in clusters and expanding the capabilities of the simulation environment VPE-qGM.
Hybrid algorithms in quantum Monte Carlo
Journal of Physics: Conference Series, 2012
With advances in algorithms and growing computing powers, quantum Monte Carlo (QMC) methods have become a leading contender for high accuracy calculations for the electronic structure of realistic systems. The performance gain on recent HPC systems is largely driven by increasing parallelism: the number of compute cores of a SMP and the number of SMPs have been going up, as the Top500 list attests. However, the available memory as well as the communication and memory bandwidth per element has not kept pace with the increasing parallelism. This severely limits the applicability of QMC and the problem size it can handle. OpenMP/MPI hybrid programming provides applications with simple but effective solutions to overcome efficiency and scalability bottlenecks on large-scale clusters based on multi/many-core SMPs. We discuss the design and implementation of hybrid methods in QMCPACK and analyze its performance on current HPC platforms characterized by various memory and communication hierarchies.
Electronic structure quantum Monte Carlo
Acta Physica Slovaca. Reviews and Tutorials, 2000
Quantum Monte Carlo (QMC) is an advanced simulation methodology for studies of manybody quantum systems. The QMC approaches combine analytical insights with stochastic computational techniques for efficient solution of several classes of important many-body problems such as the stationary Schrödinger equation. QMC methods of various flavors have been applied to a great variety of systems spanning continuous and lattice quantum models, molecular and condensed systems, BEC-BCS ultracold condensates, nuclei, etc. In this review, we focus on the electronic structure QMC, i.e., methods relevant for systems described by the electron-ion Hamiltonians. Some of the key QMC achievements include direct treatment of electron correlation, accuracy in predicting energy differences and favorable scaling in the system size. Calculations of atoms, molecules, clusters and solids have demonstrated QMC applicability to real systems with hundreds of electrons while providing 90-95% of the correlation energy and energy differences typically within a few percent of experiments. Advances in accuracy beyond these limits are hampered by the so-called fixed-node approximation which is used to circumvent the notorious fermion sign problem. Many-body nodes of fermion states and their properties have therefore become one of the important topics for further progress in predictive power and efficiency of QMC calculations. Some of our recent results on the wave function nodes and related nodal domain topologies will be briefly reviewed. This includes analysis of few-electron systems and descriptions of exact and approximate nodes using transformations and projections of the highly-dimensional nodal hypersurfaces into the 3D space. Studies of fermion nodes offer new insights into topological properties of eigenstates such as explicit demonstrations that generic fermionic ground states exhibit the minimal number of two nodal domains. Recently proposed trial wave functions based on pfaffians with pairing orbitals are presented and their nodal properties are tested in calculations of first row atoms and molecules. Finally, backflow "dressed" coordinates are introduced as another possibility for capturing correlation effects and for decreasing the fixed-node bias. detailed analysis of theoretical ideas. Indeed, QMC is very much in the line of "it from bit" paradigm, alongside, for example, of substantional computational efforts in quantum chromodynamics which not only predict hadron masses but, at the same time, contribute to the validation of the fundamental theory. Similar simulations efforts exist in other areas of physics as well. Just a few decades ago it was almost unthinkable that one would be able to solve Schrödinger equation for hundreds of electrons in an explicit, many-body wave function framework. Today, such calculations are feasible using available computational resources. At the same time, much more remains to be done, of course, to make the methods more insightful, more efficient and their application less laborious. We hope this overview will contribute to the growing interest in this rapidly developing field of research.