first principles modeling with octopus: massive ... · first principles modeling with octopus:...

First principles modeling with Octopus: massive parallelization towards petaflop computing and

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A. Castro, J. Alberdi and A. Rubio

Outline

Theoretical SpectroscopyThe octopus codeParallelization

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Outline


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Theoretical Spectroscopy

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Electronic excitations:~ Optical absorption~ Electron energy loss~ Inelastic X-ray scattering

~ Photoemission~ Inverse photoemission~ …

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Goal: First principles (from electronic structure) theoretical description of the various spectroscopies (“theoretical beamlines”):

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Role: interpretation of (complex) experimental findings

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Role: interpretation of (complex) experimental findings

Theoretical atomistic structures, and corresponding TEM images.


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The European Theoretical Spectroscopy Facility (ETSF)

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The European Theoretical Spectroscopy Facility (ETSF)

~ Networking~ Integration of tools (formalism, software)~ Maintenance of tools~ Support, service, formation


The octopus code is a member of a family of free software codes developed, to a large extent, within the ETSF:~ abinit~ octopus~ dp

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Outline


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The octopus code

Targets:~ Optical absorption spectra of molecules, clusters, nanostructures, solids.~ Response to lasers (non-perturbative response to high-intensity fields)~ Dichroic spectra, and other mixed (electric-magnetic responses)~ Adiabatic and non-adiabatic Molecular Dynamics (for, e.g. infrared and vibrational spectra, or photochemical reactions).

~ Quantum Optimal Control Theory for molecular processes.

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The octopus code

Physical approximations and techniques:~ Density-Functional Theory, Time-Dependent Density-Functional

Theory to describe the electron structure.• Comprehensive set of functionals through the libxc library.

~ Mixed quantum-classical systems.~ Both real-time and frequency domain response (“Casida” and

“Sternheimer” formulations).

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The octopus code

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Numerics:~ Basic representation: real space

grid.~ Usually regular and rectangular,

occasionally curvilinear.~ Plane waves for some procedures

(especially for periodic systems)~ Atomic orbitals for some

procedures

The octopus code

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Derivative in a point: sum over neighbor points.Cij depend on the points used: the stencil.More points -> more precision.Semi-local operation.

The octopus code

The key equations~ Ground-state DFT: Kohn-Sham equations.

~ Time-dependent DFT: time-dependent KS eqs:

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The octopus code

Key numerical operations:~ Linear systems with sparse matrices.~ Eigenvalue systems with sparse matrices.~ Non-linear eigenvalue systems.~ Propagation of “Schrödinger-like” equations.

~ The dimension can go up to 10 million points.~ The storage needs can go up to 10 Gb.

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The octopus code

Use of libraries:~ BLAS, LAPACK~ GNU GSL mathematical library.~ FFTW~ NetCDF~ ETSF input/output library~ Libxc exchange and correlation library~ Other optional libraries.

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www.tddft.org/programs/octopus/

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Outline


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Objective

Reach petaflops computing, with a scientific codeSimulate photosynthesis of the light in chlorophyll

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Simulation objective

Photovoltaic materials Biomolecules

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The Octopus code

Software package for electron dynamicsDeveloped in the UPV/EHUGround state and excited states propertiesRealtime, Casida and Sternheimer TDDFTQuantum transport and optimal controlFree software: GPL license

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http://www.tddft.org/programs/octopus/

Octopus simulation strategy

Pseudopotential approximationRealspace gridsMain operation: the finite difference Laplacian

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Libraries

Intensive use of librariesGeneral libraries:

~ BLAS~ LAPACK~ FFT~ Zoltan/Metis~ ...

Specific libraries~ Libxc~ ETSF_IO

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Multilevelparallelization

MPI KohnSham states

Realspace domains

InNo

de OpenMP threads OpenCLtasksVectorization

CPU GPU

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Target systems:

Massive number of execution units

~ Multicore processors with vectorial FPUs

~ IBM Blue Gene architecture

~ Graphical processing units

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High Level Parallelization

MPI parallelization

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Parallelization by states/orbitals

Assign each processor a group of statesTimepropagation is independent for each stateLittle communication requiredLimited by the number of states in the system

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Domain parallelization

Assign each processor a set of grid pointsPartition libraries: Zoltan or Metis

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Main operations in domain

parallelization

Laplacian: copy points in domain

boundaries

Overlap computation

and communication

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Integration: global sums (reductions)

Group reduction operations

Low level paralelization and vectorization

OpenMP andGPU

Two approaches

OpenMP

Thread programming based on compiler directivesInnode parallelizationLittle memory overhead compared to MPIScaling limited by memory bandwidthMultithreaded Blas and Lapack

OpenCL

Hundreds of execution unitsHigh memory bandwidth but with long latencyBehaves like a vector processor (length > 16)Separated memory: copy from/to main memory

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Supercomputers

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Corvo cluster~ X86_64VARGAS (in IDRIS)~ Power6~ 67 teraflopsMareNostrum~ PowerPC 970~ 94 teraflopsJugene (image)~ 1 petaflops

Test Results

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Laplacian operator

Comparison in performance of the finitedifference Laplacian operator

CPU uses 4 threadsGPU is 4 times fasterCache effects are visible

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Timepropagation

Comparison in performance for a timepropagation

Fullerene moleculeThe GPU is 3 times fasterLimited by copying and nonGPU code

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Multilevel parallelization

Clorophyll molecule: 650 atomsJugene Blue Gene/PSustained throughput: > 6.5 teraflopsPeak throughput: 55 teraflops

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Scaling

Scaling (II)

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Comparison of two atomic system in Jugene

Target system

Jugene all nodes~ 294 912 processor cores = 73 728 nodes~ Maximum theoretical performance of 1002 MFlops

5879 atoms chlorophyll system~ Complete molecule of spinach

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Tests systems

Smaller molecules~ 180 atoms~ 441 atoms~ 650 atoms~ 1365 atoms

Partition of machines~ Jugene and Corvo

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Profiling

Profiled within the codeProfiled with Paraver tool~ www.bsc.es/paraver

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1 TD iteration

Poisson

Some “inner” iterations

One “inner” iterationIreceive Isend Iwait

Poisson solver

2 xAlltoallAllgather Allgather Scatter

Improvements

Memory improvements in GS~ Split the memory among the nodes~ Use of ScaLAPACK

Improvements in the Poisson solver for TD~ Pipeline execution

• Execute Poisson while continues with an approximation~ Use new algorithms like FFM~ Use of parallel FFTs

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Conclusions

KohnSham scheme is inherently parallelIt can be exploited for parallelization and vectorizationSuited to current and future computer architecturesTheoretical improvements for large system modeling

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first principles modeling with octopus: massive ... · first principles modeling with octopus:...

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