Large scale and hybrid computing with CP2K - Prace Training Portal

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O(N) HFX: measurementsLinear scaling is key .... thousands of molecules possibleOn 'standard' cluster hardware in minutes.
Parallel implementation: OMP/MPIE=v T (Mv) ?Distribute the matrix M (dim: 10 9 x 10 9 ), replicate the vector vSimple communication pattern (v is distributed in GGA mode)allows for exploiting the full symmetries (8x speedup)Advanced load balancing model usedv and (Mv) can be rather large vectors (10 9 elements)Exploit current architectures (e.g. 16 cores with 16Gb per node) → MPI/OpenMPShared v and (Mv), v is read-only, (Mv) is atomically updatedExploit that only O(N) entries of v are non-zero → sparse storage for vRemaining memory used for storing M.
Page 1 and 2: Large scale and hybrid computing wi
Page 3 and 4: Modeling complex systemsDensity fun
Page 5 and 6: What is CP2K ?CP2K is a freely avai
Page 7 and 8: CP2K: the swiss army knife ofmolecu
Page 9 and 10: What is CP2K: teamWith contribution
Page 11 and 12: CP2K: science (I)Electronic structu
Page 13 and 14: CP2K: science (III)Structure predic
Page 15 and 16: CP2K: algorithmsThe power & challen
Page 17 and 18: CP2K on CSCSproduction hardwareFor
Page 19 and 20: Complex hardware:NUMA nodes with PC
Page 22 and 23: More accurate theory:From 'GGA' to
Page 24 and 25: Hartree-Fock exchangeAn easy term i
Page 28 and 29: Many-core era: OMP/MPIMain benefits
Page 30 and 31: In-core integral compressionAlmost
Page 32 and 33: Auxiliary Density Matrix Methods(AD
Page 34 and 35: Møller-Plesset Perturbation Theory
Page 36 and 37: Parallel efficiencyCO 2crystal (32
Page 38 and 39: Linear Scaling SCFLargest O(N 3 ) c
Page 40 and 41: Sign matrix iterationsThe density m
Page 42 and 43: Towards O(1) :constant walltime wit
Page 44 and 45: The local multiplicationA two stage
Page 46 and 47: GPU Kernel DescriptionCompute a bat
Page 48 and 49: The GPU port of DBCSR (II)Open Chal
Page 50 and 51: GPU application benchmark●>400 mu
Page 52: AcknowledgementsZürichJuerg Hutter

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