Quantum Chemistry (QC) on GPUs. October 2017
|
|
- Erika Ford
- 6 years ago
- Views:
Transcription
1 Quantum Chemistry (QC) on GPUs October 2017
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, HTMD, 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: green* = application where >90% of the workload is on GPU CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more 2
3 MD vs. QC on GPUs Classical Molecular Dynamics Simulates positions of atoms over time; chemical-biological or chemical-material behaviors Forces calculated from simple empirical formulas (bond rearrangement generally forbidden) Up to millions of atoms Solvent included without difficulty Single precision dominated Uses cublas, cufft, CUDA Geforce (Accademics), Tesla (Servers) ECC off Quantum Chemistry (MO, PW, DFT, Semi-Emp) Calculates electronic properties; ground state, excited states, spectral properties, making/breaking bonds, physical properties Forces derived from electron wave function (bond rearrangement OK, e.g., bond energies) Up to a few thousand atoms Generally in a vacuum but if needed, solvent treated classically (QM/MM) or using implicit methods Double precision is important Uses cublas, cufft, OpenACC Tesla recommended ECC on 3
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. 4
5 GPU-Accelerated Quantum Chemistry Apps Green Lettering Indicates Performance Slides Included Abinit ACES III Gaussian GPAW Octopus ONETEP Quantum SuperCharger Library ADF LATTE Petot RMG BigDFT LSDalton Q-Chem TeraChem CP2K MOLCAS QMCPACK UNM GAMESS-US Mopac2012 NWChem Quantum Espresso VASP WL-LSMS GPU Perf compared against dual multi-core x86 CPU socket. 5
6 ABINIT
7 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
8 BigDFT
9 Courtesy of BigDFT CEA
10 Courtesy of BigDFT CEA
11 Courtesy of BigDFT CEA
12 Courtesy of BigDFT CEA
13 Courtesy of BigDFT CEA
14 Courtesy of BigDFT CEA
15 Gaussian 16 April 2017
16 Project Contributors GAUSSIAN 16 A Leading Computation Chemistry Code Mike Frisch, Ph.D. President and CEO Gaussian, Inc. Valinomycin wb97xd/6-311+(2d,p) Freq 2.25X speedup Roberto Gomperts NVIDIA Hardware: HPE server with dual Intel Xeon E v3 CPUs (2.30GHz ; 16 cores/chip), 256GB memory and 4 Tesla K80 dual GPU boards (boost clocks: MEM 2505 and SM 875). Gaussian source code compiled with PGI Accelerator Compilers (16.5) with OpenACC (2.5 standard). Gaussi an, Inc. 340 Quinnipiac St. Bldg. 40 Wallingford, CT USA custser v@gaussi an.com Michael Frisch Gaussian Brent Leback NVIDIA/PGI Using OpenACC allowed us to continue development of our fundamental algorithms Copyright and 2017, software Gaussian, Inc. All capabilities rights reser ved. simultaneously with the GPU-related work. In the end, we could use the same code base for SMP, cluster/ network and GPU parallelism. PGI's compilers were essential to the success of our efforts. Gaussian is a registered trademark of Gaussian, Inc. All other trademark the proper ties of their respective holders. Specif cations subject to change w 16
17 GPU-ACCELERATED GAUSSIAN 16 AVAILABLE 100% PGI OpenACC Port (no CUDA) Gaussian is a Top Ten HPC (Quantum Chemistry) Application % of use cases are GPU-accelerated (Hartree-Fock and DFT: energies, 1 st derivatives (gradients) and 2 nd 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; jhess@gaussian.com. 17
18 Speed-up vs Dual Haswell rg-a25 on K80s 1.6x 1.4x 1.2x 1.0x 0.8x 0.6x rg-a25 Running Gaussian version 16 The blue node contains Dual Intel Xeon E (Haswell) CPUs The green nodes contain Dual Intel Xeon E (Haswell) CPUs + Tesla K80 (autoboost) GPUs Alanine 25. Two steps: Force and Frequency. APFD 6-31G* natoms = 259, nbasis = x 0.2x 0.0x 7.4 hrs 6.4 hrs 5.8 hrs 5.1 hrs 1 Haswell node 1x K80 2x K80 4x K80 18
19 Speed-up vs Dual Haswell rg-a25td on K80s 1.6x 1.4x 1.2x 1.0x 0.8x 0.6x rg-a25td Running Gaussian version 16 The blue node contains Dual Intel Xeon E (Haswell) CPUs The green nodes contain Dual Intel Xeon E (Haswell) CPUs + Tesla K80 (autoboost) GPUs Alanine 25. Two Time-Dependent (TD) steps: Force and Frequency. APFD 6-31G* natoms = 259, nbasis = x 0.2x 0.0x 27.9 hrs 25.8 hrs 22.6 hrs 20.1 hrs 1 Haswell node 1x K80 2x K80 4x K80 19
20 Speed-up vs Dual Haswell rg-on on K80s 1.8x 1.6x 1.4x 1.2x rg-on Running Gaussian version 16 The blue node contains Dual Intel Xeon E (Haswell) CPUs The green nodes contain Dual Intel Xeon E (Haswell) CPUs + Tesla K80 (autoboost) GPUs 1.0x 0.8x 0.6x GFP ONIOM. Two steps: Force and Frequency. APFD/6-311+G(2d,p):amber=softfirst)=embed natoms = 3715 (48/3667), nbasis = x 0.2x 0.0x 2.5 hrs 2.1 hrs 1.7 hrs 1.5 hrs 1 Haswell node 1x K80 2x K80 4x K80 20
21 Speed-up vs Dual Haswell rg-ontd on K80s 2.5x 2.0x 1.5x rg-ontd Running Gaussian version 16 The blue node contains Dual Intel Xeon E (Haswell) CPUs The green nodes contain Dual Intel Xeon E (Haswell) CPUs + Tesla K80 (autoboost) GPUs 1.0x GFP ONIOM. Two Time-Dependent (TD) steps: Force and Frequency. APFD/6-311+G(2d,p):amber=softfirst)=embed natoms = 3715 (48/3667), nbasis = x 0.0x 55.4 hrs 43.6 hrs 33.7 hrs 26.8 hrs 1 Haswell node 1x K80 2x K80 4x K80 21
22 Speed-up vs Dual Haswell rg-val on K80s 2.5x 2.0x rg-val Running Gaussian version 16 The blue node contains Dual Intel Xeon E (Haswell) CPUs 1.5x The green nodes contain Dual Intel Xeon E (Haswell) CPUs + Tesla K80 (autoboost) GPUs 1.0x Valinomycin. Two steps: Force and Frequency. APFD G(2d,p) natoms = 168, nbasis = x 0.0x hrs hrs hrs hrs 1 Haswell node 1x K80 2x K80 4x K80 22
23 7.4 hrs 6.4 hrs 5.8 hrs 5.1 hrs 27.9 hrs 25.8 hrs 22.6 hrs 20.1 hrs 2.5 hrs 2.1 hrs 1.7 hrs 1.5 hrs 55.4 hrs 43.6 hrs 33.7 hrs 26.8 hrs hrs hrs hrs hrs Speed-up over run without GPUs Effects of using K80s 2.5x 2.3x 2.0x Effects of using K80 boards Haswell E x 1.5x 1.3x 1.0x 0.8x 0.5x 0.3x 0.0x rg-a25 rg-a25td rg-on rg-ontd rg-val Number of K80 boards
24 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: 24
25 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
26 ACKNOWLEDGEMENTS Development is taking place with: Hewlett-Packard (HP) Series SL2500 Servers (Intel Xeon E 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) 10/12/
27 GPAW
28 Speedup Compared to CPU Only Increase Performance with Kepler 3.5 Running GPAW 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 Silicon K=1 Silicon K=2 Silicon K=3
29 Speedup Compared to CPU Only Increase Performance with Kepler x Running GPAW 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 x 1 1.7x Silicon K=1 Silicon K=2 Silicon K=3
30 Speedup Compared to CPU Only Increase Performance with Kepler 1.6 Running GPAW The blue nodes contain 2x E5-2687W CPUs (8 Cores per CPU) x 1.4x The green nodes contain 2x E5-2687W CPUs (8 Cores per CPU) and 2x NVIDIA K20 or K20X for the GPU x Silicon K=1 Silicon K=2 Silicon K=3
31 Used with permission from Samuli Hakala
32
33
34
35 35
36 36
37 37
38 38
39 39
40 40
41 LSDALTON
42 Speedup vs CPU LSDALTON Large-scale application for calculating highaccuracy molecular energies Lines of Code Modified Minimal Effort # of Weeks Required # of Codes to Maintain <100 Lines 1 Week 1 Source Big Performance LS-DALTON CCSD(T) Module Benchmarked on Titan Supercomputer (AMD CPU vs Tesla K20X) 11.7x OpenACC makes GPU computing approachable for domain scientists. Initial OpenACC implementation required only minor effort, and more importantly, no modifications of our existing CPU implementation. Janus Juul Eriksen, PhD Fellow qleap Center for Theoretical Chemistry, Aarhus University 7.9x ALANINE-1 13 ATOMS 8.9x ALANINE-2 23 ATOMS ALANINE-3 33 ATOMS 42
43 NWChem
44 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
45 NWChem - Speedup of the non-iterative calculation for various configurations/tile sizes 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 PNNL, JCTC (submitted)
46 Time to Solution (seconds) Kepler, Faster Performance (NWChem) Uracil CPU Only CPU + 1x K20X CPU + 2x K20X Uracil Molecule Performance improves by 2x with one GPU and by 3.1x with 2 GPUs
47 Quantum Espresso December 2016
48 QUANTUM ESPRESSO Quantum Chemistry Suite Filippo Spiga Head of Research Software Engineering University of Cambridge CUDA Fortran gives us the full performance potential of the CUDA programming model and NVIDIA GPUs.!$CUF KERNELS directives give us productivity and source code maintainability. It s the best of both worlds. 48
49 seconds AUSURF112 on K80s AUSURF112 *Lower is better 580 Running Quantum Espresso version X The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla K80 (autoboost) GPUs Broadwell node 4x K80 49
50 seconds AUSURF112 on P100s PCIe AUSURF1120 *Lower is better X 1.2X Running Quantum Espresso version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs 200 The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs Broadwell node 4x P100 PCIe 8x P100 PCIe 50
51 TeraChem 1.5K Speaker, Date
52 2.8x 2.8x 3.3x All timings for complete energy and gradient Calculations K80 = 1 GPU on Board, not both 3.7x TrpCage 6-31G RHF 1604 bfn 284 atoms TrpCage 6-31G** RHF 2900 bfn 284 atoms Ru Complex LANL2DZ B3LYP 4512 bfn 1013 atoms BPTI STO-3G RHF 2706 bfn 882 atoms 2.7x Olestra 6-31G* RHF 3181 bfn 453 atoms 2.3x Olestra 6-31G* BLYP 3181 bfn 453 atoms Slide courtesy of PetaChem LLC / Todd Martinez
53 TERACHEM 1.5K; TRIPCAGE ON TESLA K40S TeraChem 1.5K; TripCage on Tesla K40s & IVB CPUs (Total Processing Time in Seconds) x Xeon E v2@2.70ghz + 1 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 2 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 4 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 8 x Tesla K40@875Mhz (1 node) 53
54 TERACHEM 1.5K; TRIPCAGE ON TESLA K40S & HASWELL CPUS 200 TeraChem 1.5K; TripCage on Tesla K40s & Haswell CPUs (Total Processing Time in Seconds) x Xeon E v3@2.30ghz + 1 x Tesla K40@875Mhz (1 node) 2 x Xeon E v3@2.30ghz + 2 x Tesla K40@875Mhz (1 node) 2 x Xeon E v3@2.30ghz + 4 x Tesla K40@875Mhz (1 node) 54
55 TERACHEM 1.5K; TRIPCAGE ON TESLA K80S & IVB CPUS 120 TeraChem 1.5K; TripCage on Tesla K80s & IVB CPUs (Total Processing Time in Seconds) x Xeon E v2@2.70ghz + 1 x Tesla K80 board (1 node) 2 x Xeon E v2@2.70ghz + 2 x Tesla K80 boards 2 x Xeon E v2@2.70ghz + 4 x Tesla K80 boards (1 node) (1 node) 55
56 TERACHEM 1.5K; TRIPCAGE ON TESLA K80S & HASWELL CPUS 120 TeraChem 1.5K; TripCage on Tesla K80s & Haswell CPUs (Total Processing Time in Seconds) x Xeon E v3@2.30ghz + 1 x Tesla K80 board (1 node) 2 x Xeon E v3@2.30ghz + 2 x Tesla K80 boards (1 node) 2 x Xeon E v3@2.30ghz + 4 x Tesla K80 boards (1 node) 56
57 TERACHEM 1.5K; BPTI ON TESLA K40S & IVB CPUS TeraChem 1.5K; BPTI on Tesla K40s & IVB CPUs (Total Processing Time in Seconds) x Xeon E v2@2.70ghz + 1 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 2 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 4 x Tesla K40@875Mhz (1 node) 2 x Xeon E v2@2.70ghz + 8 x Tesla K40@875Mhz (1 node) 57
58 TERACHEM 1.5K; BPTI ON TESLA K80S & IVB CPUS 8000 TeraChem 1.5K; BPTI on Tesla K80s & IVB CPUs (Total Processing Time in Seconds) x Xeon E v2@2.70ghz + 1 x Tesla K80 board (1 node) 2 x Xeon E v2@2.70ghz + 2 x Tesla K80 boards (1 node) 2 x Xeon E v2@2.70ghz + 4 x Tesla K80 boards (1 node) 58
59 TERACHEM 1.5K; BPTI ON TESLA K40S & IVB CPUS TeraChem 1.5K; BPTI on Tesla K40s & Haswell CPUs (Total Processing Time in Seconds) x Xeon E v3@2.30ghz + 1 x Tesla K40@875Mhz (1 node) 2 x Xeon E v3@2.30ghz + 2 x Tesla K40@875Mhz (1 node) 2 x Xeon E v3@2.30ghz + 4 x Tesla K40@875Mhz (1 node) 59
60 TERACHEM 1.5K; BPTI ON TESLA K80S & HASWELL CPUS 6000 TeraChem 1.5K; BPTI on Tesla K80s & Haswell CPUs (Total Processing Time in Seconds) x Xeon E v3@2.30ghz + 1 x Tesla K80 board (1 node) 2 x Xeon E v3@2.30ghz + 2 x Tesla K80 boards (1 node) 2 x Xeon E v3@2.30ghz + 4 x Tesla K80 boards (1 node) 60
61 Time (Seconds) TeraChem Supercomputer Speeds on GPUs 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 Quad Core CPUs ($19,000,000) 8 C2050 ($31,000) Similar performance from just a handful of GPUs
62 Price/Performance relative to Supercomputer TeraChem Bang for the Buck Performance/Price 493 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 Quad Core CPUs ($19,000,000) 8 C2050 ($31,000) Dollars spent on GPUs do 500x more science than those spent on CPUs
63 Seconds Seconds Kepler s Even Better 800 Olestra BLYP 453 Atoms 2000 B3LYP/6-31G(d) TeraChem running on C2050 and K20C First graph is of BLYP/G-31(d) Second is B3LYP/6-31G(d) C2050 K20C 0 C2050 K20C Kepler performs 2x faster than Tesla
64 VASP October 2017
65 1/seconds Silica IFPEN on V100s PCIe X X 3.0X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs Broadwell node 2x V100 PCIe (16GB) 4x V100 PCIe (16GB) 8x V100 PCIe (16GB) 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 65
66 1/seconds Silica IFPEN on V100s SXM X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs Broadwell node 2x V100 SXM2 (16GB) 4x V100 SXM2 (16GB) 8x V100 SXM2 (16GB) 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 66
67 1/seconds Si-Huge on V100s PCIe X X 3.8X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs Broadwell node 2x V100 PCIe (16GB) 4x V100 PCIe (16GB) 8x V100 PCIe (16GB) 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 67
68 1/seconds Si-Huge on V100s SXM X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs Broadwell node 2x V100 SXM2 (16GB) 4x V100 SXM2 (16GB) 8x V100 SXM2 (16GB) 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 68
69 1/seconds SupportedSystems on V100s PCIe X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs Broadwell node 2x V100 PCIe (16GB) 4x V100 PCIe (16GB) 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 69
70 1/seconds SupportedSystems on V100s SXM X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs Broadwell node 2x V100 SXM2 (16GB) 4x V100 SXM2 (16GB) 8x V100 SXM2 (16GB) 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 70
71 1/seconds NiAl-MD on V100s PCIe X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs Broadwell node 2x V100 PCIe (16GB) 4x V100 PCIe (16GB) 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 71
72 1/seconds NiAl-MD on V100s SXM X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs Broadwell node 2x V100 SXM2 (16GB) 4x V100 SXM2 (16GB) 8x V100 SXM2 (16GB) 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 72
73 1/seconds B.hR105 on V100s PCIe X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs Broadwell node 2x V100 PCIe (16GB) 4x V100 PCIe (16GB) 8x V100 PCIe (16GB) 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 73
74 1/seconds B.hR105 on V100s SXM X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs Broadwell node 2x V100 SXM2 (16GB) 4x V100 SXM2 (16GB) 8x V100 SXM2 (16GB) 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 74
75 1/seconds B.aP107 on V100s PCIe (Untuned on Volta) Running VASP version X 1 Broadwell node 2x V100 PCIe (16GB) X 4x V100 PCIe (16GB) X 8x V100 PCIe (16GB) The blue node contains Dual Intel Xeon E v4@2.6ghz [3.5GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E v4@2.6ghz [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (16GB) GPUs 107 Boron atoms (symmetry broken 107-atom β variant) 216 bands plane waves Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization. Hybrid Functional with blocked Davidson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 75
76 1/seconds B.aP107 on V100s SXM (Untuned on Volta) Running VASP version X 1 Broadwell node 2x V100 SXM2 (16GB) X 4x V100 SXM2 (16GB) 13.8X 8x V100 SXM2 (16GB) The blue node contains Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (16GB) GPUs 107 Boron atoms (symmetry broken 107-atom β variant) 216 bands plane waves Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization. Hybrid Functional with blocked Davidson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 76
77 VASP February 2017
78 1/seconds Interface on P100s PCIe Interface X X X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs X 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Interface between a platinum slab Pt(111) (108 atoms) and liquid water (120 water molecules) (468 ions) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 1256 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 78
79 1/seconds Interface on P100s SXM Interface X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X X 1.6X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Interface between a platinum slab Pt(111) (108 atoms) and liquid water (120 water molecules) (468 ions) Broadwell node 1x P100 SXM2 2x P100 SXM2 4x P100 SXM2 8x P100 SXM bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 79
80 1/seconds Silica IFPEN on P100s PCIe Silica IFPEN X X X X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 80
81 1/seconds Silica IFPEN on P100s SXM Silica IFPEN X X X X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 SXM2 2x P100 SXM2 4x P100 SXM2 8x P100 SXM2 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 81
82 1/seconds Si-Huge on P100s PCIe Si-Huge X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X X 3.1X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 82
83 1/seconds Si-Huge on P100s SXM Si-Huge X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X X 2.4X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 SXM2 2x P100 SXM2 4x P100 SXM2 8x P100 SXM2 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 83
84 1/seconds SupportedSystems on P100s PCIe SupportedSystems Running VASP version X X X 1.9X The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 84
85 1/seconds SupportedSystems on P100s SXM SupportedSystems X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X 1.2X 1.4X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 SXM2 2x P100 SXM2 4x P100 SXM2 8x P100 SXM2 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 85
86 1/seconds NiAl-MD on P100s PCIe X NiAl-MD X X 2.7X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 86
87 1/seconds NiAl-MD on P100s SXM X NiAl-MD X X X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 SXM2 2x P100 SXM2 4x P100 SXM2 8x P100 SXM2 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 87
88 1/seconds LiZnO on P100s PCIe LiZnO X 1.4X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs Broadwell node 2x P100 PCIe 4x P100 PCIe 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 88
89 1/seconds LiZnO on P100s SXM LiZnO Running VASP version X X X 1.6X The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 89
90 1/seconds B.hR105 on P100s PCIe B.hR X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs X 1 Broadwell node 1x P100 PCIe 4.1X 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 90
91 1/secpnds B.hR105 on P100s SXM X 1 Broadwell node 1x P100 SXM2 B.hR X 2x P100 SXM X 4x P100 SXM X 8x P100 SXM2 Running VASP version The blue node contains Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 91
92 1/seconds B.aP107 on P100s PCIe B.aP X Running VASP version The blue node contains Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs X X The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 PCIe GPUs 1x P100 PCIe is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) X 1 Broadwell node 1x P100 PCIe 2x P100 PCIe 4x P100 PCIe 8x P100 PCIe 107 Boron atoms (symmetry broken 107-atom β variant) 216 bands plane waves Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization. Hybrid Functional with blocked Davidson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 92
93 1/seconds B.aP107 on P100s SXM X 1 Broadwell node 1x P100 SXM2 B.aP X 2x P100 SXM X 4x P100 SXM X 8x P100 SXM2 Running VASP version The blue node contains Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs The green nodes contain Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs + Tesla P100 SXM2 GPUs 1x P100 SXM2 is paired with Single Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) 107 Boron atoms (symmetry broken 107-atom β variant) 216 bands plane waves Hybrid functional calculation (exact exchange) with blocked Davidson. No KPoint parallelization. Hybrid Functional with blocked Davidson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 93
94 Quantum Chemistry (QC) on GPUs Dec, 19, 2016
95 GPU-Accelerated Molecular Dynamics Apps Green Lettering Indicates Performance Slides Included ACEMD AMBER CHARMM DESMOND ESPResSO GENESIS GPUGrid.net GROMACS HALMD HOOMD-Blue HTMD LAMMPS mdcore MELD NAMD OpenMM PolyFTS GPU Perf compared against dual multi-core x86 CPU socket. 95
Quantum Chemistry (QC) on GPUs. Feb. 2, 2017
Quantum Chemistry (QC) on GPUs Feb. 2, 2017 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
More informationQuantum Chemistry (QC) on GPUs. March 2018
Quantum Chemistry (QC) on GPUs March 2018 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 #
More informationQuantum Chemistry (QC) on GPUs. Dec. 19, 2016
Quantum Chemistry (QC) on GPUs Dec. 19, 2016 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
More informationTESLA ACCELERATED COMPUTING. Mike Wang Solutions Architect NVIDIA Australia & NZ
TESLA ACCELERATED COMPUTING Mike Wang Solutions Architect NVIDIA Australia & NZ mikewang@nvidia.com GAMING DESIGN ENTERPRISE VIRTUALIZATION HPC & CLOUD SERVICE PROVIDERS AUTONOMOUS MACHINES PC DATA CENTER
More informationPERFORMANCE PORTABILITY WITH OPENACC. Jeff Larkin, NVIDIA, November 2015
PERFORMANCE PORTABILITY WITH OPENACC Jeff Larkin, NVIDIA, November 2015 TWO TYPES OF PORTABILITY FUNCTIONAL PORTABILITY PERFORMANCE PORTABILITY The ability for a single code to run anywhere. The ability
More informationINTRODUCTION TO OPENACC. Analyzing and Parallelizing with OpenACC, Feb 22, 2017
INTRODUCTION TO OPENACC Analyzing and Parallelizing with OpenACC, Feb 22, 2017 Objective: Enable you to to accelerate your applications with OpenACC. 2 Today s Objectives Understand what OpenACC is and
More informationTESLA P100 PERFORMANCE GUIDE. HPC and Deep Learning Applications
TESLA P PERFORMANCE GUIDE HPC and Deep Learning Applications MAY 217 TESLA P PERFORMANCE GUIDE Modern high performance computing (HPC) data centers are key to solving some of the world s most important
More informationTESLA V100 PERFORMANCE GUIDE. Life Sciences Applications
TESLA V100 PERFORMANCE GUIDE Life Sciences Applications NOVEMBER 2017 TESLA V100 PERFORMANCE GUIDE Modern high performance computing (HPC) data centers are key to solving some of the world s most important
More informationTESLA P100 PERFORMANCE GUIDE. Deep Learning and HPC Applications
TESLA P PERFORMANCE GUIDE Deep Learning and HPC Applications SEPTEMBER 217 TESLA P PERFORMANCE GUIDE Modern high performance computing (HPC) data centers are key to solving some of the world s most important
More informationSTRATEGIES TO ACCELERATE VASP WITH GPUS USING OPENACC. Stefan Maintz, Dr. Markus Wetzstein
STRATEGIES TO ACCELERATE VASP WITH GPUS USING OPENACC Stefan Maintz, Dr. Markus Wetzstein smaintz@nvidia.com; mwetzstein@nvidia.com Companies Academia VASP USERS AND USAGE 12-25% of CPU cycles @ supercomputing
More informationCURRENT STATUS OF THE PROJECT TO ENABLE GAUSSIAN 09 ON GPGPUS
CURRENT STATUS OF THE PROJECT TO ENABLE GAUSSIAN 09 ON GPGPUS Roberto Gomperts (NVIDIA, Corp.) Michael Frisch (Gaussian, Inc.) Giovanni Scalmani (Gaussian, Inc.) Brent Leback (PGI) TOPICS Gaussian Design
More informationENABLING NEW SCIENCE GPU SOLUTIONS
ENABLING NEW SCIENCE TESLA BIO Workbench The NVIDIA Tesla Bio Workbench enables biophysicists and computational chemists to push the boundaries of life sciences research. It turns a standard PC into a
More informationGPU ACCELERATED COMPUTING. 1 st AlsaCalcul GPU Challenge, 14-Jun-2016, Strasbourg Frédéric Parienté, Tesla Accelerated Computing, NVIDIA Corporation
GPU ACCELERATED COMPUTING 1 st AlsaCalcul GPU Challenge, 14-Jun-2016, Strasbourg Frédéric Parienté, Tesla Accelerated Computing, NVIDIA Corporation GAMING PRO ENTERPRISE VISUALIZATION DATA CENTER AUTO
More informationRECENT TRENDS IN GPU ARCHITECTURES. Perspectives of GPU computing in Science, 26 th Sept 2016
RECENT TRENDS IN GPU ARCHITECTURES Perspectives of GPU computing in Science, 26 th Sept 2016 NVIDIA THE AI COMPUTING COMPANY GPU Computing Computer Graphics Artificial Intelligence 2 NVIDIA POWERS WORLD
More informationWHAT S NEW IN CUDA 8. Siddharth Sharma, Oct 2016
WHAT S NEW IN CUDA 8 Siddharth Sharma, Oct 2016 WHAT S NEW IN CUDA 8 Why Should You Care >2X Run Computations Faster* Solve Larger Problems** Critical Path Analysis * HOOMD Blue v1.3.3 Lennard-Jones liquid
More informationOpenACC Course Lecture 1: Introduction to OpenACC September 2015
OpenACC Course Lecture 1: Introduction to OpenACC September 2015 Course Objective: Enable you to accelerate your applications with OpenACC. 2 Oct 1: Introduction to OpenACC Oct 6: Office Hours Oct 15:
More informationHETEROGENEOUS HPC, ARCHITECTURAL OPTIMIZATION, AND NVLINK STEVE OBERLIN CTO, TESLA ACCELERATED COMPUTING NVIDIA
HETEROGENEOUS HPC, ARCHITECTURAL OPTIMIZATION, AND NVLINK STEVE OBERLIN CTO, TESLA ACCELERATED COMPUTING NVIDIA STATE OF THE ART 2012 18,688 Tesla K20X GPUs 27 PetaFLOPS FLAGSHIP SCIENTIFIC APPLICATIONS
More informationTESLA V100 PERFORMANCE GUIDE May 2018
TESLA V100 PERFORMANCE GUIDE May 2018 TESLA V100 The Fastest and Most Productive GPU for AI and HPC Volta Architecture Tensor Core Improved NVLink & HBM2 Volta MPS Improved SIMT Model Most Productive GPU
More informationAn Innovative Massively Parallelized Molecular Dynamic Software
Renewable energies Eco-friendly production Innovative transport Eco-efficient processes Sustainable resources An Innovative Massively Parallelized Molecular Dynamic Software Mohamed Hacene, Ani Anciaux,
More informationACCELERATED COMPUTING: THE PATH FORWARD. Jen-Hsun Huang, Co-Founder and CEO, NVIDIA SC15 Nov. 16, 2015
ACCELERATED COMPUTING: THE PATH FORWARD Jen-Hsun Huang, Co-Founder and CEO, NVIDIA SC15 Nov. 16, 2015 COMMODITY DISRUPTS CUSTOM SOURCE: Top500 ACCELERATED COMPUTING: THE PATH FORWARD It s time to start
More informationTECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Accelerated computing is revolutionizing the economics of the data center. HPC and hyperscale customers deploy accelerated
More informationGPUs and the Future of Accelerated Computing Emerging Technology Conference 2014 University of Manchester
NVIDIA GPU Computing A Revolution in High Performance Computing GPUs and the Future of Accelerated Computing Emerging Technology Conference 2014 University of Manchester John Ashley Senior Solutions Architect
More informationTECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Accelerated computing is revolutionizing the economics of the data center. HPC enterprise and hyperscale customers deploy
More informationHPC with the NVIDIA Accelerated Computing Toolkit Mark Harris, November 16, 2015
HPC with the NVIDIA Accelerated Computing Toolkit Mark Harris, November 16, 2015 Accelerators Surge in World s Top Supercomputers 125 100 75 Top500: # of Accelerated Supercomputers 100+ accelerated systems
More informationVSC Users Day 2018 Start to GPU Ehsan Moravveji
Outline A brief intro Available GPUs at VSC GPU architecture Benchmarking tests General Purpose GPU Programming Models VSC Users Day 2018 Start to GPU Ehsan Moravveji Image courtesy of Nvidia.com Generally
More informationHigh Performance Computing with Accelerators
High Performance Computing with Accelerators Volodymyr Kindratenko Innovative Systems Laboratory @ NCSA Institute for Advanced Computing Applications and Technologies (IACAT) National Center for Supercomputing
More informationPerformance Study of Popular Computational Chemistry Software Packages on Cray HPC Systems
Performance Study of Popular Computational Chemistry Software Packages on Cray HPC Systems Junjie Li (lijunj@iu.edu) Shijie Sheng (shengs@iu.edu) Raymond Sheppard (rsheppar@iu.edu) Pervasive Technology
More informationOPENACC ONLINE COURSE 2018
OPENACC ONLINE COURSE 2018 Week 1 Introduction to OpenACC Jeff Larkin, Senior DevTech Software Engineer, NVIDIA ABOUT THIS COURSE 3 Part Introduction to OpenACC Week 1 Introduction to OpenACC Week 2 Data
More informationQuantum ESPRESSO on GPU accelerated systems
Quantum ESPRESSO on GPU accelerated systems Massimiliano Fatica, Everett Phillips, Josh Romero - NVIDIA Filippo Spiga - University of Cambridge/ARM (UK) MaX International Conference, Trieste, Italy, January
More informationNAMD GPU Performance Benchmark. March 2011
NAMD GPU Performance Benchmark March 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory
More informationThe Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations
The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations Ophir Maor HPC Advisory Council ophir@hpcadvisorycouncil.com The HPC-AI Advisory Council World-wide HPC non-profit
More informationScaling in a Heterogeneous Environment with GPUs: GPU Architecture, Concepts, and Strategies
Scaling in a Heterogeneous Environment with GPUs: GPU Architecture, Concepts, and Strategies John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology
More informationPreparing GPU-Accelerated Applications for the Summit Supercomputer
Preparing GPU-Accelerated Applications for the Summit Supercomputer Fernanda Foertter HPC User Assistance Group Training Lead foertterfs@ornl.gov This research used resources of the Oak Ridge Leadership
More informationAMBER 11 Performance Benchmark and Profiling. July 2011
AMBER 11 Performance Benchmark and Profiling July 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource -
More informationTECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Table of Contents: The Accelerated Data Center Optimizing Data Center Productivity Same Throughput with Fewer Server Nodes
More informationHybrid KAUST Many Cores and OpenACC. Alain Clo - KAUST Research Computing Saber Feki KAUST Supercomputing Lab Florent Lebeau - CAPS
+ Hybrid Computing @ KAUST Many Cores and OpenACC Alain Clo - KAUST Research Computing Saber Feki KAUST Supercomputing Lab Florent Lebeau - CAPS + Agenda Hybrid Computing n Hybrid Computing n From Multi-Physics
More informationCALMIP : HIGH PERFORMANCE COMPUTING
CALMIP : HIGH PERFORMANCE COMPUTING Nicolas.renon@univ-tlse3.fr Emmanuel.courcelle@inp-toulouse.fr CALMIP (UMS 3667) Espace Clément Ader www.calmip.univ-toulouse.fr CALMIP :Toulouse University Computing
More informationGame-changing Extreme GPU computing with The Dell PowerEdge C4130
Game-changing Extreme GPU computing with The Dell PowerEdge C4130 A Dell Technical White Paper This white paper describes the system architecture and performance characterization of the PowerEdge C4130.
More informationAccelerating High Performance Computing.
Accelerating High Performance Computing http://www.nvidia.com/tesla Computing The 3 rd Pillar of Science Drug Design Molecular Dynamics Seismic Imaging Reverse Time Migration Automotive Design Computational
More informationThe State of Accelerated Applications. Michael Feldman
The State of Accelerated Applications Michael Feldman Accelerator Market in HPC Nearly half of all new HPC systems deployed incorporate accelerators Accelerator hardware performance has been advancing
More informationIBM CORAL HPC System Solution
IBM CORAL HPC System Solution HPC and HPDA towards Cognitive, AI and Deep Learning Deep Learning AI / Deep Learning Strategy for Power Power AI Platform High Performance Data Analytics Big Data Strategy
More informationCP2K Performance Benchmark and Profiling. April 2011
CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC
More informationAccelerating Insights In the Technical Computing Transformation
Accelerating Insights In the Technical Computing Transformation Dr. Rajeeb Hazra Vice President, Data Center Group General Manager, Technical Computing Group June 2014 TOP500 Highlights Intel Xeon Phi
More informationTimothy Lanfear, NVIDIA HPC
GPU COMPUTING AND THE Timothy Lanfear, NVIDIA FUTURE OF HPC Exascale Computing will Enable Transformational Science Results First-principles simulation of combustion for new high-efficiency, lowemision
More informationComparative Benchmarking of the First Generation of HPC-Optimised Arm Processors on Isambard
Prof Simon McIntosh-Smith Isambard PI University of Bristol / GW4 Alliance Comparative Benchmarking of the First Generation of HPC-Optimised Arm Processors on Isambard Isambard system specification 10,000+
More informationApril 4-7, 2016 Silicon Valley INSIDE PASCAL. Mark Harris, October 27,
April 4-7, 2016 Silicon Valley INSIDE PASCAL Mark Harris, October 27, 2016 @harrism INTRODUCING TESLA P100 New GPU Architecture CPU to CPUEnable the World s Fastest Compute Node PCIe Switch PCIe Switch
More informationAccelerating Implicit LS-DYNA with GPU
Accelerating Implicit LS-DYNA with GPU Yih-Yih Lin Hewlett-Packard Company Abstract A major hindrance to the widespread use of Implicit LS-DYNA is its high compute cost. This paper will show modern GPU,
More informationGROMACS Performance Benchmark and Profiling. August 2011
GROMACS Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource
More informationGROMACS (GPU) Performance Benchmark and Profiling. February 2016
GROMACS (GPU) Performance Benchmark and Profiling February 2016 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Mellanox, NVIDIA Compute
More informationCP2K Performance Benchmark and Profiling. April 2011
CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council HPC works working group activities Participating vendors: HP, Intel, Mellanox
More informationWorld s most advanced data center accelerator for PCIe-based servers
NVIDIA TESLA P100 GPU ACCELERATOR World s most advanced data center accelerator for PCIe-based servers HPC data centers need to support the ever-growing demands of scientists and researchers while staying
More informationNVIDIA GPU TECHNOLOGY UPDATE
NVIDIA GPU TECHNOLOGY UPDATE May 2015 Axel Koehler Senior Solutions Architect, NVIDIA NVIDIA: The VISUAL Computing Company GAMING DESIGN ENTERPRISE VIRTUALIZATION HPC & CLOUD SERVICE PROVIDERS AUTONOMOUS
More information19. prosince 2018 CIIRC Praha. Milan Král, IBM Radek Špimr
19. prosince 2018 CIIRC Praha Milan Král, IBM Radek Špimr CORAL CORAL 2 CORAL Installation at ORNL CORAL Installation at LLNL Order of Magnitude Leap in Computational Power Real, Accelerated Science ACME
More informationGPU computing at RZG overview & some early performance results. Markus Rampp
GPU computing at RZG overview & some early performance results Markus Rampp Introduction Outline Hydra configuration overview GPU software environment Benchmarking and porting activities Team Renate Dohmen
More informationCPMD Performance Benchmark and Profiling. February 2014
CPMD Performance Benchmark and Profiling February 2014 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting
More informationExascale: challenges and opportunities in a power constrained world
Exascale: challenges and opportunities in a power constrained world Carlo Cavazzoni c.cavazzoni@cineca.it SuperComputing Applications and Innovation Department CINECA CINECA non profit Consortium, made
More informationNVIDIA Update and Directions on GPU Acceleration for Earth System Models
NVIDIA Update and Directions on GPU Acceleration for Earth System Models Stan Posey, HPC Program Manager, ESM and CFD, NVIDIA, Santa Clara, CA, USA Carl Ponder, PhD, Applications Software Engineer, NVIDIA,
More informationNAMD Performance Benchmark and Profiling. January 2015
NAMD Performance Benchmark and Profiling January 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource
More informationLAMMPS Performance Benchmark and Profiling. July 2012
LAMMPS Performance Benchmark and Profiling July 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC
More informationPORTING CP2K TO THE INTEL XEON PHI. ARCHER Technical Forum, Wed 30 th July Iain Bethune
PORTING CP2K TO THE INTEL XEON PHI ARCHER Technical Forum, Wed 30 th July Iain Bethune (ibethune@epcc.ed.ac.uk) Outline Xeon Phi Overview Porting CP2K to Xeon Phi Performance Results Lessons Learned Further
More informationBuilding NVLink for Developers
Building NVLink for Developers Unleashing programmatic, architectural and performance capabilities for accelerated computing Why NVLink TM? Simpler, Better and Faster Simplified Programming No specialized
More informationHPC Issues for DFT Calculations. Adrian Jackson EPCC
HC Issues for DFT Calculations Adrian Jackson ECC Scientific Simulation Simulation fast becoming 4 th pillar of science Observation, Theory, Experimentation, Simulation Explore universe through simulation
More informationANSYS Improvements to Engineering Productivity with HPC and GPU-Accelerated Simulation
ANSYS Improvements to Engineering Productivity with HPC and GPU-Accelerated Simulation Ray Browell nvidia Technology Theater SC12 1 2012 ANSYS, Inc. nvidia Technology Theater SC12 HPC Revolution Recent
More informationTrends in HPC (hardware complexity and software challenges)
Trends in HPC (hardware complexity and software challenges) Mike Giles Oxford e-research Centre Mathematical Institute MIT seminar March 13th, 2013 Mike Giles (Oxford) HPC Trends March 13th, 2013 1 / 18
More informationOCTOPUS Performance Benchmark and Profiling. June 2015
OCTOPUS Performance Benchmark and Profiling June 2015 2 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the
More informationTitan - Early Experience with the Titan System at Oak Ridge National Laboratory
Office of Science Titan - Early Experience with the Titan System at Oak Ridge National Laboratory Buddy Bland Project Director Oak Ridge Leadership Computing Facility November 13, 2012 ORNL s Titan Hybrid
More informationGPU ACCELERATION OF WSMP (WATSON SPARSE MATRIX PACKAGE)
GPU ACCELERATION OF WSMP (WATSON SPARSE MATRIX PACKAGE) NATALIA GIMELSHEIN ANSHUL GUPTA STEVE RENNICH SEID KORIC NVIDIA IBM NVIDIA NCSA WATSON SPARSE MATRIX PACKAGE (WSMP) Cholesky, LDL T, LU factorization
More informationDevelopment of Intel MIC Codes in NWChem. Edoardo Aprà Pacific Northwest National Laboratory
Development of Intel MIC Codes in NWChem Edoardo Aprà Pacific Northwest National Laboratory Acknowledgements! Karol Kowalski (PNNL)! Michael Klemm (Intel)! Kiran Bhaskaran-Nair (LSU)! Wenjing Ma (Chinese
More informationJohn Levesque Nov 16, 2001
1 We see that the GPU is the best device available for us today to be able to get to the performance we want and meet our users requirements for a very high performance node with very high memory bandwidth.
More informationTechnology for a better society. hetcomp.com
Technology for a better society hetcomp.com 1 J. Seland, C. Dyken, T. R. Hagen, A. R. Brodtkorb, J. Hjelmervik,E Bjønnes GPU Computing USIT Course Week 16th November 2011 hetcomp.com 2 9:30 10:15 Introduction
More informationAn Introduction to OpenACC
An Introduction to OpenACC Alistair Hart Cray Exascale Research Initiative Europe 3 Timetable Day 1: Wednesday 29th August 2012 13:00 Welcome and overview 13:15 Session 1: An Introduction to OpenACC 13:15
More informationLAMMPSCUDA GPU Performance. April 2011
LAMMPSCUDA GPU Performance April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory Council
More informationSOLUTIONS BRIEF: Transformation of Modern Healthcare
SOLUTIONS BRIEF: Transformation of Modern Healthcare Healthcare & The Intel Xeon Scalable Processor Intel is committed to bringing the best of our manufacturing, design and partner networks to enable our
More informationn N c CIni.o ewsrg.au
@NCInews NCI and Raijin National Computational Infrastructure 2 Our Partners General purpose, highly parallel processors High FLOPs/watt and FLOPs/$ Unit of execution Kernel Separate memory subsystem GPGPU
More informationGPU Clusters for High- Performance Computing Jeremy Enos Innovative Systems Laboratory
GPU Clusters for High- Performance Computing Jeremy Enos Innovative Systems Laboratory National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Presentation Outline NVIDIA
More informationACCELERATING CFD AND RESERVOIR SIMULATIONS WITH ALGEBRAIC MULTI GRID Chris Gottbrath, Nov 2016
ACCELERATING CFD AND RESERVOIR SIMULATIONS WITH ALGEBRAIC MULTI GRID Chris Gottbrath, Nov 2016 Challenges What is Algebraic Multi-Grid (AMG)? AGENDA Why use AMG? When to use AMG? NVIDIA AmgX Results 2
More informationACCELERATED COMPUTING: THE PATH FORWARD. Jensen Huang, Founder & CEO SC17 Nov. 13, 2017
ACCELERATED COMPUTING: THE PATH FORWARD Jensen Huang, Founder & CEO SC17 Nov. 13, 2017 COMPUTING AFTER MOORE S LAW Tech Walker 40 Years of CPU Trend Data 10 7 GPU-Accelerated Computing 10 5 1.1X per year
More informationA Comprehensive Study on the Performance of Implicit LS-DYNA
12 th International LS-DYNA Users Conference Computing Technologies(4) A Comprehensive Study on the Performance of Implicit LS-DYNA Yih-Yih Lin Hewlett-Packard Company Abstract This work addresses four
More informationLAMMPS-KOKKOS Performance Benchmark and Profiling. September 2015
LAMMPS-KOKKOS Performance Benchmark and Profiling September 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, NVIDIA
More informationGPU COMPUTING AND THE FUTURE OF HPC. Timothy Lanfear, NVIDIA
GPU COMPUTING AND THE FUTURE OF HPC Timothy Lanfear, NVIDIA ~1 W ~3 W ~100 W ~30 W 1 kw 100 kw 20 MW Power-constrained Computers 2 EXASCALE COMPUTING WILL ENABLE TRANSFORMATIONAL SCIENCE RESULTS First-principles
More informationApproaches to acceleration: GPUs vs Intel MIC. Fabio AFFINITO SCAI department
Approaches to acceleration: GPUs vs Intel MIC Fabio AFFINITO SCAI department Single core Multi core Many core GPU Intel MIC 61 cores 512bit-SIMD units from http://www.karlrupp.net/ from http://www.karlrupp.net/
More informationIntroduction to High Performance Computing. Shaohao Chen Research Computing Services (RCS) Boston University
Introduction to High Performance Computing Shaohao Chen Research Computing Services (RCS) Boston University Outline What is HPC? Why computer cluster? Basic structure of a computer cluster Computer performance
More informationPedraforca: a First ARM + GPU Cluster for HPC
www.bsc.es Pedraforca: a First ARM + GPU Cluster for HPC Nikola Puzovic, Alex Ramirez We ve hit the power wall ALL computers are limited by power consumption Energy-efficient approaches Multi-core Fujitsu
More informationStan Posey, NVIDIA, Santa Clara, CA, USA
Stan Posey, sposey@nvidia.com NVIDIA, Santa Clara, CA, USA NVIDIA Strategy for CWO Modeling (Since 2010) Initial focus: CUDA applied to climate models and NWP research Opportunities to refactor code with
More informationGROMACS Performance Benchmark and Profiling. September 2012
GROMACS Performance Benchmark and Profiling September 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource
More informationThe Exascale Era Has Arrived
Technology Spotlight The Exascale Era Has Arrived Sponsored by NVIDIA Steve Conway, Earl Joseph, Bob Sorensen, and Alex Norton November 2018 EXECUTIVE SUMMARY Earlier this year, scientists broke the exascale
More informationBig Data Analytics Performance for Large Out-Of- Core Matrix Solvers on Advanced Hybrid Architectures
Procedia Computer Science Volume 51, 2015, Pages 2774 2778 ICCS 2015 International Conference On Computational Science Big Data Analytics Performance for Large Out-Of- Core Matrix Solvers on Advanced Hybrid
More informationInteractive Supercomputing for State-of-the-art Biomolecular Simulation
Interactive Supercomputing for State-of-the-art Biomolecular Simulation John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of
More informationGeneral Purpose GPU Computing in Partial Wave Analysis
JLAB at 12 GeV - INT General Purpose GPU Computing in Partial Wave Analysis Hrayr Matevosyan - NTC, Indiana University November 18/2009 COmputationAL Challenges IN PWA Rapid Increase in Available Data
More informationTechnologies and application performance. Marc Mendez-Bermond HPC Solutions Expert - Dell Technologies September 2017
Technologies and application performance Marc Mendez-Bermond HPC Solutions Expert - Dell Technologies September 2017 The landscape is changing We are no longer in the general purpose era the argument of
More informationGPU Architecture. Alan Gray EPCC The University of Edinburgh
GPU Architecture Alan Gray EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? Architectural reasons for accelerator performance advantages Latest GPU Products From
More informationSystem Design of Kepler Based HPC Solutions. Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering.
System Design of Kepler Based HPC Solutions Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering. Introduction The System Level View K20 GPU is a powerful parallel processor! K20 has
More informationGPGPUs in HPC. VILLE TIMONEN Åbo Akademi University CSC
GPGPUs in HPC VILLE TIMONEN Åbo Akademi University 2.11.2010 @ CSC Content Background How do GPUs pull off higher throughput Typical architecture Current situation & the future GPGPU languages A tale of
More informationHIGH-PERFORMANCE COMPUTING WITH NVIDIA TESLA GPUS. Chris Butler NVIDIA
HIGH-PERFORMANCE COMPUTING WITH NVIDIA TESLA GPUS Chris Butler NVIDIA Science is Desperate for Throughput Gigaflops 1,000,000,000 1 Exaflop 1,000,000 1 Petaflop Bacteria 100s of Chromatophores Chromatophore
More informationACCELERATING HPC APPLICATIONS ON NVIDIA GPUS WITH OPENACC
ACCELERATING HPC APPLICATIONS ON NVIDIA GPUS WITH OPENACC Doug Miles, PGI Compilers & Tools, NVIDIA High Performance Computing Advisory Council February 21, 2018 PGI THE NVIDIA HPC SDK Fortran, C & C++
More informationHOKUSAI System. Figure 0-1 System diagram
HOKUSAI System October 11, 2017 Information Systems Division, RIKEN 1.1 System Overview The HOKUSAI system consists of the following key components: - Massively Parallel Computer(GWMPC,BWMPC) - Application
More informationFast Quantum Molecular Dynamics on Multi-GPU Architectures in LATTE
Fast Quantum Molecular Dynamics on Multi-GPU Architectures in LATTE S. Mniszewski*, M. Cawkwell, A. Niklasson GPU Technology Conference San Jose, California March 18-21, 2013 *smm@lanl.gov Slide 1 Background:
More informationCUDA. Matthew Joyner, Jeremy Williams
CUDA Matthew Joyner, Jeremy Williams Agenda What is CUDA? CUDA GPU Architecture CPU/GPU Communication Coding in CUDA Use cases of CUDA Comparison to OpenCL What is CUDA? What is CUDA? CUDA is a parallel
More informationGPU WORKSHOP. NYU Center for Data Science. Bradley Palmer Solution Architect
GPU WORKSHOP NYU Center for Data Science Bradley Palmer Solution Architect HOW GPU ACCELERATION WORKS Application Code Compute-Intensive Functions GPU 5% of Code Rest of Sequential CPU Code CPU + 2 TAKE
More informationarxiv: v1 [cs.dc] 3 Jul 2015
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations arxiv:1507.00898v1 [cs.dc] 3 Jul 2015 Carsten Kutzner,, Szilárd Páll, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, and Helmut
More information