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GPGPU use cases from the MoBrain community

Explore GPGPU applications in molecular research, focusing on Cryo-EM mapping, MD simulations, and software solutions like Powerfit and DisVis. Key tasks, challenges, and hardware details are discussed.

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GPGPU use cases from the MoBrain community

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  1. GPGPU use cases from the MoBrain community João Rodrigues • Postdoctoral Researcher • Utrecht University, NL • j.rodrigues@uu.nl

  2. MoBrainmain activities • Task 1: User support and training • Task 2: Cryo-EM in the cloud: bringing clouds to the data • Task 3: Increasing the throughput efficiency of WeNMR portals via DIRAC4EGI • Task 4: Cloud VMs for structural biology • Task 5: GPU portals for biomolecular simulations • Task 6: Integrating the micro (WeNMR/INSTRUCT) and macroscopic (NeuGRID4you) VRCs

  3. Software our solutions Powerfit Fitting atomic structures in Cryo-EM density maps using a full exhaustive 6D cross-correlation search based on FFT techniques. • DisVis Visualization and quantification of accessible interaction space of distance-restrained protein-protein docking based on FFT techniques. • GROMACS Versatile package to perform Molecular Dynamics simulations on systems with hundreds to millions of particles. • AMBER Package to perform Molecular Dynamics simulations.

  4. Use Case fitting atomic structures in Cryo-EM density maps Powerfit Fitting atomic structures in Cryo-EM density maps using a full exhaustive 6D cross-correlation search based on FFT techniques.

  5. Software powerfit & disvis Core Dependencies Numpy Cython Scipy https://github.com/haddocking/

  6. Software powerfit & disvis Numpy Cython Scipy Accelerated CPU FFTW3 pyFFTW https://github.com/haddocking/

  7. Software powerfit & disvis OpenCL pyOpenCL clFFT Numpy Cython Scipy gpyFFT GPGPU Acceleration FFTW3 pyFFTW https://github.com/haddocking/

  8. Software powerfit & disvis https://github.com/haddocking/

  9. Use Case MD simulation of a large protein system Ferritin is a protein of 450 kDa, consisting of 24 subunits A MD simulation in explicit solvent involves: more than 4000 amino acids more than 36000 water molecules Total atoms: 176000 Test Simulations were run using AMBER 14, with OpenMPI Performance on 2 GPU K20m: 8.66 ns/day

  10. Software GROMACS & AMBER CUDA 4.x MKL CC & CMake FFTW3

  11. Software GROMACS & AMBER CUDA 4.x GBs of data per day per simulation. MKL CC & CMake FFTW3

  12. Queueing & Middleware resources & requirements • Example Hardware • Cluster based on 3 WorkerNodes: • 2x XEON E5-2620 v2 • 2x K20m • 64 GbRAM • Total • 36 CPU core and 6 GPU 192 Gb RAM

  13. Queueing & Middleware resources & requirements OpenMPI • MiddlewareRequirements • One Job per GPU (AMBER) • CPUs must be powerful to match the GPU • CPU isstilldoing some work (e.g. bondedinteractions) • Discoverablewithin the e-infrastructure (e.g. jdlrequirement) • Preferrablycontaining GPU type(GTX vs K-series, AMD vs NVIDIA) • AMD GPUsnotsupported by MD code (yet) • Double-precisiononlysupported by Tesla cards Torque & Maui

  14. Conclusions & Questions OpenCL Scipy pyOpenCL CUDA CC & CMake clFFT Numpy OpenMPI FFTW3 Torque & Maui gpyFFT Cython pyFFTW MKL Thank you for your attention

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