DOCSLIB.ORG

High-Performance Algorithms and Software for Large-Scale Molecular Simulation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
High-Performance Algorithms and Software for Large-Scale Molecular Simulation HIGH-PERFORMANCE ALGORITHMS AND SOFTWARE FOR LARGE-SCALE MOLECULAR SIMULATION A Thesis Presented to The Academic Faculty by Xing Liu In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computational Science and Engineering Georgia Institute of Technology May 2015 Copyright ⃝c 2015 by Xing Liu HIGH-PERFORMANCE ALGORITHMS AND SOFTWARE FOR LARGE-SCALE MOLECULAR SIMULATION Approved by: Professor Edmond Chow, Professor Richard Vuduc Committee Chair School of Computational Science and School of Computational Science and Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Edmond Chow, Advisor Professor C. David Sherrill School of Computational Science and School of Chemistry and Biochemistry Engineering Georgia Institute of Technology Georgia Institute of Technology Professor David A. Bader Professor Jeffrey Skolnick School of Computational Science and Center for the Study of Systems Biology Engineering Georgia Institute of Technology Georgia Institute of Technology Date Approved: 10 December 2014 To my wife, Ying Huang the woman of my life. iii ACKNOWLEDGEMENTS I would like to first extend my deepest gratitude to my advisor, Dr. Edmond Chow, for his expertise, valuable time and unwavering support throughout my PhD study. I would also like to sincerely thank Dr. David A. Bader for recruiting me into Georgia Tech and inviting me to join in this interesting research area. My appreciation is extended to my committee members, Dr. Richard Vuduc, Dr. C. David Sherrill and Dr. Jeffrey Skolnick, for their advice and helpful discussions during my research. Similarly, I want to thank all of the faculty and staff in the School of Compu- tational Science and Engineering at Georgia Tech. I am also thankful for the support and guidance of Dr. Mikhail Smelyanskiy and Dr. Pradeep Dubey when I was interning at Intel Labs. To my friends at Georgia Tech, Jee Choi, Kent Czechowski, Marat Dukhan, Oded Green, Adam McLaughlin, Robert McColl, Lluis Miquel Munguia, Aftab Patel, Piyush Sao, Zhichen Xia, and Zhaoming Yin, thanks for helping me in many ways and their in- spiring discussions on research (and other things). Finally, and most importantly, my recognition goes out to my family, specially to my beloved wife, Ying Huang, for their support, encouragement and patience during my pur- suit of PhD. Thanks for their faith in me and allowing me to be as ambitious as I wanted. Without their constant support and encouragement, I would not have completed the PhD program. iv Contents DEDICATION ..................................... iii ACKNOWLEDGEMENTS .............................. iv LIST OF TABLES .................................. x LIST OF FIGURES .................................. xii SUMMARY ...................................... xv I INTRODUCTION ................................ 1 1.1 Background . 1 1.2 Motivation . 5 II SCALABLE DISTRIBUTED PARALLEL ALGORITHMS FOR FOCK MA- TRIX CONSTRUCTION ............................ 7 2.1 Background: Hartree-Fock Method and Fock Matrix Construction . 7 2.1.1 Hartree-Fock Equations . 7 2.1.2 Hartree-Fock Algorithm . 9 2.1.3 Electron Repulsion Integrals . 10 2.1.4 Screening . 11 2.2 Challenges of Parallelizing Fock Matrix Construction . 13 2.3 Limitations of Previous Work . 14 2.4 New Algorithm for Parallel Fock Matrix Construction . 16 2.4.1 Overview . 16 2.4.2 Task Description . 17 2.4.3 Initial Static Partitioning . 18 2.4.4 Shell Reordering . 19 2.4.5 Algorithm . 20 2.4.6 Work-Stealing Scheduler . 22 2.4.7 Performance Model and Analysis . 22 2.5 Heterogeneous Fock Matrix Construction . 26 v 2.6 Experimental Results . 28 2.6.1 Experimental Setup . 28 2.6.2 Performance of Heterogeneous Fock Matrix Construction . 29 2.6.3 Performance of Distributed Fock Matrix Construction . 30 2.6.4 Analysis of Parallel Overhead . 33 2.6.5 Load Balance Results . 33 2.7 Summary . 35 III HARTREE-FOCK CALCULATIONS ON LARGE-SCALE DISTRIBUTED SYSTEMS ..................................... 36 3.1 Current State-of-the-Art . 36 3.2 Improving Parallel Scalability of Fock Matrix Construction . 37 3.3 Optimization of Integral Calculations . 38 3.4 Computation of the Density Matrix . 41 3.5 Experimental Results . 43 3.5.1 Experimental Setup . 43 3.5.2 Scaling Results . 44 3.5.3 Comparison to NWChem . 48 3.5.4 HF Strong Scaling Results . 48 3.5.5 HF Weak Scaling Results . 49 3.5.6 Flop Rate . 50 3.6 Summary . 53 IV “MATRIX-FREE” ALGORITHM FOR HYDRODYNAMIC BROWNIAN SIMULATIONS ................................. 55 4.1 Background: Conventional Ewald BD Algorithm . 56 4.1.1 Brownian Dynamics with Hydrodynamic Interactions . 56 4.1.2 Ewald Summation of the RPY Tensor . 56 4.1.3 Brownian Displacements . 58 4.1.4 Ewald BD Algorithm . 58 4.2 Motivation . 58 vi 4.3 Related Work . 60 4.4 Matrix-Free BD Algorithm . 60 4.4.1 Particle-Mesh Ewald for the RPY Tensor . 61 4.4.2 Computing Brownian Displacements with PME . 65 4.4.3 Matrix-Free BD Algorithm . 66 4.5 Hybrid Implementation of PME . 66 4.5.1 Reformulating the Reciprocal-Space Calculation . 66 4.5.2 Optimizing the Reciprocal-Space Calculation . 68 4.5.3 Computation of Real-Space Terms . 71 4.5.4 Performance Modelling and Analysis . 71 4.5.5 Hybrid Implementation on Intel Xeon Phi . 73 4.6 Experimental Results . 75 4.6.1 Experimental Setup . 75 4.6.2 Accuracy of the Matrix-Free BD Algorithm . 76 4.6.3 Simulation Configurations . 77 4.6.4 Performance of PME . 78 4.6.5 Performance of BD Simulations . 81 4.7 Summary . 84 V IMPROVING THE PERFORMANCE OF STOKESIAN DYNAMICS SIM- ULATIONS VIA MULTIPLE RIGHT-HAND SIDES ............. 86 5.1 Motivation . 86 5.2 Background: Stokesian Dynamics . 88 5.2.1 Governing Equations . 88 5.2.2 Resistance Matrix . 89 5.2.3 Brownian Forces . 90 5.2.4 SD Algorithm . 90 5.3 Exploiting Multiple Right-Hand Sides in Stokesian Dynamics . 91 5.4 Generalized Sparse Matrix-Vector Products with Multiple Vectors . 93 5.4.1 Performance Optimizations for SPIV . 94 vii 5.4.2 Performance Model . 96 5.4.3 Experimental Setup . 99 5.4.4 Experimental Results . 100 5.5 Stokesian Dynamics Results . 104 5.5.1 Simulation Setup . 105 5.5.2 Experimental Results . 106 5.6 Summary . 113 VI EFFICIENT SPARSE MATRIX-VECTOR MULTIPLICATION ON X86-BASED MANY-CORE PROCESSORS ......................... 116 6.1 Related Work . 116 6.2 Understanding the Performance of SpMV on Intel Xeon Phi . 118 6.2.1 Test Matrices and Platform . 118 6.2.2 Overview of CSR Kernel . 119 6.2.3 Performance Bounds . 120 6.2.4 Performance Bottlenecks . 121 6.3 Ellpack Sparse Block Format . 125 6.3.1 Motivation . 125 6.3.2 Proposed Matrix Format . 128 6.3.3 SpMV Kernel with ESB Format . 132 6.3.4 Selecting c and w . 133 6.4 Load Balancers for SpMV on Intel Xeon Phi . 134 6.4.1 Static Partitioning of Cache Misses . 135 6.4.2 Hybrid Dynamic Scheduler . 135 6.4.3 Adaptive Load Balancer . 136 6.5 Experimental Results . 137 6.5.1 Load Balancing Results . 137 6.5.2 ESB Results . 138 6.5.3 Performance Comparison . 140 6.6 Summary . 142 viii VII CONCLUSIONS ................................. 144 Appendix A — OVERVIEW OF INTEL XEON PHI .............. 147 Appendix B — GTFOCK DISTRIBUTED FRAMEWORK .......... 151 Appendix C — STOKESDT TOOLKIT ..................... 157 REFERENCES ..................................... 161 VITA .......................................... 171 ix List of Tables 1 Common computational problems in molecular simulation. 3 2 Test molecules. 29 3 Speedup compared to single socket Westmere (WSM) processor. 30 4 Fock matrix construction time (in seconds) for GTFock and NWChem on four test cases. Although NWChem is faster for smaller core counts, GT- Fock is faster for larger core counts. 30 5 Speedup in Fock matrix construction for GTFock and NWChem on four test cases, using the data in the previous table. Speedup for both GTFock and NWChem is computed using the fastest 12-core running time, which is from NWChem. GTFock has better speedup at 3888 cores. 31 6 Average time, tint, for computing each ERI for GTFock (using the ERD library) and NWChem. 31 7 Average Global Arrays communication volume (MB) per MPI process for GTFock and NWChem. 34 8 Average number of calls to Global Arrays communication functions per MPI process for GTFock and NWChem. 34 9 Load balance ratio l = Tf ock;max=Tf ock;avg for four test molecules. A value of 1:000 indicates perfect load balance. 34 10 Test molecules. 45 11 Timings (seconds) for 1hsg 180 on Tianhe-2. Top half of table is CPU-only mode; bottom half is heterogeneous mode. 50 12 Timing data (seconds) used to approximate weak scaling Tianhe-2. Top half.
Load more

Recommended publications
  • Free and Open Source Software for Computational Chemistry Education
    Free and Open Source Software for Computational Chemistry Education Susi Lehtola∗,y and Antti J. Karttunenz yMolecular Sciences Software Institute, Blacksburg, Virginia 24061, United States zDepartment of Chemistry and Materials Science, Aalto University, Espoo, Finland E-mail: [email protected].fi Abstract Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. We point out the existence of a variety of free and open source software (FOSS) packages for computational chemistry that offer a wide range of functionality all the way from approximate semiempirical calculations with tight- binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. By their very definition, FOSS packages allow usage for whatever purpose by anyone, meaning they can also be used in industrial applications without limitation. Also, FOSS software has no limitations to redistribution in source or binary form, allowing their easy distribution and installation by third parties. Many FOSS scientific software packages are available as part of popular Linux distributions, and other package managers such as pip and conda. Combined with the remarkable increase in the power of personal devices—which rival that of the fastest supercomputers in the world of the 1990s—a decentralized model for teaching computational chemistry is now possible, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device 1 (BYOD) scheme. In addition to the programs’ use for various applications, open access to the programs’ source code also enables comprehensive teaching strategies, as actual algorithms’ implementations can be used in teaching.
    [Show full text]
  • Open Babel Documentation Release 2.3.1
    Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options .................................................
    [Show full text]
  • In Quantum Chemistry
    http://www.cca-forum.org Computational Quality of Service (CQoS) in Quantum Chemistry Joseph Kenny1, Kevin Huck2, Li Li3, Lois Curfman McInnes3, Heather Netzloff4, Boyana Norris3, Meng-Shiou Wu4, Alexander Gaenko4 , and Hirotoshi Mori5 1Sandia National Laboratories, 2University of Oregon, 3Argonne National Laboratory, 4Ames Laboratory, 5Ochanomizu University, Japan This work is a collaboration among participants in the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS), Performance Engineering Research Institute (PERI), Quantum Chemistry Science Application Partnership (QCSAP), and the Tuning and Analysis Utilities (TAU) group at the University of Oregon. Quantum Chemistry and the CQoS in Quantum Chemistry: Motivation and Approach Common Component Architecture (CCA) Motivation: CQoS Approach: CCA Overview: • QCSAP Challenges: How, during runtime, can we make the best choices • Overall: Develop infrastructure for dynamic component adaptivity, i.e., • The CCA Forum provides a specification and software tools for the for reliability, accuracy, and performance of interoperable quantum composing, substituting, and reconfiguring running CCA component development of high-performance components. chemistry components based on NWChem, MPQC, and GAMESS? applications in response to changing conditions – Performance, accuracy, mathematical consistency, reliability, etc. • Components = Composition – When several QC components provide the same functionality, what • Approach: Develop CQoS tools for – A component is a unit
    [Show full text]
  • Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published
    Best Practices: Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published • Abaqus • COSMO • HPCC • Nekbone • RFD tNavigator • ABySS • CP2K • HPCG • NEMO • SNAP • AcuSolve • CPMD • HYCOM • NWChem • SPECFEM3D • Amber • Dacapo • ICON • Octopus • STAR-CCM+ • AMG • Desmond • Lattice QCD • OpenAtom • STAR-CD • AMR • DL-POLY • LAMMPS • OpenFOAM • VASP • ANSYS CFX • Eclipse • LS-DYNA • OpenMX • WRF • ANSYS Fluent • FLOW-3D • miniFE • OptiStruct • ANSYS Mechanical• GADGET-2 • MILC • PAM-CRASH / VPS • BQCD • Graph500 • MSC Nastran • PARATEC • BSMBench • GROMACS • MR Bayes • Pretty Fast Analysis • CAM-SE • Himeno • MM5 • PFLOTRAN • CCSM 4.0 • HIT3D • MPQC • Quantum ESPRESSO • CESM • HOOMD-blue • NAMD • RADIOSS For more information, visit: http://www.hpcadvisorycouncil.com/best_practices.php 2 35 Applications Installation Best Practices Published • Adaptive Mesh Refinement (AMR) • ESI PAM-CRASH / VPS 2013.1 • NEMO • Amber (for GPU/CUDA) • GADGET-2 • NWChem • Amber (for CPU) • GROMACS 5.1.2 • Octopus • ANSYS Fluent 15.0.7 • GROMACS 4.5.4 • OpenFOAM • ANSYS Fluent 17.1 • GROMACS 5.0.4 (GPU/CUDA) • OpenMX • BQCD • Himeno • PyFR • CASTEP 16.1 • HOOMD Blue • Quantum ESPRESSO 4.1.2 • CESM • LAMMPS • Quantum ESPRESSO 5.1.1 • CP2K • LAMMPS-KOKKOS • Quantum ESPRESSO 5.3.0 • CPMD • LS-DYNA • WRF 3.2.1 • DL-POLY 4 • MrBayes • WRF 3.8 • ESI PAM-CRASH 2015.1 • NAMD For more information, visit: http://www.hpcadvisorycouncil.com/subgroups_hpc_works.php 3 HPC Advisory Council HPC Center HPE Apollo 6000 HPE ProLiant
    [Show full text]
  • Core Software Blocks in Quantum Chemistry: Tensors and Integrals Workshop Program
    Core Software Blocks in Quantum Chemistry: Tensors and Integrals Workshop Program Start: Sunday, May 7, 2017 afternoon. Resort check-in at 4:00 pm. Finish: Wednesday, May 10, noon Lectures are in Scripps, posters are in Heather. Sunday 6:00-7:00: Dinner 7:30-9:00 Opening session 7:30–7:45 Anna Krylov (USC): “MolSSI and some lessons from previous workshop, goals of the workshop” 7:45-8:00 Theresa Windus (Iowa): “Mission of the Molecular Science Consortium” 8:00-9:00 Introduction of participants: 2 min presentation, can have one slide (send in advance) 9:00-10:30 Reception and posters Monday Breakfast: 7:30-9:00 9:00-11:45 Session I: Overview of tensors projects and current developments (moderator Daniel Smith) 9:00-9:10 Daniel Smith (MolSSI): Overview of tensor projects 9:10-9:30 Evgeny Epifanovsky (Q-Chem): Overview of Libtensor 9:30-9:50 Ed Solomonik: "An Overview of Cyclops Tensor Framework" 9:50-10:10 Ed Valeev (VT): "TiledArray: A composable massively parallel block-sparse tensor framework" 10:10-10:30 Coffee break 10:30-10:50 Devin Matthews (UT Austin): "Aquarius and TBLIS: Orthogonal Axes in Multilinear Algebra" 10:50-11:10 Karol Kowalskii (PNNL, NWChem): "NWChem, NWChemEX , and new tensor algebra systems for many-body methods" 11:10-11:30 Peng Chong (VT): "Many-body toolkit in re-designed Massively Parallel Quantum Chemistry package" 11:30-11:55 Moderated discussion: "What problems are we *still* solving?” Lunch: 12:00-1:00 Free time for unstructured discussions 5:00 Posters (coffee/tea) Dinner: 6-7:00 7:15-9:00 Session II: Computer
    [Show full text]
  • A Summary of ERCAP Survey of the Users of Top Chemistry Codes
    A survey of codes and algorithms used in NERSC chemical science allocations Lin-Wang Wang NERSC System Architecture Team Lawrence Berkeley National Laboratory We have analyzed the codes and their usages in the NERSC allocations in chemical science category. This is done mainly based on the ERCAP NERSC allocation data. While the MPP hours are based on actually ERCAP award for each account, the time spent on each code within an account is estimated based on the user’s estimated partition (if the user provided such estimation), or based on an equal partition among the codes within an account (if the user did not provide the partition estimation). Everything is based on 2007 allocation, before the computer time of Franklin machine is allocated. Besides the ERCAP data analysis, we have also conducted a direct user survey via email for a few most heavily used codes. We have received responses from 10 users. The user survey not only provide us with the code usage for MPP hours, more importantly, it provides us with information on how the users use their codes, e.g., on which machine, on how many processors, and how long are their simulations? We have the following observations based on our analysis. (1) There are 48 accounts under chemistry category. This is only second to the material science category. The total MPP allocation for these 48 accounts is 7.7 million hours. This is about 12% of the 66.7 MPP hours annually available for the whole NERSC facility (not accounting Franklin). The allocation is very tight. The majority of the accounts are only awarded less than half of what they requested for.
    [Show full text]
  • Massive-Parallel Implementation of the Resolution-Of-Identity Coupled
    Article Cite This: J. Chem. Theory Comput. 2019, 15, 4721−4734 pubs.acs.org/JCTC Massive-Parallel Implementation of the Resolution-of-Identity Coupled-Cluster Approaches in the Numeric Atom-Centered Orbital Framework for Molecular Systems † § † † ‡ § § Tonghao Shen, , Zhenyu Zhu, Igor Ying Zhang,*, , , and Matthias Scheffler † Department of Chemistry, Fudan University, Shanghai 200433, China ‡ Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Science, Fudan University, Shanghai 200433, China § Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany *S Supporting Information ABSTRACT: We present a massive-parallel implementation of the resolution of identity (RI) coupled-cluster approach that includes single, double, and perturbatively triple excitations, namely, RI-CCSD(T), in the FHI-aims package for molecular systems. A domain-based distributed-memory algorithm in the MPI/OpenMP hybrid framework has been designed to effectively utilize the memory bandwidth and significantly minimize the interconnect communication, particularly for the tensor contraction in the evaluation of the particle−particle ladder term. Our implementation features a rigorous avoidance of the on- the-fly disk storage and excellent strong scaling of up to 10 000 and more cores. Taking a set of molecules with different sizes, we demonstrate that the parallel performance of our CCSD(T) code is competitive with the CC implementations in state-of- the-art high-performance-computing computational chemistry packages. We also demonstrate that the numerical error due to the use of RI approximation in our RI-CCSD(T) method is negligibly small. Together with the correlation-consistent numeric atom-centered orbital (NAO) basis sets, NAO-VCC-nZ, the method is applied to produce accurate theoretical reference data for 22 bio-oriented weak interactions (S22), 11 conformational energies of gaseous cysteine conformers (CYCONF), and 32 Downloaded via FRITZ HABER INST DER MPI on January 8, 2021 at 22:13:06 (UTC).
    [Show full text]
  • Lawrence Berkeley National Laboratory Recent Work
    Lawrence Berkeley National Laboratory Recent Work Title From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape. Permalink https://escholarship.org/uc/item/4sm897jh Journal Chemical reviews, 121(8) ISSN 0009-2665 Authors Kowalski, Karol Bair, Raymond Bauman, Nicholas P et al. Publication Date 2021-04-01 DOI 10.1021/acs.chemrev.0c00998 Peer reviewed eScholarship.org Powered by the California Digital Library University of California From NWChem to NWChemEx: Evolving with the computational chemistry landscape Karol Kowalski,y Raymond Bair,z Nicholas P. Bauman,y Jeffery S. Boschen,{ Eric J. Bylaska,y Jeff Daily,y Wibe A. de Jong,x Thom Dunning, Jr,y Niranjan Govind,y Robert J. Harrison,k Murat Keçeli,z Kristopher Keipert,? Sriram Krishnamoorthy,y Suraj Kumar,y Erdal Mutlu,y Bruce Palmer,y Ajay Panyala,y Bo Peng,y Ryan M. Richard,{ T. P. Straatsma,# Peter Sushko,y Edward F. Valeev,@ Marat Valiev,y Hubertus J. J. van Dam,4 Jonathan M. Waldrop,{ David B. Williams-Young,x Chao Yang,x Marcin Zalewski,y and Theresa L. Windus*,r yPacific Northwest National Laboratory, Richland, WA 99352 zArgonne National Laboratory, Lemont, IL 60439 {Ames Laboratory, Ames, IA 50011 xLawrence Berkeley National Laboratory, Berkeley, 94720 kInstitute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794 ?NVIDIA Inc, previously Argonne National Laboratory, Lemont, IL 60439 #National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6373 @Department of Chemistry, Virginia Tech, Blacksburg, VA 24061 4Brookhaven National Laboratory, Upton, NY 11973 rDepartment of Chemistry, Iowa State University and Ames Laboratory, Ames, IA 50011 E-mail: [email protected] 1 Abstract Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems.
    [Show full text]
  • Optimizing the Use of Open-Source Software Applications in Drug
    DDT • Volume 11, Number 3/4 • February 2006 REVIEWS TICS INFORMA Optimizing the use of open-source • software applications in drug discovery Reviews Werner J. Geldenhuys1, Kevin E. Gaasch2, Mark Watson2, David D. Allen1 and Cornelis J.Van der Schyf1,3 1Department of Pharmaceutical Sciences, School of Pharmacy,Texas Tech University Health Sciences Center, Amarillo,TX, USA 2West Texas A&M University, Canyon,TX, USA 3Pharmaceutical Chemistry, School of Pharmacy, North-West University, Potchefstroom, South Africa Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs. To bring a new drug to the market is very costly, with the current of combinatorial approaches and HTS. The addition of computer- price tag approximating US$800 million, according to data reported aided drug design technologies to the R&D approaches of a com- in a recent study [1]. Therefore, it is not surprising that pharma- pany, could lead to a reduction in the cost of drug design and ceutical companies are seeking ways to optimize costs associated development by up to 50% [6,7].
    [Show full text]
  • State, Trends, and Needs of Quantum Chemistry Software
    State, Trends, and Needs of Quantum Chemistry Software Edward Valeev Department of Chemistry, Virginia Tech Software Institute for Abstractions and Methodologies for HPC Simulations Codes on Future Architectures University of Chicago December 10, 2012 Monday, December 10, 12 Talk Outline Intro to QC Core problems, data, and algorithms QC Software: Now Overview of the field-wide challenges + our own codes QC Software: Then Development trends and needs Monday, December 10, 12 What is Quantum Chemistry? QC = quantum mechanics of electrons (+nuclei) in chemical setting will not talk about solid state physics today! similar (yet different) ballgame 1 electrons nuclei electrons Z Hˆ Ψ = EΨ Hˆ = 2 I − 2 ∇i − r R i i i I I | − | ellipic linear 2nd-order PDE with electrons 1 (usually) Dirichlet boundary conditions + r r i<j i j | − | Ψ Ψ( r ) ≡ { i} Provides Molecular Geometries Chemical Reaction Energetics Chemical Reaction Pathways Response to Perturbations (Spectra) tamoxifen (breast cancer drug) etc. 57 nuclei, 200 electrons Monday, December 10, 12 What is Quantum Chemistry? QC = quantum mechanics of electrons (+nuclei) in chemical setting will not talk about solid state physics today! similar (yet different) ballgame 1 electrons nuclei electrons Z Hˆ Ψ = EΨ Hˆ = 2 I − 2 ∇i − r R i i i I I | − | ellipic linear 2nd-order PDE with electrons 1 (usually) Dirichlet boundary conditions + r r i<j i j | − | Ψ Ψ( r ) ≡ { i} Issues Huge # of variables High precision needed relevant # of electrons = 10 ... 10000 relevant differences of E on the
    [Show full text]
  • Virt&L-Comm.3.2012.1
    Virt&l-Comm.3.2012.1 A MODERN APPROACH TO AB INITIO COMPUTING IN CHEMISTRY, MOLECULAR AND MATERIALS SCIENCE AND TECHNOLOGIES ANTONIO LAGANA’, DEPARTMENT OF CHEMISTRY, UNIVERSITY OF PERUGIA, PERUGIA (IT)* ABSTRACT In this document we examine the present situation of Ab initio computing in Chemistry and Molecular and Materials Science and Technologies applications. To this end we give a short survey of the most popular quantum chemistry and quantum (as well as classical and semiclassical) molecular dynamics programs and packages. We then examine the need to move to higher complexity multiscale computational applications and the related need to adopt for them on the platform side cloud and grid computing. On this ground we examine also the need for reorganizing. The design of a possible roadmap to establishing a Chemistry Virtual Research Community is then sketched and some examples of Chemistry and Molecular and Materials Science and Technologies prototype applications exploiting the synergy between competences and distributed platforms are illustrated for these applications the middleware and work habits into cooperative schemes and virtual research communities (part of the first draft of this paper has been incorporated in the white paper issued by the Computational Chemistry Division of EUCHEMS in August 2012) INTRODUCTION The computational chemistry (CC) community is made of individuals (academics, affiliated to research institutions and operators of chemistry related companies) carrying out computational research in Chemistry, Molecular and Materials Science and Technology (CMMST). It is to a large extent registered into the CC division (DCC) of the European Chemistry and Molecular Science (EUCHEMS) Society and is connected to other chemistry related organizations operating in Chemical Engineering, Biochemistry, Chemometrics, Omics-sciences, Medicinal chemistry, Forensic chemistry, Food chemistry, etc.
    [Show full text]
  • Computational Chemistry at the Petascale: Tools in the Tool Box
    Computational chemistry at the petascale: tools in the tool box Robert J. Harrison [email protected] [email protected] The DOE funding • This work is funded by the U.S. Department of Energy, the division of Basic Energy Science, Office of Science, under contract DE-AC05-00OR22725 with Oak Ridge National Laboratory. This research was performed in part using – resources of the National Energy Scientific Computing Center which is supported by the Office of Energy Research of the U.S. Department of Energy under contract DE-AC03-76SF0098, – and the Center for Computational Sciences at Oak Ridge National Laboratory under contract DE-AC05-00OR22725 . Chemical Computations on Future High-end Computers Award #CHE 0626354, NSF Cyber Chemistry • NCSA (T. Dunning, PI) • U. Tennessee (Chemistry, EE/CS) • U. Illinois UC (Chemistry) • U. Pennsylvania (Chemistry) • Conventional single processor performance no longer increasing exponentially – Multi-core • Many “corners” of chemistry will increasingly be best served by non-conventional technologies – GP-GPU, FPGA, MD Grape/Wine Award No: NSF CNS-0509410 Project Title: Collaborative research: CAS-AES: An integrated framework for compile-time/run-time support for multi-scale applications on high-end systems Investigators: P. Sadayappan (PI) G. Baumgartner, D. Bernholdt, R.J. Harrison, J. Nieplocha, J. Ramanujam and A. Rountev Institution: The Ohio State University Louisiana State University University of Tennessee, Knoxville Website: Description of Graphic Image: http://www.cse.ohio-state.edu/~saday/ A tree-based algorithm (here in 1D, but in actuality in 3-12D) is naturally implemented via recursion. The data in subtrees is managed in chunks and the computation expressed very compactly.
    [Show full text]