Utilizing High Performance Computing for Chemistry: Parallel Computational Chemistry.
Abstract not provided.
Janssen, Curtis L.↗
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NWChem: scalable parallel computational chemistry
NWChem is a general purpose computational chemistry code specifically designed to run on distributed memory parallel computers. The core functionality of the code focuses on molecular dynamics, Hartree-Fock and density functional theory methods for both plane-wave basis sets as well as Gaussian basis sets, tensor contraction engine based coupled cluster capabilities and combined quantum mechanics/molecular mechanics descriptions. It was realized from the beginning that scalable implementations of these methods required a programming paradigm inherently different from what message passing approaches could offer. In response a global address space library, the Global Array Toolkit, was developed. The programming model it offers is based on using predominantly one-sided communication. This model underpins most of the functionality in NWChem and the power of it is exemplified by the fact that the code scales to tens of thousands of processors. In this paper the core capabilities of NWChem are described as well as their implementation to achieve an efficient computational chemistry code with high parallel scalability. NWChem is a modern, open source, computational chemistry code1 specifically designed for large scale parallel applications2. To meet the challenges of developing efficient, scalable and portable programs of this nature a particular code design was adopted. This code design involved two main features. First of all, the code is build up in a modular fashion so that a large variety of functionality can be integrated easily. Secondly, to facilitate writing complex parallel algorithms the Global Array toolkit was developed. This toolkit allows one to write parallel applications in a shared memory like approach, but offers additional mechanisms to exploit data locality to lower communication overheads. This framework has proven to be very successful in computational chemistry but is applicable to any engineering domain. Within the context created by the features above NWChem has grown into a general purpose computational chemistry code that supports a wide variety of energy expressions and capabilities to calculate properties based there upon. The main energy expressions are classical mechanics force fields, Hartree-Fock and DFT both for finite systems and condensed phase systems, coupled cluster, as well as QM/MM. For most energy expressions single point calculations, geometry optimizations, excited states, and other properties are available. Below we briefly discuss each of the main energy expressions and the critical points involved in scalable implementations thereof.
99 GENERAL AND MISCELLANEOUS↗
The Parareal Method for Solving Space-Dependent Neutron Kinetics Equations in Hexagonal-z Geometry
Neutron kinetics plays an important role in reactor safety and analysis. The backward Euler method is the most widely used time integration method in the calculation of space-dependent nuclear reactor kinetics. Xia (2006) used this method to discretize the derivative of flux, and adopted the analytic integration to discretize the delayed neutron precursor concentrations. Diagonally Implicit Runge-Kutta (DIRK) method owns high accuracy and excellent stability, and it was applied in the rectangle core for calculating core the nuclear reactor kinetics by Wenbo Zhao (2013). Recently, DIRK was applied to the neutron kinetics for hexagonal-z geometry application (CAI, 2016). As solving the neutron kinetics equations is very time-consuming and the number of available cores continues to increase with parallel architectures evolving, parallel algorithms need to be designed in order to effectively utilize the available resources. However, it is difficult to parallel in time axis because the later moment is strongly dependent on the previous moment. Some methods have been proposed to parallel in time algorithm over the last decades. One of the famous methods to realize time parallelism is the Parareal algorithm (Lions, 2001), which offers a promising approach to increase the degree of concurrency. More than one decade from this method first proposed, this method has been studied and applied in the many fields of applied mathematics, physics, chemistry, parallel computing, etc. Fischer (2014 applied the Parareal method in the field of the Navier-Stokes Equations. Baudron (2014) combined the Parareal method with finite element method to solve the neutron kinetics equations, where the Parareal method was implemented based on the implicit Euler method or method. In this paper, the Parareal method is studied in the hexagonal-z geometry with the help of DIRK developed for neutron kinetics equations (CAI, 2016)
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape
Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NWChem
Computational atomistic modeling has emerged as a powerful approach for fundamental studies in chemistry, materials science, geosciences, and biology. It has the ability to provide detailed insights into complex phenomena and over the years has grown into a powerful tool not only to validate experiment, but also to predict new phenomena. In order to harness computational modeling to the fullest, superior computational tools are required that not only offer a broad spectrum of capabilities, but are also capable of taking advantage of modern massively parallel computer architectures efficiently to solve real world problems. The NWChem program suite is an example along these lines. It offers a range of capabilities to perform first-principles electronic structure calculations on molecular and periodic systems, classical molecular dynamics simulations and hybrid QM/MM simulations that permits one to combine quantum and classical methods within a calculation. The code is designed to run on high-performance parallel supercomputers as well as conventional workstation clusters with the goal to provide scalable solutions for large scale atomistic simulations. It has been ported to almost all high-performance computing platforms, workstations, PCs running LINUX, as well as clusters of desktop platforms or workgroup servers. The package is scalable, both in its ability to treat large problems efficiently, and in its utilization of available parallel computing resources. The parallel framework for NWChem is provided by the Global Array (GA) toolkit developed at PNNL. NWChem is developed and maintained by the Environmental Molecular Sciences Laboratory (EMSL) located at the Pacific Northwest National Laboratory (PNNL) in Washington State. The latest release (version 6.0) of the code is distributed under the terms of the Educational Community License (ECL) version 2.0.
99 GENERAL AND MISCELLANEOUS↗
Work stealing for GPU-accelerated parallel programs in a global address space framework
Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a function of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain
97 MATHEMATICS AND COMPUTING↗
Extensible Computational Chemistry Environment
ECCE provides a sophisticated graphical user interface, scientific visualization tools, and the underlying data management framework enabling scientists to efficiently set up calculations and store, retrieve, and analyze the rapidly growing volumes of data produced by computational chemistry studies. ECCE was conceived as part of the Environmental Molecular Sciences Laboratory construction to solve the problem of researchers being able to effectively utilize complex computational chemistry codes and massively parallel high performance compute resources. Bringing the power of these codes and resources to the desktops of researcher and thus enabling world class research without users needing a detailed understanding of the inner workings of either the theoretical codes or the supercomputers needed to run them was a grand challenge problem in the original version of the EMSL. ECCE allows collaboration among researchers using a web-based data repository where the inputs and results for all calculations done within ECCE are organized. ECCE is a first of kind end-to-end problem solving environment for all phases of computational chemistry research: setting up calculations with sophisticated GUI and direct manipulation visualization tools, submitting and monitoring calculations on remote high performance supercomputers without having to be familiar with the details of using these compute resources, and performing results visualization and analysis including creating publication quality images. ECCE is a suite of tightly integrated applications that are employed as the user moves through the modeling process.
Source record↗
Extensible Computational Chemistry Environment
ECCE provides a sophisticated graphical user interface, scientific visualization tools, and the underlying data management framework enabling scientists to efficiently set up calculations and store, retrieve, and analyze the rapidly growing volumes of data produced by computational chemistry studies. ECCE was conceived as part of the Environmental Molecular Sciences Laboratory construction to solve the problem of researchers being able to effectively utilize complex computational chemistry codes and massively parallel high performance compute resources. Bringing the power of these codes and resources to the desktops of researcher and thus enabling world class research without users needing a detailed understanding of the inner workings of either the theoretical codes or the supercomputers needed to run them was a grand challenge problem in the original version of the EMSL. ECCE allows collaboration among researchers using a web-based data repository where the inputs and results for all calculations done within ECCE are organized. ECCE is a first of kind end-to-end problem solving environment for all phases of computational chemistry research: setting up calculations with sophisticated GUI and direct manipulation visualization tools, submitting and monitoring calculations on remote high performance supercomputers without having to be familiar with the details of using these compute resources, and performing results visualization and analysis including creating publication quality images. ECCE is a suite of tightly integrated applications that are employed as the user moves through the modeling process.
Source record↗
Aces4: A Platform for Computational Chemistry Calculations with Extremely Large Block-Sparse Arrays
Aces4 is a parallel programming platform comprising a DSL for Computational Chemistry and its runtime system. It offers a convenient way to express parallelism together with extensive support for extremely large, possibly sparse, distributed arrays. It aids scientists in the creation of performant, scalable, massively parallel programs that can effectively take advantage of leadership class computing systems to address important scientific questions. Aces4 has enabled the development and implementation of new methods in electronic structure theory which are breaking new ground in their ability to perform highly accurate calculations on ever larger molecular systems. In this paper the design of Aces4, which is based on the the Super Instruction Architecture approach, is described. Experimental scaling results for Molecular Cluster Perturbation Theory, a new method enabled by Aces4, and CCSD, a widely used computational chemistry method are given.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Brief Summary on Formalizing Parallel Tensor Distributions, Redistributions, and Algorithm Derivations
Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized implementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.
97 MATHEMATICS AND COMPUTING↗
Computational Investigation of the Effect of Chemistry on Mars Retropropulsion Environments using a Massively Parallel GPU Approach
In this work, we investigate the effects of chemistry on a human-scale Mars lander concept using scale-resolving computational fluid dynamics (CFD) with finite-rate chemistry and a graphics processing unit (GPU)-enabled implementation of the NASA FUN3D flow solver, enabling run-times of a few days for the simulations presented. Simulations are carried out on Summit at Oak Ridge Leadership Computing Facility using thousands of GPUs. Retropropulsion ground tests require significant compromises on physical scale, instrumentation, configuration, and environments. Ground tests of retropropulsion configurations thus far have neglected effects of chemistry due to physical constraints of wind tunnel models and facilities; most experiments use inert simulant gases at low temperatures. As such, a strong reliance on high-fidelity computational analyses such as those presented in this work is required to expand the knowledge of retropropulsion aerodynamics. An overview of the GPU approach will be presented. Results are compared to a previous scaled perfect gas (air) campaign.
retropropulsion↗
Flame-vortex interactions imaged in microgravity
The scientific objective is to obtain high quality color-enhanced digital images of a vortex exerting aerodynamic strain on premixed and nonpremixed flames with the complicating effects of buoyancy removed. The images will provide universal (buoyancy free) scaling relations that are required to improve several types of models of turbulent combustion, including KIVA-3, discrete vortex, and large-eddy simulations. The images will be used to help quantify several source terms in the models, including those due to flame stretch, flame-generated vorticity, flame curvature, and preferential diffusion, for a range of vortex sizes and flame conditions. The experiment is an ideal way to study turbulence-chemistry interactions and isolate the effect of vortices of different sizes and strengths in a repeatable manner. A parallel computational effort is being conducted which considers full chemistry and preferential diffusion.
Driscoll, James F.↗
On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters
The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.
97 MATHEMATICS AND COMPUTING↗
MPQC: Performance Analysis and Optimization
MPQC (Massively Parallel Quantum Chemistry) is a widely used computational quantum chemistry code. It is capable of performing a number of computations commonly occurring in quantum chemistry. In order to achieve better performance of MPQC, in this report we present a detailed performance analysis of this code. We then perform loop and memory access optimizations, and measure performance improvements by comparing the performance of the optimized code with that of the original MPQC code. We observe that the optimized MPQC code achieves a significant improvement in the performance through a better utilization of vector processing and memory hierarchies.
97 MATHEMATICS AND COMPUTING↗
TChem-atm v1.0
SAND2024-11300O TChem-atm is a software library that was developed to solve complex kinetic models for atmospheric chemistry applications. TChem-atm interface employs a hierarchical parallelism design to exploit the massive parallelism available from modern computing platforms. It also supports gas atmospheric chemistry applications, e.g., the energy exascale earth system model. TChem can be used as a box model or coupled with a climate model to compute the time evolution of gas tracer species. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Safta, Cosmin↗
DOE Project 18545, AOP Task 2.0B, CRADA with Reaction Design
We ran 5 FACE fuels and 8 surrogate blends in diesel combustion with detailed particulate and exhaust chemistry measurements to provide data needed to develop and evaluate a kinetic model for particulate formation. Surrogate blends duplicated engine performance of real fuels. We demonstrated that a simple 2 surrogate blend is capable of duplicating the range of engine response for the FACE fuels, but that further tuning and complexity will be needed to reproduce emissions. We assisted in setting up a Jaguar computer user program for bench marking parallel solvers for chemistry in GPU machine environment. This program has just been approved by the Jaguar user facility and will begin in 2012.
10 SYNTHETIC FUELS↗
DFT modeling of adsorption onto uranium metal using large-scale parallel computing
There is a dearth of atomistic simulations involving the surface chemistry of 7-uranium which is of interest as the key fuel component of a breeder-burner stage in future fuel cycles. Recent availability of high-performance computing hardware and software has rendered extended quantum chemical surface simulations involving actinides feasible. With that motivation, data for bulk and surface 7-phase uranium metal are calculated in the plane-wave pseudopotential density functional theory method. Chemisorption of atomic hydrogen and oxygen on several un-relaxed low-index faces of 7-uranium is considered. The optimal adsorption sites (calculated cohesive energies) on the (100), (110), and (111) faces are found to be the one-coordinated top site (8.8 eV), four-coordinated center site (9.9 eV), and one-coordinated top 1 site (7.9 eV) respectively, for oxygen; and the four-coordinated center site (2.7 eV), four-coordinated center site (3.1 eV), and three-coordinated top2 site (3.2 eV) for hydrogen. (authors)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗