DOCSLIB.ORG

NVIDIA Cryo-EM May 2019

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
NVIDIA Cryo-EM May 2019 Molecular Dynamics (MD) on GPUs March 2019 Accelerating Discoveries Using a supercomputer powered by the Tesla Platform with over 3,000 Tesla accelerators, University of Illinois scientists performed the first all-atom simulation of the HIV virus and discovered the chemical structure of its capsid — “the perfect target for fighting the infection.” Without gpu, the supercomputer would need to be 5x larger for similar performance. 2 Overview of Life & Material Accelerated Apps MD QC All key codes are GPU-accelerated All key codes are ported or optimizing Great multi-GPU, multi-node (dense) performance GPU-accelerated math libraries, OpenACC directives GPU-accelerated apps GPU-accelerated apps ACEMD*, AMBER*, BAND, CHARMM, DESMOND, ESPResso, Folding@Home, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS-UK, GPAW, LATTE, LSDalton, LSMS, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, LAMMPS, Lattice Microbes*, mdcore, MOLCAS, MOPAC2012, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, Quantum MELD, miniMD, NAMD, OpenMM, PolyFTS, SOP-GPU* & more Espresso/PWscf, QUICK, TeraChem* Active acceleration projects CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* >90% of the workload is on GPU 3 MD vs. QC on GPUs Molecular Dynamics Quantum Chemistry Properties - electronic properties, Simulates atomic positions over time Calculations ground state, excitation, spectra Chemical-biological or chemical-material Examples: MO, PW, DFT, semi-emp Simple empirical formulas Electron wave function Forces No bond rearrangements Bond rearrangements allowed Atom count Millions Thousands Solvent optional Solvent Solvent included without difficulty Classical QM/MM or implicit methods Numeric precision Primarily FP32 Primarily FP64 CUDA - cuBLAS, cuFFT Software acceleration CUDA - cuFFT Solvers – cuTensor, Eigen OpenACC Quadro for workstations NVIDIA GPUs Tesla for data center Tesla for data center Error correction (ECC) Not required Required 4 GPU-Accelerated Molecular Dynamics Apps Performance Slides Available ACEMD HOOMD-Blue • DESMOND/FEP • HTMD • ESPResSO • mdcore AMBER/GTI LAMMPS • Folding@Home • MELD Chameleon NAMD • Genesis • OpenMM CHARMM • GPUGrid.net • PolyFTS GROMACS • HALMD GPU Perf compared against dual multi-core x86 CPU socket. 5 MD Applications GPU-Accelerated Computing Turbocharge your research! • Speedup of 3X-8X compared to CPU only in all tests (average) • Majority of compute intensive for classical MD ported to GPUs • Large performance boost and improve TCO for compute infrastructure • Tesla GPUs are more energy efficient <50% of CPU-only computing • GPUs scale well within a node and/or over multiple nodes • Tesla V100 is highest performance GPU Try GPU accelerated MD apps for free – nvidia.com/GPUTestDrive 6 AmberMD 18.10-AT_18.12 March 2019 AmberMD 18.10_AT_18.12- PME-Cellulose AmberMD 18.10-AT_18.12 - Tesla V100-SXM2-32GB 90 30.0X 80 78.55 25.0X 70 71.11 60 63.19 20.0X Cellulose 57.68 58.54 408,609 atoms 50 48.13 15.0X Running AmberMD 18.10_AT_18.12 ns/day 40 4X V100 2X V100 The blue node contains Dual Intel Xeon 4X V100 24.2X 30 2X V100 21.9X 10.0X Gold 6140 (Skylake) CPUs 20.3X 1X V100 1X V100 18.5X 18.0X The green nodes contain Dual Intel 20 15.4X Xeon Gold 6140 (Skylake) CPUs + Tesla 5.0X Skylake Skylake V100 SXM2 (32GB) GPUs 10 Dual CPU Dual CPU 1.0X 1.0X Speed up over dual CPU node (X) 0 0.0X PME-Cellulose_NPT 2fs PME-Cellulose_NVE 2fs 8 AmberMD 18.10_AT_18.12 - PME-FactorIX AmberMD 18.10-AT_18.12 - Tesla V100-SXM2-32GB 350 25.0X 326.85 300 290.8 20.0X Factor IX 268.08 250 262.56 90,906 atoms 236.39 15.0X 200 207.66 Running AmberMD 18.10_AT_18.12 ns/day 150 4X V100 2X V100 21.0X 10.0X The blue node contains Dual Intel Xeon 4X V100 1X V100 2X V100 18.7X Gold 6140 (Skylake) CPUs 17.8X 16.9X 100 1X V100 15.7X 13.8X The green nodes contain Dual Intel 5.0X Xeon Gold 6140 (Skylake) CPUs + Tesla 50 Skylake Skylake Dual CPU Dual CPU V100 SXM2 (32GB) GPUs 1.0X 1.0X Speed up over dual CPU node (X) 0 0.0X PME-FactorIX_NPT 2fs PME-FactorIX_NVE 2fs 9 AmberMD 18.10_AT_18.12 - PME-JAC AmberMD 18.10-AT_18.12 - Tesla V100-SXM2-32GB 800 14.0X 700 12.0X 687.83 600 622.91 591.28 10.0X DHFR 571.21 23,558 atoms 500 522.88 506.36 8.0X 400 Running AmberMD 18.10_AT_18.12 ns/day 4X V100 1X V100 6.0X 4X V100 2X V100 12.3X The blue node contains Dual Intel Xeon 300 1X V100 11.1X 2X V100 10.4X 10.6X Gold 6140 (Skylake) CPUs 9.6X 9.3X 4.0X 200 The green nodes contain Dual Intel Skylake Skylake Xeon Gold 6140 (Skylake) CPUs + Tesla Dual CPU Dual CPU 2.0X 100 V100 SXM2 (32GB) GPUs 1.0X 1.0X Speed up over dual CPU node (X) 0 0.0X PME-JAC_NPT 2fs PME-JAC_NVE 2fs 10 AmberMD 18.10_AT_18.12 - PME-STMV_NPT AmberMD 18.10-AT_18.12 - Tesla V100-SXM2-32GB 25 25.0X 20 20.83 20.0X 19.94 Satellite Tobacco Mosaic Virus 17.02 1,067,095 atoms 15 15.0X Running AmberMD 18.10_AT_18.12 ns/day 2X V100 4X V100 21.9X 10 1X V100 21.0X 10.0X The blue node contains Dual Intel Xeon 17.9X Gold 6140 (Skylake) CPUs The green nodes contain Dual Intel 5 5.0X Xeon Gold 6140 (Skylake) CPUs + Tesla Skylake Dual CPU V100 SXM2 (32GB) GPUs 1.0X Speed up over dual CPU node (X) 0 0.0X PME-STMV_NPT 4fs 11 AmberMD 18.10_AT_18.12 – P100 vs V100 AmberMD 18.10-AT_18.12 70.00 25.0X 60.00 63.19 All benchmarks compared as set 57.68 20.0X Cellulose, FactorIX, JAC, STMV 50.00 Running AmberMD 18.10_AT_18.12 48.13 15.0X 40.00 The blue node contains Dual Intel Xeon 39.99 Gold 6140 (Skylake) CPUs 37.17 ns/day 4X 30.00 The green nodes contain Dual Intel 2X V100 10.0X 30.22 Xeon Gold 6140 (Skylake) CPUs + 1X V100 20.3X 4X V100 18.5X Tesla P100 SXM2 (16GB) GPUs or 20.00 21.24 2X 21.24 Tesla V100 SXM2 (32GB) GPUs 1X P100 P100 15.4X P100 11.9X 12.8X 5.0X 10.00 Skylake 9.7X Skylake Speed up over dual CPU node (X) Dual CPU Dual CPU 1.0X 1.0X 0.00 0.0X P100 V100 12 AmberMD 18.10_AT_18.12- PME-Cellulose AmberMD 18.10-AT_18.12 - Tesla T4 30.0 9.0X 8.0X 25.0 24.9 7.0X 23.9 22.7 21.8 20.0 6.0X Cellulose 408,609 atoms 5.0X 17.1 15.0 16.0 Running AmberMD 18.10_AT_18.12 ns/day 2X T4 4X T4 4.0X 2X T4 4X T4 7.7X 7.4X The blue node contains Dual Intel Xeon 7.3X 7.0X 10.0 3.0X Gold 6140 (Skylake) CPUs 1X T4 1X T4 5.1X 5.3X The green nodes contain Dual Intel Skylake Skylake 2.0X Dual CPU Dual CPU Xeon Gold 6140 (Skylake) CPUs + Tesla 5.0 1.0X 1.0X T4 PCIe (16GB) GPUs 1.0X Speed up over dual CPU node (X) 0.0 0.0X PME-Cellulose_NPT 2fs PME-Cellulose_NVE 2fs 13 AmberMD 18.10_AT_18.12 - PME-FactorIX AmberMD 18.10-AT_18.12 - Tesla T4 140.0 9.0X 8.0X 120.0 123.8 112.5 112.6 7.0X Factor IX 100.0 102.3 90,906 atoms 6.0X 80.0 85.0 5.0X 79.6 Running AmberMD 18.10_AT_18.12 ns/day 2X T4 4.0X 60.0 2X T4 4X T4 The blue node contains Dual Intel Xeon 4X T4 8.0X 7.5X 7.2X Gold 6140 (Skylake) CPUs 6.8X 1X T4 1X T4 3.0X 40.0 5.3X 5.5X The green nodes contain Dual Intel Skylake Skylake 2.0X Xeon Gold 6140 (Skylake) CPUs + Tesla Dual CPU Dual CPU T4 PCIe (16GB) GPUs 20.0 1.0X 1.0X 1.0X 0.0 0.0X Speed up over dual CPU node (X) PME-FactorIX_NPT 2fs PME-FactorIX_NVE 2fs 14 AmberMD 18.10_AT_18.12 - PME-JAC AmberMD 18.10-AT_18.12 - Tesla T4 400.0 8.0X 372.8 350.0 7.0X 331.8 336.3 300.0 6.0X 301.8 DHFR 285.4 23,558 atoms 250.0 262.2 5.0X Running AmberMD 18.10_AT_18.12 200.0ns/day 4.0X 2X T4 The blue node contains Dual Intel Xeon 2X T4 6.7X 4X T4 150.0 4X T4 3.0X Gold 6140 (Skylake) CPUs 6.1X 1X T4 6.0X 1X T4 5.5X 4.8X 5.1X The green nodes contain Dual Intel 100.0 Skylake Skylake 2.0X Dual CPU Dual CPU Xeon Gold 6140 (Skylake) CPUs + Tesla 1.0X 1.0X T4 PCIe (16GB) GPUs 50.0 1.0X 0.0 0.0X Speed up over dual CPU node (X) PME-JAC_NPT 2fs PME-JAC_NVE 2fs 15 AmberMD 18.10_AT_18.12 - PME-STMV_NPT AmberMD 18.10-AT_18.12 - Tesla T4 16.0 9.0X 15.0 14.0 8.0X 14.3 7.0X 12.0 Satellite Tobacco Mosaic Virus 6.0X 10.0 10.7 1,067,095 atoms 5.0X 8.0 Running AmberMD 18.10_AT_18.12 ns/day 2X T4 4X T4 8.2X 4.0X 7.8X The blue node contains Dual Intel Xeon 6.0 1X T4 3.0X Gold 6140 (Skylake) CPUs 5.9X 4.0 Skylake 2.0X The green nodes contain Dual Intel Dual CPU Xeon Gold 6140 (Skylake) CPUs + Tesla 1.0X T4 PCIe (16GB) GPUs 2.0 1.0X Speed up over dual CPU node (X) 0.0 0.0X PME-STMV_NPT 4fs 16 AmberMD recommended usage Motherboard and CPU Dual-socket with server x86-64 CPU System memory >=16GB GPUs Tesla V100 SXM2 GPUs per socket 1 to 8 GPUs per task 1 – 4 (case dependent) 17 17 GROMACS 2019.1 March 2019 GROMACS 2019.1 - ADH Dodec GROMACS 2019.1 - Tesla V100-SXM2-32GB 250 4.0X 3.5X 200 193.52 3.0X 184.67 ADH 2.5X 150 160.21 134,000 atoms 4X V100 2.0X Running GROMACS 2019.1 ns/day 2X V100 100 1X V100 3.6X 3.4X 1.5X The blue node contains Dual Intel Xeon 3.0X Gold 6140 (Skylake) CPUs 1.0X The green nodes contain Dual Intel 50 53.7 Xeon Gold 6140 (Skylake) CPUs + Tesla Skylake 0.5X V100 SXM2 (32GB) GPUs Dual CPU 1.0X 0 0.0X Speed up over dual CPU node (X) ADH Dodec (h-bond) 19 GROMACS 2019.1 - Cellulose GROMACS 2019.1 - Tesla V100-SXM2-32GB 60 4.0X 54.22 3.5X 50 51.94 3.0X 44.49 40 Cellulose 2.5X 408,609 atoms 30 4X V100 2.0X Running GROMACS 2019.1 ns/day 2X V100 3.6X 1X V100 3.4X 1.5X The blue node contains Dual Intel Xeon 20 2.9X Gold 6140 (Skylake) CPUs 1.0X 15.13 The green nodes contain Dual Intel 10 Xeon Gold 6140 (Skylake) CPUs + Tesla Skylake 0.5X V100 SXM2 (32GB) GPUs Dual CPU 1.0X 0 0.0X Speed up over dual CPU node (X) Cellulose (h-bond) 20 GROMACS 2019.1 - STMV GROMACS 2019.1 - Tesla V100-SXM2-32GB 18 5.0X 16 4.5X 15.84 15.95 4.0X 14 3.5X Satellite Tobacco Mosaic Virus 12 1,067,095 atoms 3.0X 10 10.24 2X V100 4X V100 2.5X Running GROMACS 2019.1 ns/day 8 4.5X 4.5X 2.0X The blue node contains Dual Intel Xeon 6 Gold 6140 (Skylake) CPUs 1X
Load more

Recommended publications
  • GPAW, Gpus, and LUMI
    GPAW, GPUs, and LUMI Martti Louhivuori, CSC - IT Center for Science Jussi Enkovaara GPAW 2021: Users and Developers Meeting, 2021-06-01 Outline LUMI supercomputer Brief history of GPAW with GPUs GPUs and DFT Current status Roadmap LUMI - EuroHPC system of the North Pre-exascale system with AMD CPUs and GPUs ~ 550 Pflop/s performance Half of the resources dedicated to consortium members Programming for LUMI Finland, Belgium, Czechia, MPI between nodes / GPUs Denmark, Estonia, Iceland, HIP and OpenMP for GPUs Norway, Poland, Sweden, and how to use Python with AMD Switzerland GPUs? https://www.lumi-supercomputer.eu GPAW and GPUs: history (1/2) Early proof-of-concept implementation for NVIDIA GPUs in 2012 ground state DFT and real-time TD-DFT with finite-difference basis separate version for RPA with plane-waves Hakala et al. in "Electronic Structure Calculations on Graphics Processing Units", Wiley (2016), https://doi.org/10.1002/9781118670712 PyCUDA, cuBLAS, cuFFT, custom CUDA kernels Promising performance with factor of 4-8 speedup in best cases (CPU node vs. GPU node) GPAW and GPUs: history (2/2) Code base diverged from the main branch quite a bit proof-of-concept implementation had lots of quick and dirty hacks fixes and features were pulled from other branches and patches no proper unit tests for GPU functionality active development stopped soon after publications Before development re-started, code didn't even work anymore on modern GPUs without applying a few small patches Lesson learned: try to always get new functionality to the
    [Show full text]
  • 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]
  • D6.1 Report on the Deployment of the Max Demonstrators and Feedback to WP1-5
    Ref. Ares(2020)2820381 - 31/05/2020 HORIZON2020 European Centre of Excellence ​ Deliverable D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 Pablo Ordejón, Uliana Alekseeva, Stefano Baroni, Riccardo Bertossa, Miki Bonacci, Pietro Bonfà, Claudia Cardoso, Carlo Cavazzoni, Vladimir Dikan, Stefano de Gironcoli, Andrea Ferretti, Alberto García, Luigi Genovese, Federico Grasselli, Anton Kozhevnikov, Deborah Prezzi, Davide Sangalli, Joost VandeVondele, Daniele Varsano, Daniel Wortmann Due date of deliverable: 31/05/2020 Actual submission date: 31/05/2020 Final version: 31/05/2020 Lead beneficiary: ICN2 (participant number 3) Dissemination level: PU - Public www.max-centre.eu 1 HORIZON2020 European Centre of Excellence ​ Deliverable D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 Document information Project acronym: MaX Project full title: Materials Design at the Exascale Research Action Project type: European Centre of Excellence in materials modelling, simulations and design EC Grant agreement no.: 824143 Project starting / end date: 01/12/2018 (month 1) / 30/11/2021 (month 36) Website: www.max-centre.eu Deliverable No.: D6.1 Authors: P. Ordejón, U. Alekseeva, S. Baroni, R. Bertossa, M. ​ Bonacci, P. Bonfà, C. Cardoso, C. Cavazzoni, V. Dikan, S. de Gironcoli, A. Ferretti, A. García, L. Genovese, F. Grasselli, A. Kozhevnikov, D. Prezzi, D. Sangalli, J. VandeVondele, D. Varsano, D. Wortmann To be cited as: Ordejón, et al., (2020): Report on the deployment of the MaX Demonstrators and feedback to WP1-5. Deliverable D6.1 of the H2020 project MaX (final version as of 31/05/2020).
    [Show full text]
  • Atom: a MATLAB PACKAGE for MANIPULATION of MOLECULAR SYSTEMS
    Clays and Clay Minerals, Vol. 67, No. 5:419–426, 2019 atom: A MATLAB PACKAGE FOR MANIPULATION OF MOLECULAR SYSTEMS MICHAEL HOLMBOE * 1Chemistry Department, Umeå University, SE-901 87 Umeå, Sweden Abstract—This work presents Atomistic Topology Operations in MATLAB (atom), an open source library of modular MATLAB routines which comprise a general and flexible framework for manipulation of atomistic systems. The purpose of the atom library is simply to facilitate common operations performed for construction, manipulation, or structural analysis. Due to the data structure used, atoms and molecules can be operated upon based on different chemical names or attributes, such as atom-ormolecule-ID, name, residue name, charge, positions, etc. Furthermore, the Bond Valence Method and a neighbor-distance analysis can be performed to assign many chemical properties of inorganic molecules. Apart from reading and writing common coordinate files (.pdb, .xyz, .gro, .cif) and trajectories (.dcd, .trr, .xtc; binary formats are parsed via third-party packages), the atom library can also be used to generate topology files with bonding and angle information taking the periodic boundary conditions into account, and supports basic Gromacs, NAMD, LAMMPS, and RASPA2 topology file formats. Focusing on clay-mineral systems, the library supports CLAYFF (Cygan, 2004) but can also generate topology files for the INTERFACE forcefield (Heinz, 2005, 2013) for Gromacs and NAMD. Keywords—CLAYFF. Gromacs . INTERFACE force field . MATLAB . Molecular dynamics simulations . Monte Carlo INTRODUCTION Dombrowsky et al. 2018; Kapla & Lindén 2018; Matsunaga & Sugita 2018). This work presents the Molecular modeling is becoming increasingly important in Atomistic Topology Operations in MATLAB (atom)li- many different areas of fundamental and applied research brary – a large collection of >100 modular MATLAB (Cygan 2001;Luetal.2006; Medina, 2009).
    [Show full text]
  • 5 Jul 2020 (finite Non-Periodic Vs
    ELSI | An Open Infrastructure for Electronic Structure Solvers Victor Wen-zhe Yua, Carmen Camposb, William Dawsonc, Alberto Garc´ıad, Ville Havue, Ben Hourahinef, William P. Huhna, Mathias Jacqueling, Weile Jiag,h, Murat Ke¸celii, Raul Laasnera, Yingzhou Lij, Lin Ling,h, Jianfeng Luj,k,l, Jonathan Moussam, Jose E. Romanb, Alvaro´ V´azquez-Mayagoitiai, Chao Yangg, Volker Bluma,l,∗ aDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA bDepartament de Sistemes Inform`aticsi Computaci´o,Universitat Polit`ecnica de Val`encia,Val`encia,Spain cRIKEN Center for Computational Science, Kobe 650-0047, Japan dInstitut de Ci`enciade Materials de Barcelona (ICMAB-CSIC), Bellaterra E-08193, Spain eDepartment of Applied Physics, Aalto University, Aalto FI-00076, Finland fSUPA, University of Strathclyde, Glasgow G4 0NG, UK gComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA hDepartment of Mathematics, University of California, Berkeley, CA 94720, USA iComputational Science Division, Argonne National Laboratory, Argonne, IL 60439, USA jDepartment of Mathematics, Duke University, Durham, NC 27708, USA kDepartment of Physics, Duke University, Durham, NC 27708, USA lDepartment of Chemistry, Duke University, Durham, NC 27708, USA mMolecular Sciences Software Institute, Blacksburg, VA 24060, USA Abstract Routine applications of electronic structure theory to molecules and peri- odic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal basis sets, overlap matrices. System sizes can range from few to thousands or, in some examples, millions of atoms. Different discretization schemes (basis sets) and different system geometries arXiv:1912.13403v3 [physics.comp-ph] 5 Jul 2020 (finite non-periodic vs.
    [Show full text]
  • ES2015 Compilation of Abstracts of Invited Speakers and Contributed
    ES2015 Compilation of Abstracts of Invited Speakers and Contributed Talk Speakers MONDAY, JUNE 22, 2015 Haggett Hall, Cascade Room, University of Washington New Methods Jim Chelikowsky, U of Texas, Austin “Seeing” the covalent bond: Simulating Atomic Force Microscopy Images Alexandru Georgescu, Yale U A Generalized Slave-Particle Formalism for Extended Hubbard Models Bryan Clark, UIUC From ab-initio to model systems: tales of unusual conductivity in electronic systems at high temperatures Advances in DFT and Applications Priya Gopal, Central Michigan U Novel tools for accelerated materials discovery in the AFLOWLIB.ORG repository Ismaila Dabo, Penn State U Electronic-Structure Calculations from Koopmans-Compliant Functionals Improving the performance of ab initio molecular dynamics simulations and band structure calculations for Eric Bylaska, PNNL actinide and geochemical systems with new algorithms and new machines QMC Hao Shi, College of William & Mary Recent developments in auxiliary-field quantum Monte Carlo: magnetic orders and spin-orbit coupling Fengjie Ma, College of William & Mary Ground and excited state calculations of auxiliary-field Quantum Monte Carlo in solids Paul Kent, ORNL New applications of Diffusion Quantum Monte Carlo Many Body Diana Qiu, UC Berkeley Many-body effects on the electronic and optical properties of quasi-two-dimensional materials Mei-Yin Chou, Academia Sinica Dirac Electrons in Silicene on Ag(111): Do they exist? Emmanuel Gull, U of Michigan Solutions of the Two Dimensional Hubbard Model TUESDAY, JUNE
    [Show full text]
  • Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
    processes Review Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development Outi M. H. Salo-Ahen 1,2,* , Ida Alanko 1,2, Rajendra Bhadane 1,2 , Alexandre M. J. J. Bonvin 3,* , Rodrigo Vargas Honorato 3, Shakhawath Hossain 4 , André H. Juffer 5 , Aleksei Kabedev 4, Maija Lahtela-Kakkonen 6, Anders Støttrup Larsen 7, Eveline Lescrinier 8 , Parthiban Marimuthu 1,2 , Muhammad Usman Mirza 8 , Ghulam Mustafa 9, Ariane Nunes-Alves 10,11,* , Tatu Pantsar 6,12, Atefeh Saadabadi 1,2 , Kalaimathy Singaravelu 13 and Michiel Vanmeert 8 1 Pharmaceutical Sciences Laboratory (Pharmacy), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland; ida.alanko@abo.fi (I.A.); rajendra.bhadane@abo.fi (R.B.); parthiban.marimuthu@abo.fi (P.M.); atefeh.saadabadi@abo.fi (A.S.) 2 Structural Bioinformatics Laboratory (Biochemistry), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland 3 Faculty of Science-Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands; [email protected] 4 Swedish Drug Delivery Forum (SDDF), Department of Pharmacy, Uppsala Biomedical Center, Uppsala University, 751 23 Uppsala, Sweden; [email protected] (S.H.); [email protected] (A.K.) 5 Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 7 A, FI-90014 Oulu, Finland; andre.juffer@oulu.fi 6 School of Pharmacy, University of Eastern Finland, FI-70210 Kuopio, Finland; maija.lahtela-kakkonen@uef.fi (M.L.-K.); tatu.pantsar@uef.fi
    [Show full text]
  • The CECAM Electronic Structure Library and the Modular Software Development Paradigm
    The CECAM electronic structure library and the modular software development paradigm Cite as: J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 Submitted: 06 May 2020 . Accepted: 08 June 2020 . Published Online: 13 July 2020 Micael J. T. Oliveira , Nick Papior , Yann Pouillon , Volker Blum , Emilio Artacho , Damien Caliste , Fabiano Corsetti , Stefano de Gironcoli , Alin M. Elena , Alberto García , Víctor M. García-Suárez , Luigi Genovese , William P. Huhn , Georg Huhs , Sebastian Kokott , Emine Küçükbenli , Ask H. Larsen , Alfio Lazzaro , Irina V. Lebedeva , Yingzhou Li , David López- Durán , Pablo López-Tarifa , Martin Lüders , Miguel A. L. Marques , Jan Minar , Stephan Mohr , Arash A. Mostofi , Alan O’Cais , Mike C. Payne, Thomas Ruh, Daniel G. A. Smith , José M. Soler , David A. Strubbe , Nicolas Tancogne-Dejean , Dominic Tildesley, Marc Torrent , and Victor Wen-zhe Yu COLLECTIONS Paper published as part of the special topic on Electronic Structure Software Note: This article is part of the JCP Special Topic on Electronic Structure Software. This paper was selected as Featured ARTICLES YOU MAY BE INTERESTED IN Recent developments in the PySCF program package The Journal of Chemical Physics 153, 024109 (2020); https://doi.org/10.1063/5.0006074 An open-source coding paradigm for electronic structure calculations Scilight 2020, 291101 (2020); https://doi.org/10.1063/10.0001593 Siesta: Recent developments and applications The Journal of Chemical Physics 152, 204108 (2020); https://doi.org/10.1063/5.0005077 J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 153, 024117 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp The CECAM electronic structure library and the modular software development paradigm Cite as: J.
    [Show full text]
  • Advances in Automated Transition State Theory Calculations: Improvements on the Autotst Framework
    Advances in automated transition state theory calculations: improvements on the AutoTST framework Nathan D. Harmsa, Carl E. Underkofflera, Richard H. Westa aDepartment of Chemical Engineering Northeastern University, Boston, MA 02115, USA Abstract Kinetic modeling of combustion chemistry has made substantial progress in recent years with the development of increasingly detailed models. However, many of the chemical kinetic parameters utilized in detailed models are estimated, often inaccurately. To help replace rate estimates with more accurate calculations, we have developed AutoTST, an automated Transition State Theory rate calculator. This work describes improvements to AutoTST, including: a systematic conformer search to find an ensemble of low energy conformers, vibrational analysis to validate transition state geometries, more accurate symmetry number calculations, and a hindered rotor treatment when deriving kinetics. These improvements resulted in location of transition state geometry for 93% of cases and generation of kinetic parameters for 74% of cases. Newly calculated parameters agree well with benchmark calculations and perform well when used to replace estimated parameters in a detailed kinetic model of methanol combustion. Keywords: Transition state theory, Chemical kinetic models, Model comparison, Uncertainty 1. Introduction Detailed kinetic models allow researchers to understand the chemistry of complex phenomena in systems such as combustion and hetrogeneous catalysis, thus enabling them to make informed experimental design choices, and to design and optimize processes and devices. Microkinetic models often contain hundreds of intermediates and thousands of reactions, for which thermodynamic and kinetic parameters need to be specified [1,2]. These parameters are ideally determined experimen- tally or calculated theoretically with high accuracy, but most are estimated [3].
    [Show full text]
  • Improvements of Bigdft Code in Modern HPC Architectures
    Available on-line at www.prace-ri.eu Partnership for Advanced Computing in Europe Improvements of BigDFT code in modern HPC architectures Luigi Genovesea;b;∗, Brice Videaua, Thierry Deutscha, Huan Tranc, Stefan Goedeckerc aLaboratoire de Simulation Atomistique, SP2M/INAC/CEA, 17 Av. des Martyrs, 38054 Grenoble, France bEuropean Synchrotron Radiation Facility, 6 rue Horowitz, BP 220, 38043 Grenoble, France cInstitut f¨urPhysik, Universit¨atBasel, Klingelbergstr.82, 4056 Basel, Switzerland Abstract Electronic structure calculations (DFT codes) are certainly among the disciplines for which an increasing of the computa- tional power correspond to an advancement in the scientific results. In this report, we present the ongoing advancements of DFT code that can run on massively parallel, hybrid and heterogeneous CPU-GPU clusters. This DFT code, named BigDFT, is delivered within the GNU-GPL license either in a stand-alone version or integrated in the ABINIT software package. Hybrid BigDFT routines were initially ported with NVidia's CUDA language, and recently more functionalities have been added with new routines writeen within Kronos' OpenCL standard. The formalism of this code is based on Daubechies wavelets, which is a systematic real-space based basis set. The properties of this basis set are well suited for an extension on a GPU-accelerated environment. In addition to focusing on the performances of the MPI and OpenMP parallelisation the BigDFT code, this presentation also relies of the usage of the GPU resources in a complex code with different kinds of operations. A discussion on the interest of present and expected performances of Hybrid architectures computation in the framework of electronic structure calculations is also adressed.
    [Show full text]
  • Maestro 10.2 User Manual
    Maestro User Manual Maestro 10.2 User Manual Schrödinger Press Maestro User Manual Copyright © 2015 Schrödinger, LLC. All rights reserved. While care has been taken in the preparation of this publication, Schrödinger assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. Canvas, CombiGlide, ConfGen, Epik, Glide, Impact, Jaguar, Liaison, LigPrep, Maestro, Phase, Prime, PrimeX, QikProp, QikFit, QikSim, QSite, SiteMap, Strike, and WaterMap are trademarks of Schrödinger, LLC. Schrödinger, BioLuminate, and MacroModel are registered trademarks of Schrödinger, LLC. MCPRO is a trademark of William L. Jorgensen. DESMOND is a trademark of D. E. Shaw Research, LLC. Desmond is used with the permission of D. E. Shaw Research. All rights reserved. This publication may contain the trademarks of other companies. Schrödinger software includes software and libraries provided by third parties. For details of the copyrights, and terms and conditions associated with such included third party software, use your browser to open third_party_legal.html, which is in the docs folder of your Schrödinger software installation. This publication may refer to other third party software not included in or with Schrödinger software ("such other third party software"), and provide links to third party Web sites ("linked sites"). References to such other third party software or linked sites do not constitute an endorsement by Schrödinger, LLC or its affiliates. Use of such other third party software and linked sites may be subject to third party license agreements and fees. Schrödinger, LLC and its affiliates have no responsibility or liability, directly or indirectly, for such other third party software and linked sites, or for damage resulting from the use thereof.
    [Show full text]
  • Kepler Gpus and NVIDIA's Life and Material Science
    LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc.
    [Show full text]