Quantum Chemistry (QC) on GPUs - Nvidia · • Gaussian is a Top Ten HPC (Quantum Chemistry) Application. • 80-85% of use cases are GPU-accelerated (Hartree-Fock and DFT: energies,

Feb. 2, 2017

Quantum Chemistry (QC) on GPUs

2

Overview of Life & Material Accelerated Apps

MD: All key codes are GPU-accelerated

Great multi-GPU performance

Focus on dense (up to 16) GPU nodes &/or large # of

GPU nodes

ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso,

Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*,

LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD,

OpenMM, PolyFTS, SOP-GPU* & more

QC: All key codes are ported or optimizing

Focus on using GPU-accelerated math libraries,

OpenACC directives

GPU-accelerated and available today:

ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS-

UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012,

NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack,

Quantum Espresso/PWscf, QUICK, TeraChem*

Active GPU acceleration projects:

CASTEP, GAMESS, Gaussian, ONETEP, Quantum

Supercharger Library*, VASP & more

green* = application where >90% of the workload is on GPU

3

MD vs. QC on GPUs

“Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp)Simulates positions of atoms over time;

chemical-biological or chemical-material behaviors

Calculates electronic properties; ground state, excited states, spectral properties,

making/breaking bonds, physical properties

Forces calculated from simple empirical formulas (bond rearrangement generally forbidden)

Forces derived from electron wave function (bond rearrangement OK, e.g., bond energies)

Up to millions of atoms Up to a few thousand atoms

Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically (QM/MM)

or using implicit methods

Single precision dominated Double precision is important

Uses cuBLAS, cuFFT, CUDA Uses cuBLAS, cuFFT, OpenACC

Geforce (Accademics), Tesla (Servers) Tesla recommended

ECC off ECC on

4

Accelerating Discoveries

Using a supercomputer powered by the Tesla

Platform with over 3,000 Tesla accelerators,

University of Illinois scientists performed the first

all-atom simulation of the HIV virus and discovered

the chemical structure of its capsid — “the perfect

target for fighting the infection.”

Without gpu, the supercomputer would need to be

5x larger for similar performance.

5

GPU-Accelerated Quantum Chemistry Apps

Abinit

ACES III

ADF

BigDFT

CP2K

GAMESS-US

Gaussian

GPAW

LATTE

LSDalton

MOLCAS

Mopac2012

NWChem

Green Lettering Indicates Performance Slides Included

GPU Perf compared against dual multi-core x86 CPU socket.

Quantum SuperChargerLibrary

RMG

TeraChem

UNM

VASP

WL-LSMS

Octopus

ONETEP

Petot

Q-Chem

QMCPACK

Quantum Espresso

ABINIT

ABINIT on GPUS

Speed in the parallel version:

For ground-state calculations, GPUs can be used. This is based on

CUDA+MAGMA

For ground-state calculations, the wavelet part of ABINIT (which is BigDFT) is

also very well parallelized : MPI band parallelism, combined with GPUs

BigDFT

Courtesy of BigDFTteam @ CEA






April 2017

Gaussian 16

16

GPU-ACCELERATED GAUSSIAN 16 AVAILABLE

• Gaussian is a Top Ten HPC (Quantum Chemistry) Application.

• 80-85% of use cases are GPU-accelerated (Hartree-Fock and DFT: energies, 1st derivatives (gradients) and 2nd derivatives). More functionality to come.

• K40, K80 support; P100 support coming as a minor release, performance “good”, faster wall clock times. Early P100 results promising.

• No pricing difference between Gaussian CPU and GPU versions.

• Existing Gaussian 09 customers under maintenance contract get (free) upgrade.

• Existing non-maintenance customers required to pay upgrade fee.

• To get the bits or to ask about the upgrade fee, please contact Gaussian, Inc.’s Jim Hess, Operations Manager; [email protected].

100% PGI OpenACC Port (no CUDA)

mailto:[email protected]

17

rg-a25 on K80s

Running Gaussian version 16

The blue node contains Dual Intel Xeon E5-2698 [email protected] (Haswell) CPUs

The green nodes contain Dual Intel Xeon E5-2698 [email protected] (Haswell) CPUs + Tesla K80 (autoboost) GPUs

Alanine 25. Two steps: Force and Frequency. APFD 6-31G*

nAtoms = 259, nBasis = 2195

7.4 hrs 6.4 hrs 5.8 hrs 5.1 hrs0.0x

0.2x

0.4x

0.6x

0.8x

1.0x

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1 Haswell node 1 node +1x K80 per node

1 node +2x K80 per node


Speed-u

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rg-a25td on K80s




Alanine 25. Two Time-Dependent (TD) steps: Force and Frequency. APFD 6-31G*


27.9 hrs 25.8 hrs 22.6 hrs 20.1 hrs0.0x

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Speed-u

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rg-on on K80s




GFP ONIOM. Two steps: Force and Frequency. APFD/6-

311+G(2d,p):amber=softfirst)=embednAtoms = 3715 (48/3667), nBasis = 813

2.5 hrs 2.1 hrs 1.7 hrs 1.5 hrs0.0x

0.2x

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Speed-u

p v

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rg-on

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rg-ontd on K80s




GFP ONIOM. Two Time-Dependent (TD) steps: Force and Frequency. APFD/6-311+G(2d,p):amber=softfirst)=embed

nAtoms = 3715 (48/3667), nBasis = 813

55.4 hrs 43.6 hrs 33.7 hrs 26.8 hrs0.0x

0.5x

1.0x

1.5x

2.0x

2.5x




Speed-u

p v

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rg-val on K80s




Valinomycin. Two steps: Force and Frequency. APFD 6-311+G(2d,p)


229.0 hrs 168.4 hrs 141.7 hrs 101.5 hrs0.0x

0.5x

1.0x

1.5x

2.0x

2.5x




Speed-u

p v

s D

ual H

asw

ell

rg-val

22

Effects of using K80s7.4

hrs

27.9

hrs

2.5

hrs

55.4

hrs

229.0

hrs

6.4

hrs

25.8

hrs

2.1

hrs

43.6

hrs

168.4

hrs

5.8

hrs

22.6

hrs

1.7

hrs

33.7

hrs

141.7

hrs

5.1

hrs

20.1

hrs

1.5

hrs

26.8

hrs

101.5

hrs

0.0x

0.3x

0.5x

0.8x

1.0x

1.3x

1.5x

1.8x

2.0x

2.3x

2.5x

rg-a25 rg-a25td rg-on rg-ontd rg-val

Speed-u

p o

ver

run w

ithout

GPU

s

Number of K80 boards

Effects of using K80 boardsHaswell E5-2698 [email protected]

0 1 2 4

23

Gaussian 16 Supported Platforms

• 4-way collaboration; Gaussian, Inc., PGI, NVIDIA and HPE

• HPE, NVIDIA and PGI is the development platform

• All released/certified x86_64 versions of Gaussian 16 use the PGI compilers

• Certified versions of Gaussian 16 use Intel only for Itanium, XLF for some IBM platforms, Fujitsu compilers for some SPARC-based machines and PGI for the rest (including some Apple products)

• GINC is collaborating with IBM, PGI (and NVIDIA) to release an OpenPower version of Gaussian that also uses the PGI compiler

• See Gaussian Supported Platforms for more details: http://gaussian.com/g16/g16_plat.pdf

http://gaussian.com/g16/g16_plat.pdf

24

CLOSING REMARKS

Significant Progress has been made in enabling Gaussian on GPUs with OpenACC

OpenACC is increasingly becoming more versatile

Significant work lies ahead to improve performance

Expand feature set:

PBC, Solvation, MP2, ONIOM, triples-Corrections

25

ACKNOWLEDGEMENTS

Development is taking place with:

Hewlett-Packard (HP) Series SL2500 Servers (Intel® Xeon® E5-2680 v2 (2.8GHz/10-core/25MB/8.0GT-s QPI/115W, DDR3-1866)

NVIDIA® Tesla® GPUs (K40 and later)

PGI Accelerator Compilers (16.x) with OpenACC (2.5 standard)

5/10/2017

GPAW

Increase Performance with Kepler

Running GPAW 10258

The blue nodes contain 1x E5-2687W CPU (8

Cores per CPU).

The green nodes contain 1x E5-2687W CPU (8

Cores per CPU) and 1x or 2x NVIDIA K20X for

the GPU.

0

0.5

1

1.5

2

2.5

3

3.5

Silicon K=1 Silicon K=2 Silicon K=3

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eed

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PU

On

ly

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1 1 1


0

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2

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3


Sp

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up

Co

mp

are

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PU

On

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1.7x

2.2x

2.4x

Running GPAW 10258

The blue nodes contain 1x E5-2687W CPU (8

Cores per CPU).

The green nodes contain 1x E5-2687W CPUs (8

Cores per CPU) and 2x NVIDIA K20 or K20X for

the GPU.


Running GPAW 10258

The blue nodes contain 2x E5-2687W CPUs (8

Cores per CPU).

The green nodes contain 2x E5-2687W CPUs (8

Cores per CPU) and 2x NVIDIA K20 or K20X for

the GPU.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6


Sp

eed

up

Co

mp

are

d t

o C

PU

On

ly

1.3x

1.4x

1.4x

Used with

permission from

Samuli Hakala

34

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39

NWChem

NWChem 6.3 Release with GPU Acceleration

Addresses large complex and challenging molecular-scale scientific

problems in the areas of catalysis, materials, geochemistry and

biochemistry on highly scalable, parallel computing platforms to

obtain the fastest time-to-solution

Researchers can for the first time be able to perform large scale

coupled cluster with perturbative triples calculations utilizing the

NVIDIA GPU technology. A highly scalable multi-reference coupled

cluster capability will also be available in NWChem 6.3.

The software, released under the Educational Community License

2.0, can be downloaded from the NWChem website at

www.nwchem-sw.org

http://www.nwchem-sw.org/

System: cluster consisting

of dual-socket nodes

constructed from:

• 8-core AMD Interlagos

processors

• 64 GB of memory

• Tesla M2090 (Fermi)

GPUs

The nodes are connected

using a high-performance

QDR Infiniband interconnect

Courtesy of Kowolski, K.,

Bhaskaran-Nair, at al @

PNNL, JCTC (submitted)

NWChem - Speedup of the non-iterative calculation for various configurations/tile sizes

Kepler, Faster Performance (NWChem)

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CPU Only CPU + 1x K20X CPU + 2x K20X

Tim

e t

o S

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tio

n (

sec

on

ds

)

165

81

54

Uracil

Uracil Molecule

Performance improves by 2x with one GPU and by 3.1x with 2 GPUs

December 2016

Quantum Espresso 5.4.0

45

AUSURF112 on K80s

Running Quantum Espresso version 5.4.0

The blue node contains Dual Intel Xeon E5-2690 [email protected] [3.5GHz Turbo]

(Broadwell) CPUs

The green node contains Dual Intel Xeon E5-2690 [email protected] [3.5GHz Turbo]

(Broadwell) CPUs + Tesla K80 (autoboost) GPUs

606.00

528.20

480

500

520

540

560

580

600

620

1 Broadwell node 1 node +4x K80 per node

seconds

AUSURF112*Lower is better

1.1X

46

AUSURF112 on P100s PCIe

606.00

515.70

486.90

0

100

200

300

400

500

600

700

1 Broadwell node 1 node +4x P100 PCIe per node

1 node +8x P100 PCIe per node

seconds

AUSURF1120

Running Quantum Espresso version 5.4.0


(Broadwell) CPUs

The green nodes contain Dual Intel Xeon E5-2690 [email protected] [3.5GHz Turbo]

(Broadwell) CPUs + Tesla P100 PCIe GPUs

*Lower is better

1.2X 1.2X

Speaker, Date

TeraChem 1.5K

48

TERACHEM 1.5K; TRIPCAGE ON TESLA K40S

0

40

80

120

160

200

2 x Xeon E5-2697 [email protected] + 1 xTesla K40@875Mhz (1 node)




TeraChem 1.5K; TripCage on Tesla K40s & IVB CPUs(Total Processing Time in Seconds)

49

TERACHEM 1.5K; TRIPCAGE ON TESLA K40S & HASWELL CPUS

0

40

80

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200

2 x Xeon E5-2698 [email protected] + 1 x TeslaK40@875Mhz (1 node)



TeraChem 1.5K; TripCage on Tesla K40s & Haswell CPUs(Total Processing Time in Seconds)

50

TERACHEM 1.5K; TRIPCAGE ON TESLA K80S & IVB CPUS

0

40

80

120

2 x Xeon E5-2697 [email protected] + 1 x Tesla K80 board(1 node)

2 x Xeon E5-2697 [email protected] + 2 x Tesla K80 boards(1 node)

2 x Xeon E5-2697 [email protected] + 4 x Tesla K80 boards(1 node)

TeraChem 1.5K; TripCage on Tesla K80s & IVB CPUs(Total Processing Time in Seconds)

51

TERACHEM 1.5K; TRIPCAGE ON TESLA K80S & HASWELL CPUS

0

30

60

90

120

2 x Xeon E5-2698 [email protected] + 1 x Tesla K80board (1 node)

2 x Xeon E5-2698 [email protected] + 2 x Tesla K80boards (1 node)


TeraChem 1.5K; TripCage on Tesla K80s & Haswell CPUs(Total Processing Time in Seconds)

52

TERACHEM 1.5K; BPTI ON TESLA K40S & IVB CPUS

0

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12000





TeraChem 1.5K; BPTI on Tesla K40s & IVB CPUs(Total Processing Time in Seconds)

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0

2000

4000

6000

8000

2 x Xeon E5-2697 [email protected] + 1 x Tesla K80board (1 node)



TeraChem 1.5K; BPTI on Tesla K80s & IVB CPUs(Total Processing Time in Seconds)

54


0

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TeraChem 1.5K; BPTI on Tesla K40s & Haswell CPUs(Total Processing Time in Seconds)

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TERACHEM 1.5K; BPTI ON TESLA K80S & HASWELL CPUS

0

2000

4000

6000

2 x Xeon E5-2698 [email protected] + 1 x Tesla K80 board(1 node)



TeraChem 1.5K; BPTI on Tesla K80s & Haswell CPUs(Total Processing Time in Seconds)

TeraChemSupercomputer Speeds on GPUs

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4096 Quad Core CPUs ($19,000,000) 8 C2050 ($31,000)

Tim

e (

Seco

nd

s)

Time for SCF Step

TeraChem running on 8 C2050s on 1 node

NWChem running on 4096 Quad Core CPUs

In the Chinook Supercomputer

Giant Fullerene C240 Molecule

Similar performance from just a handful of GPUs

TeraChemBang for the Buck

1

493

0

100

200

300

400

500

600

4096 Quad Core CPUs ($19,000,000) 8 C2050 ($31,000)

Pri

ce/P

erf

orm

an

ce r

ela

tiv

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o S

up

erc

om

pu

ter

Performance/Price

Dollars spent on GPUs do 500x more science than those spent on CPUs

TeraChem running on 8 C2050s on 1 node

NWChem running on 4096 Quad Core

CPUs

In the Chinook Supercomputer

Giant Fullerene C240 Molecule

Note: Typical CPU and GPU node pricing

used. Pricing may vary depending on node

configuration. Contact your preferred HW

vendor for actual pricing.

Kepler’s Even Better

Kepler performs 2x faster than Tesla

TeraChem running on C2050 and K20C

First graph is of BLYP/G-31(d)

Second is B3LYP/6-31G(d)

0

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C2050 K20C

Seco

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Olestra BLYP 453 Atoms

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B3LYP/6-31G(d)

February 2017

VASP 5.4.1

60

Interface on K80s

Running VASP version 5.4.1


(Broadwell) CPUs

The green nodes contain Dual Intel Xeon E5-2699 [email protected] [3.6GHz

Turbo] (Broadwell) CPUs + Tesla K80 (autoboost) GPUs

➢ 1x K80 is paired with Single Intel Xeon E5-2699 [email protected] [3.6GHz

Turbo] (Broadwell)

Interface between a platinum slab Pt(111) (108 atoms) and liquid water (120 water

molecules) (468 ions)

1256 bands762048 plane waves

ALGO = Fast (Davidson + RMM-DIIS)

0.00171 0.00173

0.00238

0.00317

0.0000

0.0010

0.0020

0.0030

0.0040

0.0050




1/se

conds

Interface

1.0X1.4X

1.9X

61

Interface on P100s PCIe



(Broadwell) CPUs


Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs

➢ 1x P100 PCIe is paired with Single Intel Xeon E5-2699 [email protected]

[3.6GHz Turbo] (Broadwell)





0.00171

0.00228

0.00308

0.00359

0.00434

0.00000

0.00050

0.00100

0.00150

0.00200

0.00250

0.00300

0.00350

0.00400

0.00450

0.00500

1 Broadwell node 1 node +1x P100 PCIe

per node

1 node +2x P100 PCIe

per node


per node


per node

1/se

conds

Interface

1.3X

1.8X2.1X

2.5X

62

Interface on P100s SXM2



(Broadwell) CPUs


Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs

➢ 1x P100 SXM2 is paired with Single Intel Xeon E5-2698 [email protected]






0.00171

0.00228

0.00270

0.00326

0.00462

0.00000

0.00050

0.00100

0.00150

0.00200

0.00250

0.00300

0.00350

0.00400

0.00450

0.00500

1 Broadwell node 1 node +1x P100 SXM2

per node

1 node +2x P100 SXM2

per node


per node


per node

1/se

conds

Interface

1.3X1.6X

1.9X

2.7X

63

Silica IFPEN on K80s



(Broadwell) CPUs




Turbo] (Broadwell)

240 ions, cristobalite (high) bulk720 bands

? plane wavesALGO = Very Fast (RMM-DIIS)

0.00273 0.00276

0.00403

0.00481

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600




1/se

conds

Silica IFPEN

1.0X

1.5X

1.8X

64

Silica IFPEN on P100s PCIe



(Broadwell) CPUs







0.00273

0.00380

0.00474

0.00616

0.00674

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700

0.00800


per node


per node


per node


per node

1/se

conds

Silica IFPEN

1.4X

1.7X

2.3X

2.5X

65

Silica IFPEN on P100s SXM2



(Broadwell) CPUs







0.00273

0.00352

0.00475

0.00616

0.00692

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700

0.00800


per node


per node


per node


per node

1/se

conds

Silica IFPEN

1.3X

1.7X

2.3X2.5X

66

Si-Huge on K80s



(Broadwell) CPUs




Turbo] (Broadwell)

512 Si atoms1282 bands

864000 Plane WavesAlgo = Normal (blocked Davidson)

0.00019

0.00024

0.00032

0.00047

0.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

0.00035

0.00040

0.00045

0.00050




1/se

conds

Si-Huge

67

Si-Huge on P100s PCIe



(Broadwell) CPUs







0.00019

0.00034

0.00044

0.00058

0.00074

0.00000

0.00010

0.00020

0.00030

0.00040

0.00050

0.00060

0.00070

0.00080


per node


per node


per node


per node

1/se

conds

Si-Huge

1.8X

2.3X

3.1X

3.9X

68

Si-Huge on P100s SXM2



(Broadwell) CPUs







0.00019

0.00033

0.00040

0.00045

0.00066

0.00000

0.00010

0.00020

0.00030

0.00040

0.00050

0.00060

0.00070


per node


per node


per node


per node

1/se

conds

Si-Huge

1.7X

2.1X2.4X

3.5X

69

SupportedSystems on K80s



(Broadwell) CPUs




Turbo] (Broadwell)

267 ions788 bands

762048 plane wavesALGO = Fast (Davidson + RMM-DIIS)

0.00413 0.00414

0.00519

0.00599

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700




1/se

conds

SupportedSystems

1.0X

1.3X

1.5X

70

SupportedSystems on P100s PCIe



(Broadwell) CPUs





267 ions788 bands


0.00413

0.00518

0.00651

0.00794 0.00796

0.00000

0.00200

0.00400

0.00600

0.00800

0.01000


per node


per node


per node


per node

1/se

conds

SupportedSystems

1.3X

1.6X

1.9X 1.9X

71

SupportedSystems on P100s SXM2



(Broadwell) CPUs





267 ions788 bands


0.00413

0.00516

0.00570

0.00692

0.00938

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700

0.00800

0.00900

0.01000


per node


per node


per node


per node

1/se

conds

SupportedSystems

1.2X1.4X

1.7X

2.3X

72

NiAl-MD on K80s



(Broadwell) CPUs




Turbo] (Broadwell)

500 ions3200 bands


0.003470.00359

0.00537

0.00614

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700




1/se

conds

NiAl-MD

1.0X

1.5X

1.8X

73

NiAl-MD on P100s PCIe



(Broadwell) CPUs





500 ions3200 bands


0.00347

0.00577

0.00731

0.009020.00936

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700

0.00800

0.00900

0.01000


per node


per node


per node


per node

1/se

conds

NiAl-MD

1.7X

2.1X2.6X

2.7X

74

NiAl-MD on P100s SXM2



(Broadwell) CPUs





500 ions3200 bands


0.0035

0.0057

0.0074

0.0081

0.0090

0.0000

0.0010

0.0020

0.0030

0.0040

0.0050

0.0060

0.0070

0.0080

0.0090

0.0100


per node


per node


per node


per node

1/se

conds

NiAl-MD

1.6X

2.1X2.3X

2.6X

75

LiZnO on P100s PCIe



(Broadwell) CPUs



500 ions3200 bands


0.00106

0.00137

0.00153

0.00000

0.00020

0.00040

0.00060

0.00080

0.00100

0.00120

0.00140

0.00160

0.00180


per node


per node

1/se

conds

LiZnO

1.3X1.4X

76

LiZnO on P100s SXM2



(Broadwell) CPUs





500 ions3200 bands


0.00110.0011

0.0013

0.0015

0.0018

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

0.0018

0.0020


per node


per node


per node


per node

1/se

conds

LiZnO

1.0X1.2X

1.4X1.6X

77

B.hR105 on P100s PCIe



(Broadwell) CPUs





105 Boron atoms (β-rhombohedral structure)216 bands

110592 plane wavesHybrid Functional with blocked Davicson

(ALGO=Normal)LHFCALC=.True. (Exact Exchange)

0.00090

0.00223

0.00371

0.00560

0.00702

0.00000

0.00100

0.00200

0.00300

0.00400

0.00500

0.00600

0.00700

0.00800


per node


per node


per node


per node

1/se

conds

B.hR105

2.5X

4.1X

6.2X

7.8X

78

B.hR105 on P100s SXM2



(Broadwell) CPUs





105 Boron atoms (β-rhombohedral structure)216 bands

110592 plane wavesHybrid Functional with blocked Davicson

(ALGO=Normal)LHFCALC=.True. (Exact Exchange)

0.0009

0.0024

0.0039

0.0059

0.0078

0.0000

0.0010

0.0020

0.0030

0.0040

0.0050

0.0060

0.0070

0.0080

0.0090


per node


per node


per node


per node

1/se

cpnds

B.hR105

2.7X

4.3X

6.6X

8.7X

79

B.aP107 on P100s PCIe



(Broadwell) CPUs





107 Boron atoms (symmetry broken 107-atom β′ variant)


Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization.

Hybrid Functional with blocked Davidson (ALGO=Normal)

LHFCALC=.True. (Exact Exchange)

0.00003

0.00012

0.00021

0.00031

0.00041

0.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

0.00035

0.00040

0.00045


per node


per node


per node


per node

1/se

conds

B.aP107

4.0X

7.0X

10.3X

13.7X

80

B.aP107 on P100s SXM2



(Broadwell) CPUs





107 Boron atoms (symmetry broken 107-atom β′ variant)


Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization.

Hybrid Functional with blocked Davidson (ALGO=Normal)

LHFCALC=.True. (Exact Exchange)

0.00003

0.00011

0.00020

0.00027

0.00044

0.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

0.00035

0.00040

0.00045

0.00050


per node


per node


per node


per node

1/se

conds

B.aP107

3.7X

6.7X9.0X

14.7X

Dec, 19, 2016

Quantum Chemistry (QC) on GPUs

Quantum Chemistry (QC) on GPUs - Nvidia · • Gaussian is a Top Ten HPC (Quantum Chemistry) Application. • 80-85% of use cases are GPU-accelerated (Hartree-Fock and DFT: energies,

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