Experience many-core architectures at CASPUR: developing biochemistry application on Microsoft HPC 2008 environment

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Grant Pearson
7 years ago
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1 Experience many-core architectures at CASPUR: developing biochemistry application on Microsoft HPC 2008 environment Nico Sanna Manager of HPC Support Group in (Bio)Chemical-Physics

3 CASPUR Numeric Simulation Science Technology Database (experiment) S = f (..,..,T,..,..)

4 CASPUR Computational fields Computational Physics Computational Chemistry Computational Biology Computational Fluid-Dynamics Applied Mathematics Computational Material Science

5 HPC Computational (bio)-chemical-physics Quantum Chemistry WaveFunction Methods Density Functional (DFT) Methods Molecular Dynamics Classical MD Molecular Modeling Atomic & Molecular Physics E-mol scattering DNA Radiation Damage Data mining / warehousing Genome/Protein databases Microarray Experiment databases

6 Beyond ( with) multi-core? Many-core computing could be an alternative Accelerating co-processors become to be viable solutions to intensive computing Exploit GPU (&FPGA) computing at its best

7 Beyond ( with) multi-core? Reproduced w/ permission of Prof. E. Stahlberg

8 project

9 project

12 Ionizing radiation Radiation Damage: effect of the secondary species X rays γ rays α particles β particles Neutrons Cosmic rays R R * e- M + Living tissue The genotoxic effects, due to various DNA lesions, are not only produced by the direct impact of the initial high energy particles (direct ionization) Role of SECONDARY SPECIES: excited atoms and molecules, radicals, ions, low-energy electrons (LEEs) 1 ev < E < 20 ev (the most abundant, carrying most of the energy of the initial radiation)

13 U

14 GTGATAGAG e - SCELib2 + VOLSCAT Cross Sections & Resonances E 1 E 2 E 3 High Intensity Resonances T A G T G A T A G A G Low Intensity Resonances

15 The SCELib Package The SCELib package: study of e-/p+ scattering from molecular targets. N. Sanna, & F. A. Gianturco, Comp. Phys. Comm. 128(2) (2000) 615 N. Sanna & G. Morelli, Comp. Phys. Comm. 162(1) (2004) 51 N. Sanna, I. Baccarelli & G. Morelli, Comp. Phys. Comm. 180, 2544 (2009)

16 The SCE part of SCELib: implementation on the GPUs Multi centered Basis Functions Multi centered Molecular Orbitals Single centered Molecular Orbitals our target in electron-molecule scattering

17 Current implementation of SCELib 3.0 on GPUs SCE algorithm 1) Primitive GTO mapping 2) MOs Single Center Expansion

18 The GPU implementation of SCELib3.0: nvidia GPGPUs GPU Processor Memory System Clock Bandwidth G GHz 64 GB/s PC Asus Quadro G80GL 1.51 GHz 76.8 GB/s nvidia Tesla C GHz GB/s Dell5400

19 SCELib3.0 performance on GPUs - optimal parameters (warp size, arrays dimension in constant memory). Still work in progress scalability for CH 4 with respect to the number of grid points Execution time (in msec) and speed-up with respect to Host SPP calculation

20 SCELib3.0 performance on GPUs scalability for CH 4 with respect to the number of primitive GTOs G9800 Speed-up with respect to Host SPP calculations

21 SCELib3.0 performance on GPUs scalability with respect to the type of molecular system

GPU Hardware and Programming Models. Jeremy Appleyard, September 2015

GPU Hardware and Programming Models Jeremy Appleyard, September 2015 A brief history of GPUs In this talk Hardware Overview Programming Models Ask questions at any point! 2 A Brief History of GPUs 3 Once