Quantum Chemistry (QC) on GPUs. March 2018
|
|
|
- Sarah Cooper
- 6 years ago
- Views:
Transcription
1 Quantum Chemistry (QC) on GPUs March 2018
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 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 Single precision dominated (FP32) Uses cufft, CUDA GeForce (Workstations), Tesla (Servers) ECC off Double precision is important (FP64) Uses cublas, cufft, Tensor/Eigen Solvers, 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 ADF BigDFT CP2K DIRAC GAMESS-US Gaussian GPAW FHI-AIMS LATTE LSDalton MOLCAS Mopac2012 NWChem Octopus ONETEP Petot Q-Chem QMCPACK Quantum Espresso GPU Perf compared against dual multi-core x86 CPU socket. Quantum SuperCharger Library RMG TeraChem UNM VASP WL-LSMS 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 GAMESS February 2018
16 1/seconds GAMESS valinomycin rimp2 energy on V100 vs P100 (PCIe 16GB) X X 10.5X X X 7.5X (Untuned on Volta) Running GAMESS 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 P100 PCIe (16GB) or V100 PCIe (16GB) GPUs Broadwell node 1x P100 PCIe per node 2x P100 PCIe per node 4x P100 PCIe per node 1x V100 PCIe per node 2x V100 PCIe per node 4x V100 PCIe per node 16
17 Gaussian 16 March 2018
18 Project Contributors GAUSSIAN 16 A Leading Computation Chemistry Code Mike Frisch, Ph.D. President and CEO Gaussian, Inc. Valinomycin Frequency. APFD G(2d,p) 7X speedup on 8X P100 GPUs Roberto Gomperts NVIDIA Hardware: HPE Apollo 6500 server with dual Intel Xeon E v4 CPUs (2.40GHz; 14 cores/chip, 28 cores total), 256GB memory and 8 Tesla P100 GPU boards (autoboost clocks). Gaussian source code compiled with PGI Accelerator Compilers (17.7) 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 18
19 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
20 6.9 hrs 2.9 hrs 26.6 hrs 8.5 hrs 2.2 hrs 0.7 hrs 53.6 hrs 12.4 hrs hrs 30.5 hrs Speed-up over run without GPUs GAUSSIAN 16 P100 PERFORMANCE Dual-socket 28-core Broadwell vs 8x P100 GPUs 7.5x 7.0x 6.5x 6.0x 5.5x 5.0x 4.5x 4.0x 3.5x 3.0x 2.5x 2.0x 1.5x Number of GPUs none 8x P x 0.5x 0.0x rg-a25 rg-a25td rg-on rg-ontd rg-val 20
21 GPU-ACCELERATED GAUSSIAN 16 AVAILABLE 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 for ground & excited states). More functionality to come. K40, K80, P100 support; B.01 release. 100% PGI OpenACC Port (no CUDA) 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. 21
22 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 per node 2x K80 per node 4x K80 per node 22
23 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 per node 2x K80 per node 4x K80 per node 23
24 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 per node 2x K80 per node 4x K80 per node 24
25 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 per node 2x K80 per node 4x K80 per node 25
26 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 per node 2x K80 per node 4x K80 per node 26
27 Gaussian 16 Supported Platforms 4-way collaboration; Gaussian, Inc., NVIDIA (& PGI) 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: 27
28 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, triples-corrections 28
29 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 (V100) PGI Accelerator Compilers (18.x) with OpenACC (2.5 standard) 3/8/
30 GPAW
31 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
32 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
33 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
34 Used with permission from Samuli Hakala
35
36
37
38 38
39 39
40 40
41 41
42 42
43 43
44 LSDALTON
45 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 45
46 NWChem
47 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
48 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)
49 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
50 Quantum Espresso March 2018
51 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. 51
52 Speed up QUANTUM ESPRESSO PERFORMANCE Benchmark Si63GE-vc-relax x 2 1.2x KNL nodes (640 cores) 10 Dual-Broadwell nodes (360 cores) 10 1xP100 nodes System information: KNL: Cineca System A2 nodes (Xeon Phi 1.4GHz, 68 cores, 16GB MCDRAM and 96GB DDR4 per node) Broadwell: CSCS Piz Dora Cray XC50 nodes (2 Xeon E GHz, 36 cores and 128GB DDR4 per node. P100: CSCS Piz Dora Cray XC50 nodes (1 Xeon E GHz, 18 cores, 64/128GB DD4 and one Tesla P100 per node) 52
53 TeraChem 1.5K Speaker, Date
54 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
55 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) 55
56 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) 56
57 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) 57
58 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) 58
59 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) 59
60 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) 60
61 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) 61
62 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) 62
63 Beyond VASP Development March 2018
64 VASP The Vienna Ab Initio Simulation Package Photo Prof. Georg Kresse Computational Materials Physics University of Vienna For VASP, OpenACC is the way forward for GPU acceleration. Performance is similar to CUDA, and OpenACC dramatically decreases GPU development and maintenance efforts. We re excited to collaborate with NVIDIA and PGI as an early adopter of Unified Memory. 64
65 1/seconds B.aP107 on V100s PCIe (Untuned on Volta) Running VASP version X 1 Broadwell node 2x V100 PCIe per node (16GB) X 4x V100 PCIe per node (16GB) X 8x V100 PCIe per node (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) 65
66 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 per node (16GB) 4x V100 PCIe per node (16GB) 8x V100 PCIe per node (16GB) 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 66
67 Speed-up VASP Dev Version OpenACC Performance silica IFPEN, RMM-DIIS on P vasp_std (5.4.4) vasp_gpu (5.4.4) OpenACC port 40 cores MPI Ranks / Nr. of P100 SXM2 NVLINK Reasons for performance gain: OpenACC port covers more VASP routines OpenACC port planned top down, with complete analysis of the call tree OpenACC port leverages improvements in latest VASP Fortran source base 67
68 VASP Dev Version OpenACC Performance VASP with OpenACC is an ongoing project. The developers in Vienna are integrating some OpenACC into their code base and there are plans to continue porting in that direction. Given that there is ongoing work, chances are the next major release will contain some of the results of this work. But from NV s side, we can neither comment on any release schedule for VASP in general, nor on any feature set for upcoming releases. 68
69 VASP February 2018
70 1/seconds VASP Silica IFPEN on Gold 6140 vs V100 PCIe (32GB) X 1.4X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs Skylake node 1x V100 PCIe per node 2x V100 PCIe per node 4x V100 PCIe per node 8x V100 PCIe per node 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 70
71 1/seconds VASP Silica IFPEN on Gold 6140 vs V100 SXM2 (32GB) X 1.4X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs Skylake node 1x V100 SXM2 per node 2x V100 SXM2 per node 4x V100 SXM2 per node 8x V100 SXM2 per node 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 71
72 1/seconds VASP Silica IFPEN on Platinum 8180 vs V100 PCIe (32GB) X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs Skylake node 2x V100 PCIe per node 4x V100 PCIe per node 8x V100 PCIe per node 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 72
73 1/seconds VASP Silica IFPEN on Platinum 8180 vs V100 SXM2 (32GB) X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs Skylake node 2x V100 SXM2 per node 4x V100 SXM2 per node 8x V100 SXM2 per node 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 73
74 1/seconds VASP Si-Huge on Gold 6140 vs V100 PCIe (32GB) X 1.9X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs Skylake node 1x V100 PCIe per node 2x V100 PCIe per node 4x V100 PCIe per node 8x V100 PCIe per node 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 74
75 1/seconds VASP Si-Huge on Gold 6140 vs V100 SXM2 (32GB) X 1.9X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs Skylake node 1x V100 SXM2 per node 2x V100 SXM2 per node 4x V100 SXM2 per node 8x V100 SXM2 per node 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 75
76 1/seconds VASP Si-Huge on Platinum 8180 vs V100 PCIe (32GB) X 1.6X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs Skylake node 1x V100 PCIe per node 2x V100 PCIe per node 4x V100 PCIe per node 8x V100 PCIe per node 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 76
77 1/seconds VASP Si-Huge on Platinum 8180 vs V100 SXM2 (32GB) X 1.6X X X (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs Skylake node 1x V100 SXM2 per node 2x V100 SXM2 per node 4x V100 SXM2 per node 8x V100 SXM2 per node 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 77
78 1/seconds VASP B.hR105 on Gold 6140 vs V100 PCIe (32GB) X 1 Skylake node 1x V100 PCIe per node X 2x V100 PCIe per node X 4x V100 PCIe per node X 8x V100 PCIe per node (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold 6140@2.30GHz [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E v4@2.6ghz [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 78
79 1/seconds VASP B.hR105 on Gold 6140 vs V100 SXM2 (32GB) X 1 Skylake node 1x V100 SXM2 per node X 2x V100 SXM2 per node X 4x V100 SXM2 per node X 8x V100 SXM2 per node (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Gold 6140@2.30GHz [2.301GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 79
80 1/seconds VASP B.hR105 on Platinum 8180 vs V100 PCIe (32GB) X 1 Skylake node 1x V100 PCIe per node X 2x V100 PCIe per node X 4x V100 PCIe per node X 8x V100 PCIe per node (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum 8180@2.50GHz [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E v4@2.6ghz [3.5GHz Turbo] (Broadwell) CPUs + Tesla V100 PCIe (32GB) GPUs 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 80
81 1/seconds VASP B.hR105 on Platinum 8180 vs V100 SXM2 (32GB) X 1 Skylake node 1x V100 SXM2 per node X 2x V100 SXM2 per node X 4x V100 SXM2 per node X 8x V100 SXM2 per node (Untuned on Volta) Running VASP version The blue node contains Dual Intel Xeon Platinum 8180@2.50GHz [2.501GHz Turbo] (Skylake) CPUs The green nodes contain Dual Intel Xeon E v4@2.2ghz [3.6GHz Turbo] (Broadwell) CPUs + Tesla V100 SXM2 (32GB) GPUs 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 81
82 VASP October 2017
83 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 per node (16GB) 4x V100 PCIe per node (16GB) 8x V100 PCIe per node (16GB) 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 83
84 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 per node (16GB) 4x V100 SXM2 per node (16GB) 8x V100 SXM2 per node (16GB) 240 ions, cristobalite (high) bulk 720 bands? plane waves ALGO = Very Fast (RMM-DIIS) 84
85 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 per node (16GB) 4x V100 PCIe per node (16GB) 8x V100 PCIe per node (16GB) 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 85
86 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 per node (16GB) 4x V100 SXM2 per node (16GB) 8x V100 SXM2 per node (16GB) 512 Si atoms 1282 bands Plane Waves Algo = Normal (blocked Davidson) 86
87 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 per node (16GB) 4x V100 PCIe per node (16GB) 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 87
88 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 per node (16GB) 4x V100 SXM2 per node (16GB) 8x V100 SXM2 per node (16GB) 267 ions 788 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 88
89 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 per node (16GB) 4x V100 PCIe per node (16GB) 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 89
90 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 per node (16GB) 4x V100 SXM2 per node (16GB) 8x V100 SXM2 per node (16GB) 500 ions 3200 bands plane waves ALGO = Fast (Davidson + RMM-DIIS) 90
91 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 per node (16GB) 4x V100 PCIe per node (16GB) 8x V100 PCIe per node (16GB) 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.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 per node (16GB) 4x V100 SXM2 per node (16GB) 8x V100 SXM2 per node (16GB) 105 Boron atoms (β-rhombohedral structure) 216 bands plane waves Hybrid Functional with blocked Davicson (ALGO=Normal) LHFCALC=.True. (Exact Exchange) 92
93 1/seconds B.aP107 on V100s PCIe (Untuned on Volta) Running VASP version X 1 Broadwell node 2x V100 PCIe per node (16GB) X 4x V100 PCIe per node (16GB) X 8x V100 PCIe per node (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) 93
94 1/seconds B.aP107 on V100s SXM (Untuned on Volta) Running VASP version X 1 Broadwell node 2x V100 SXM2 per node (16GB) X 4x V100 SXM2 per node (16GB) 13.8X 8x V100 SXM2 per node (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) 94
95 Quantum Chemistry (QC) on GPUs March 2018
96 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. 96
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
Quantum Chemistry (QC) on GPUs. October 2017
Quantum Chemistry (QC) on GPUs October 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
Quantum 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
STRATEGIES 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
TESLA 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
TESLA 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
INTRODUCTION 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
PERFORMANCE 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
TESLA 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
TESLA 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
CURRENT 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
Quantum 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
ENABLING 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
WHAT 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
HETEROGENEOUS 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
RECENT 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
TESLA 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
GPU 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
GPUs 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
Comparative 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+
OpenACC 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:
An 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,
OPENACC 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
HPC 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
Performance 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
NVIDIA 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,
ACCELERATED 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
The 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
Scaling 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
TECHNICAL 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
CALMIP : 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
VSC 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
NVIDIA 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
Accelerating 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,
The 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
Preparing 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
TECHNICAL 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
Exascale: 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
High 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
SOLUTIONS 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
GPU 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
AMBER 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 -
Hybrid 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
19. 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
IBM 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
TECHNICAL 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
World 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
NAMD 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
GPU 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
PORTING 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
ACCELERATED 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
Performance Evaluation of NWChem Ab-Initio Molecular Dynamics (AIMD) Simulations on the Intel Xeon Phi Processor
* Some names and brands may be claimed as the property of others. Performance Evaluation of NWChem Ab-Initio Molecular Dynamics (AIMD) Simulations on the Intel Xeon Phi Processor E.J. Bylaska 1, M. Jacquelin
CPMD 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
Piz Daint: Application driven co-design of a supercomputer based on Cray s adaptive system design
Piz Daint: Application driven co-design of a supercomputer based on Cray s adaptive system design Sadaf Alam & Thomas Schulthess CSCS & ETHzürich CUG 2014 * Timelines & releases are not precise Top 500
Accelerating 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
CP2K 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
Trends 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
HPC future trends from a science perspective
HPC future trends from a science perspective Simon McIntosh-Smith University of Bristol HPC Research Group simonm@cs.bris.ac.uk 1 Business as usual? We've all got used to new machines being relatively
Accelerating 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
CP2K 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
HPC 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
CRAY XK6 REDEFINING SUPERCOMPUTING. - Sanjana Rakhecha - Nishad Nerurkar
CRAY XK6 REDEFINING SUPERCOMPUTING - Sanjana Rakhecha - Nishad Nerurkar CONTENTS Introduction History Specifications Cray XK6 Architecture Performance Industry acceptance and applications Summary INTRODUCTION
Timothy 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
Building 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
HPC and AI Solution Overview. Garima Kochhar HPC and AI Innovation Lab
HPC and AI Solution Overview Garima Kochhar HPC and AI Innovation Lab 1 Dell EMC HPC and DL team charter Design, develop and integrate HPC and DL Heading systems Lorem ipsum dolor sit amet, consectetur
Interactive 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
April 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
Development 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
Introduction 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
GROMACS (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
Stan 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
HPC Architectures. Types of resource currently in use
HPC Architectures Types of resource currently in use Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_us
VASP Accelerated with GPUs
VASP Accelerated with GPUs Capabilities, Methods, and Road-Map Max Hutchinson University of Chicago; Carnegie Mellon University GTC, May 17th, 2012 Max Hutchinson (UChicago and CMU) GPU VASP GTC 5/17/12
Performance and Energy Usage of Workloads on KNL and Haswell Architectures
Performance and Energy Usage of Workloads on KNL and Haswell Architectures Tyler Allen 1 Christopher Daley 2 Doug Doerfler 2 Brian Austin 2 Nicholas Wright 2 1 Clemson University 2 National Energy Research
John 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.
ACCELERATING 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
ACCELERATING 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++
An 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
Titan - 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
Porting Scalable Parallel CFD Application HiFUN on NVIDIA GPU
Porting Scalable Parallel CFD Application NVIDIA D. V., N. Munikrishna, Nikhil Vijay Shende 1 N. Balakrishnan 2 Thejaswi Rao 3 1. S & I Engineering Solutions Pvt. Ltd., Bangalore, India 2. Aerospace Engineering,
Game-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.
Technologies 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
GROMACS 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
A 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
Mapping MPI+X Applications to Multi-GPU Architectures
Mapping MPI+X Applications to Multi-GPU Architectures A Performance-Portable Approach Edgar A. León Computer Scientist San Jose, CA March 28, 2018 GPU Technology Conference This work was performed under
Pedraforca: 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
GPU 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
Molecular Dynamics and Quantum Mechanics Applications
Understanding the Performance of Molecular Dynamics and Quantum Mechanics Applications on Dell HPC Clusters High-performance computing (HPC) clusters are proving to be suitable environments for running
OCTOPUS 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
GPU 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
NAMD 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
Programming NVIDIA GPUs with OpenACC Directives
Programming NVIDIA GPUs with OpenACC Directives Michael Wolfe michael.wolfe@pgroup.com http://www.pgroup.com/accelerate Programming NVIDIA GPUs with OpenACC Directives Michael Wolfe mwolfe@nvidia.com http://www.pgroup.com/accelerate
HPC with Multicore and GPUs
HPC with Multicore and GPUs Stan Tomov Electrical Engineering and Computer Science Department University of Tennessee, Knoxville COSC 594 Lecture Notes March 22, 2017 1/20 Outline Introduction - Hardware
ANSYS 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
Faster Innovation - Accelerating SIMULIA Abaqus Simulations with NVIDIA GPUs. Baskar Rajagopalan Accelerated Computing, NVIDIA
Faster Innovation - Accelerating SIMULIA Abaqus Simulations with NVIDIA GPUs Baskar Rajagopalan Accelerated Computing, NVIDIA 1 Engineering & IT Challenges/Trends NVIDIA GPU Solutions AGENDA Abaqus GPU
Approaches 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/
HPC Architectures past,present and emerging trends
HPC Architectures past,present and emerging trends Andrew Emerson, Cineca a.emerson@cineca.it 27/09/2016 High Performance Molecular 1 Dynamics - HPC architectures Agenda Computational Science Trends in
General 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
Directive-based Programming for Highly-scalable Nodes
Directive-based Programming for Highly-scalable Nodes Doug Miles Michael Wolfe PGI Compilers & Tools NVIDIA Cray User Group Meeting May 2016 Talk Outline Increasingly Parallel Nodes Exposing Parallelism
First Steps of YALES2 Code Towards GPU Acceleration on Standard and Prototype Cluster
First Steps of YALES2 Code Towards GPU Acceleration on Standard and Prototype Cluster YALES2: Semi-industrial code for turbulent combustion and flows Jean-Matthieu Etancelin, ROMEO, NVIDIA GPU Application