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GPU accelerated data analysis in Python PDF

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X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization GPU accelerated data analysis in Python: a study case in Materials Science Giuseppe Di Bernardo Max Planck Computing and Data Facility (MPCDF) Garching bei München, Germany [email protected] @jose_dibernardo Firenze April, 19th - 22nd 2018 Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 1 / 59 Basic Research at cutting-edge; strictly curiosity-driven and quality oriented; Flexible, dynamic, interdisciplinary MPIs; Autonomy → scientists decide upon science; Long-term trust systems; Quality assurance by peers. Sites of Max Planck Research Institutes and Associated Institutes MAX PLANCK INSTITUTES ABROAD ITALY BibliothecaHertziana, Rome Art HistoryInstitute, Florence THE NETHERLANDS Max Planck Institute forPsycholinguistics, Nijmegen USA, FLORIDA Max Planck Florida, Jupiter LUXEMBOURG Max Planck Institute Luxembourg forInternational, European andRegulatoryProceduralLaw, Luxembourg M A X -P L A N C K -G E S E L L S C H A F T | NAME, ABTEILUNG, KURZTITEL TT.MM.JJJJ | SEITE ‹Nr.› X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization Outline 1 X-ray Crystallography: a short overview 2 Accelerated Big Data Analytics with Python 3 3D Advanced Visualization of APT Crystallography Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 4 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization Outline 1 X-ray Crystallography: a short overview Atom Probe Tomography APT Crystallography: implementation on a GPU 2 Accelerated Big Data Analytics with Python PyCUDA: a Python-based approach to GPU computing PyNX: a GPU-based Fourier analysis in a Python workbench 3 3D Advanced Visualization of APT Crystallography Python Plotting for Exploratory Data Analysis Getting started with ParaView Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 5 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization Atom Probe Tomography (APT) A new playground for Big Data analysis1 Atom probe tomography: Retrieve high-quality/crystalline data Iterativily use the Fourier analysis for reconstruction of APT data; Need of the state-of-the art data mining and visualization Baptiste Gault & Shyam Katnagallu BDDMS -APT Fourier Transforms on billion points data!! 1by courtesy of S. Katnagallu, B. Gault, L. Stephenson & D. Raabe (MPIE) and M. Rampp (MPCDF) Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 6 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization Scattering computing from an atomistic model Theory: X-ray and neutron scattering Scattering vector Scattering density, at position r Z A(S) = ρ(r) exp(2iπ S · r)dV = FT[ρ(r)] V Scattered amplitude divide et impera Our approach: Direct calculation FFT is very fast: O(N logN) N−1 P A(s) = f (s)exp(2iπS · r ) coordinates in reciprocal space j j j=0 (hkl) are imposed by the O(N2) electron density grid Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 7 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization GPU-accelerated computing of large X-ray data Direct calculation in 3D reciprocal space (hkl): atomic 1 positions (r ) & scattering factors (f ): i i N−1 P A(s) = f (s)e2iπS·rj j j=0 computing any point in (hkl) from any structural model! In crystallography usually we have: 2 many atoms, e.g. N O(≥1 × 108) atoms many reflections (N ) in hkl, e.g. N O(128 × 128 × 128) hkl khl It requires massive parallelism 3 N ≈ 10 × N × N : O(≥1 × 1015) Flop flop atoms hkl modern GPU: 1013 Flop/s, CPU server: 1012 Flop/s algorithm well suited for GPU computations, scalable across multiple GPUs → O(minutes) Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 8 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization GPU-accelerated computing of large X-ray data Direct calculation in 3D reciprocal space (hkl): atomic 1 positions (r ) & scattering factors (f ): i i N−1 P A(s) = f (s)e2iπS·rj j j=0 computing any point in (hkl) from any structural model! In crystallography usually we have: 2 many atoms, e.g. N O(≥1 × 108) atoms many reflections (N ) in hkl, e.g. N O(128 × 128 × 128) hkl khl It requires massive parallelism 3 N ≈ 10 × N × N : O(≥1 × 1015) Flop flop atoms hkl modern GPU: 1013 Flop/s, CPU server: 1012 Flop/s algorithm well suited for GPU computations, scalable across multiple GPUs → O(minutes) Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 8 / 59 X-ray Crystallography: a short overview Accelerated Big Data Analytics with Python 3D Advanced Visualization GPU-accelerated computing of large X-ray data Direct calculation in 3D reciprocal space (hkl): atomic 1 positions (r ) & scattering factors (f ): i i N−1 P A(s) = f (s)e2iπS·rj j j=0 computing any point in (hkl) from any structural model! In crystallography usually we have: 2 many atoms, e.g. N O(≥1 × 108) atoms many reflections (N ) in hkl, e.g. N O(128 × 128 × 128) hkl khl It requires massive parallelism 3 N ≈ 10 × N × N : O(≥1 × 1015) Flop flop atoms hkl modern GPU: 1013 Flop/s, CPU server: 1012 Flop/s algorithm well suited for GPU computations, scalable across multiple GPUs → O(minutes) Giuseppe Di Bernardo (MPCDF) GPU Crystallography with Python April, 19-22 2017 8 / 59

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PyCUDA: a Python-based approach to GPU computing. PyNX: a GPU-based Fourier . parallel compute engine in NVIDIA graphics processing units (GPUs). Giuseppe Di .. OpenCL6language, along with pyOpenCL library const unsigned long ix=threadIdx.x+blockDim.x*blockIdx.x; // Thread idx. 2.
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