Document 430332
CPU+GPU Molecular Dynamics engine in CHARMM with Biofuels Applica?ons An@-‐Pekka Hynninen Na?onal Renewable Energy Laboratory Golden CO [email protected]@nrel.gov NVIDIA Webinar 11/11/2014 NREL is a na*onal laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. What is CHARMM? • Chemistry at HARvard Molecular Mechanics o o Molecular simula*on package with wild range of features, force fields, and analysis methods Input script is more like a simple programming language – Loops, IF, arithme*c opera*ons, variables, etc. o o o Started late 1960s by Mar*n Karplus group at Harvard Network of developers all over the world Mostly Fortran 90 code – Recently C++ / CUDA parts added o Licensed for academic research groups 2 Classical Molecular Dynamics Take up about 90% of the total CPU *me • Poten?al energy consists of non-‐bonded and bonded interac?ons N bonded N U= ∑u bonded i i=1 + ∑u non−bonded i< j (r −r ) i j • Non-‐bonded interac?ons are set to zero at distances larger than a finite cut-‐off radius Rcut • Dynamics follow Newton’s equa?on of mo?on mi!! ri = Fi = −∇iU Rcut 3 Explicit water molecular dynamics • Typical for biomolecules • Simula?ons are performed in a periodic box to minimize boundary effects • Most (about 90%) of the simulated atoms are water • Atoms are point charges interac?ng via Coulomb and Van der Waals force 4 Par?cle Mesh Ewald (PME) method • Long range Coulomb interac?ons are split into direct and reciprocal parts Direct – range limited by Rcut Reciprocal – computed by FFT Rcut T. Darden, D. York, and L. Pedersen, J. Chem. Phys. 98, 10098 (1993). 5 Domain decomposi?on method • Simula?on box is divided into sub-‐boxes of size bx x by x bz • Single MPI task is assigned to each sub-‐box • The MPI task is responsible for upda?ng the coordinates within the sub-‐box (local-‐box) • In order to calculate the forces, we need to coordinates from an import volume o • • by bx Eighth-‐shell method by D.E. Shaw research* Import volume extends Rcut away from the local-‐ box boundary in pos?ve x, y, and z direc?ons bz Import Minimizes the amount of MPI communica?on Local *K. J. Bowers, R. O. Dror, and D. E. Shaw, J. Comput. Phys., 221, p. 303 (2007). bz Rcut 6 Molecular dynamics (MD) challenges • Inherently non-‐parallel algorithm o o MD *me steps have to be performed one aber another Have to parallelize the actual force calcula*on • Small ?me step -‐ large ?me scales o o o Typical *me step is 1-‐2 fs (10-‐15s) Relevant *me scale ns (10-‐9s) or μs (10-‐6s) Millions or billions of MD steps • MD steps are performed in microsecond wall-‐clock ?me scale o High-‐bandwidth and low-‐latency communica*on required • Hard to use plug-‐in libraries o o Many library provided sor*ng, hashing, or vectorized math func*ons are not useable in MD FFT is the excep*on (FFTW, Intel MKL) • Very compe??ve field with many socware packages o o NAMD, Gromacs, AMBER, LAMMPS, OpenMM, etc. Makes it interes*ng! 7 Why use GPUs for molecular dynamics? • Intel Xeon E5-‐2695 v2 Ivy Bridge-‐EP 2.4GHz o o o $2,313.99 on (newegg.com,5/31/2014) 460.80 Giga FLOPS Cost of a Giga FLOPS = $5 • NVIDIA GeForce GTX Titan Black o o o $1,099.99 on (newegg.com (5/31/2014) 5.1 Tera FLOPS = 5100 Giga FLOPS Giga FLOPS = $0.2 • GPUs are cheaper • GPUs are more compute intensive o o CPUs are complicated. Branch predic*on, large cache memories use up a real estate on the chip GPUs are simple and can devote more transistors to compute tasks • GPUs are not much harder to program than modern CPU/MIC setups o Parallellized, vectorized, and op*mized CPU code 8 CHARMM approach to GPU MD engine • Version 1.0 o o Offload non-‐bonded force calcula*on on GPU NVIDIA CUDA implementa*on only (for now) • Modular o o Core rou*nes wrioen as a standalone CUDA/C++ library Easy switch to different accelerator architecture • CPU code threaded with OpenMP • MPI used for Mul?-‐GPU support • Goal o o o o Make it easy for users to switch to GPUs Not just vanilla MD – “science” features ready to go One executable runs everything Easy to use, simple “on/off” command in the input script. 9 Not just vanilla MD – supported features in CHARMM GPU o Umbrella poten*al (RXNC) o Restrained distances (RESD) o Imposed distance restraints (NOE) o Absolute harmonic constraints (CONS HARM ABSO) o Dihedral constraints (CONS DIHE) o Distance matrix constraints (DMCONS) o Center of mass constraints (CONS HMCM) o Ensemble • In works o Replica exchange (REPD) o Lambda dynamics (BLOCK) o Drude force field 10 CHARMM GPU system requirements • NVIDIA GPU with compute capability 2.0 or greater • NVIDIA CUDA library 5.0 or greater • Fast CPU for best performance • Compiling o ./install.com {em64t, gnu, osx} {ww, mkl} domdec_gpu M • Running o In CHARMM script, before running dynamics “domdec gpu on” 11 CHARMM GPU molecular dynamics cycle MPI node CPU Send local coordinates Receive local coordinates Communicate coordinates among MPI nodes Send import coordinates Bonded & Reciprocal force Receive import coordinates Non-‐bonded force (import) Non-‐bonded force (local) Receive forces Communicate forces among MPI nodes Constraints, integra*on, etc.. Send forces GPU *me • Only non-‐bonded (direct) forces calculated on GPU • Easy to implement, only a few CUDA kernels • Requires highly op*mized (threaded + vectorized) CPU code 12 Non-‐bonded force calcula?on on GPU z y x Sor?ng atoms • Divide simula?on box into even z-‐columns • Divide the z-‐columns into boxes such that each box contains exactly 32 atoms, except possibly the top box 13 Neighborlist search I J 32 Rnl z • • 32 Excluded to avoid double coun*ng I y Neighborlist search finds interac?ng pairs I – J Neighborlist search is done on CPU using bounding boxes o o • Natom J Natom Detailed distance exclusions done on GPU Topological exclusions done on CPU Non-‐bonded calcula?on on GPU is performed on the 32x32 ?le 14 Non-‐bonded force calcula?on on GPU i atoms 0 31 31 t=0 ... ... j atoms 0 ... t=31 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 Exclusion mask • Single warp (32 threads) • Iterate t=0…31 and calculate interac?ons in the 32x32 ?le • Thread p calculates the interac?on between atoms i[(p+t)%32] and j[p] o Index i is offset by p to avoid race condi*on when wri*ng atom i forces • Exclusion mask (= 32 x 32bit integers) takes care of topological exclusions 15 Force and energy accumula?on on GPU • Force calculated in single precision, accumulated in fixed precision o o o 24 bits 15.34762 40 bits FP Q24.40 model used for direct non-‐bonded calcula*on (long long int) Allows for fast force accumula*on using hardware integer atomic opera*ons on NVIDIA GPUs No need for mul*ple force arrays and reduc*on • Energy and virial calculated in single precision, accumulated in double precision NVE simula*on of DHFR (23558 atoms) using CHARMM GPU 16 Benchmark setup • Titan supercomputer o 16 core AMD Opteron & Tesla K20X on each node • Explicit water simula?ons o o o PME with 4th order bspline and ~1Å grid spacing 2fs *me step Hydrogen bonds constrained “SHAKE” • 24k (23558) atoms o Rnl=11Å, Rcut=9Å • 128k (127906) atoms o Rnl=14Å, Rcut=12Å • 177k (176818) atoms o Rnl=12Å, Rcut=9Å 17 Performance on Titan, 24k atoms #CPU cores 0 32 64 96 128 160 192 224 256 60 50 40 ns/day Direct + Reciprocal 30 20 4x Reciprocal Direct 10 CHARMM CPU CHARMM GPU 0 0 2 4 6 8 10 12 14 16 #node 18 Performance on Titan, 128k atoms #CPU cores 0 128 256 384 512 640 768 896 1024 18 16 14 ns/day 12 10 8 6 4 CHARMM CPU 7.7x CHARMM GPU 2 0 0 8 16 24 32 40 48 56 64 #node 19 Performance on Titan, 177k atoms #CPU cores 0 128 256 384 512 640 768 896 1024 20 18 16 14 ns/day 12 10 8 6 4 CHARMM CPU 2 CHARMM GPU 5.5x 0 0 8 16 24 32 40 48 56 64 #node 20 CHARMM GPU on GridMachine • CHARMM GPU code has been tested on the GridMachine cloud servers • GridMachine offers o o o o o a cloud-‐based compute as a service with unprecedented scale enabling you to tackle large compute problems at accessible prices Fully managed, pay for use Get access to thousands of GPUs and CPUs very quickly High-‐end NVIDIA Kepler GPUs • User access and user interface are in the works www.Gridmachine.com [email protected] 21 Comparison to GPU-‐only approach CHARMM GPU 2.0, Tesla K20 OpenMM 6.0.1, Tesla K20 CHARMM GPU, Intel+K20 (skynet) CHARMM GPU, AMD+K20 (Titan) 0 10 20 30 40 50 60 ns/day 23558 atoms with fixed 2fs *me step 22 Conclusions o CHARMM GPU gives 4-‐8x speedup compared to the fastest CHARMM CPU code Gives the best speedup at low node counts and large system sizes Supports many “non-‐vanilla” MD features Beta version included in c39b1 launched in August, improved version in c39b2 coming out in mid-‐February hop://www.charmm.org o Acknowledgements o o o o – – – – – – Charles Brooks, University of Michigan CHARMM community NIH NVIDIA Gridmachine Oak Ridge Leadership Compu*ng Facility (OLCF) for *me on Titan supercomputer 23 Using GPU for Computa2onal Bioenergy Challenges Mike Crowley Antti-Pekka Hynninen NREL Na#onal Renewable Energy Laboratory Innova#on for Our Energy Future Tuesday, November 11, 14 An#-‐Pekka Hynninen Lintao Bu Gregg Beckham James MaChews Mike Crowley Charles Brooks III Josh Buckner Funding for moderniza#on and performance of CHARMM Funding for Biofuels research and method development 2 Tuesday, November 11, 14 Biofuels from Biomass = Cell Walls 3 Tuesday, November 11, 14 Computa:onal Tools • Molecular Mechanics and Dynamics – Sta#s#cal mechanics and thermodynamics – Structure-‐func#on rela#onships – Enzyme design • Quantum Mechanics – Enzyme mechanism and design • Quantum Mechanics/Molecular Mechanics (QMMM) – Whole enzyme reac#on mechanism • Hypothesis genera:on and tes:ng 4 Tuesday, November 11, 14 Relevant Biofuel Molecular Ques:ons • How do exis:ng enzymes degrade biomass? – Can we op#mize enzymes for biofuel industry? • What is the architecture of cell walls? – What is the source of recalcitrance to degrada#on? • How are cell walls constructed? – Can we op#mize cell wall synthesis for deconstruc#on? • Can we test the exis:ng dogma about cell walls, biomass degrading enzymes, and upgrading processes? 5 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil 6 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil 6 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil Cellulose 6 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil Cataly#c Domain Cellulose 6 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil Cataly#c Domain Carbohydrate-‐ binding module Cellulose 6 Tuesday, November 11, 14 Biomass Degrading Enzymes: Cellulase Our approach to enhanced cellulose conversion: Experiments and modeling as complementary tools “Models are to be used, not believed” – Henri Theil Linker Cataly#c Domain Carbohydrate-‐ binding module Cellulose 6 Tuesday, November 11, 14 TrCel7A Processive Cycle -‐Cel7A is the most abundant enzyme in the secretome of Trichoderma reesei -‐GH7 CBHs are the primary component of industrial cellulase mixtures for biofuels produc#on Seidl, et al. PNAS 2009 Tuesday, November 11, 14 TrCel7A hydrolysis -‐ glycosyla2on reac2on QM/MM Aimless shoo:ng Equilibrium path sampling 8 Tuesday, November 11, 14 TrCel7A hydrolysis -‐ glycosyla2on reac2on QM/MM Aimless shoo:ng Equilibrium path sampling 8 Tuesday, November 11, 14 TrCel7A hydrolysis -‐ deglycosyla2on reac2on QM/MM Aimless shoo:ng Equilibrium path sampling 9 Tuesday, November 11, 14 TrCel7A hydrolysis -‐ deglycosyla2on reac2on QM/MM Aimless shoo:ng Equilibrium path sampling 9 Tuesday, November 11, 14 Behavior of water in Tunnel – Bound water • Sites of high residency by not long occupancy • Routes of entry and exit Red waters are invariant (crystal structure); Blue waters visit the tunnel and stay Tuesday, November 11, 14 Behavior of water in Tunnel – Bound water • Sites of high residency by not long occupancy • Routes of entry and exit Red waters are invariant (crystal structure); Blue waters visit the tunnel and stay Tuesday, November 11, 14 TrCel7A processivity RMSD restraints to a target Hamiltonian exchange umbrella sampling 120 windows of 10 ns each Hamiltonian exchange 2GPU each window 11 Tuesday, November 11, 14 TrCel7A processivity RMSD restraints to a target Hamiltonian exchange umbrella sampling 120 windows of 10 ns each Hamiltonian exchange 2GPU each window 11 Tuesday, November 11, 14 TrCel7A processivity RMSD restraints to a target Hamiltonian exchange umbrella sampling 120 windows of 10 ns each Hamiltonian exchange 2GPU each window 11 Tuesday, November 11, 14 TrCel7A processivity 12 Tuesday, November 11, 14 Longer 2me behavior (1 μs) of Linker Why Glycosyla:on on the Linker? Very flexible, protects exposed protein from proteolysis. Ini:ally half of linker binds via glycosyla:on. Eventually all of linker binds and stays bound. 13 Tuesday, November 11, 14 Product Binding Free Energy Calcula:on Using Steered MD force Simple MD Steered MD: distance restraints on COM force Lintao Bu 14 Tuesday, November 11, 14 Product Binding Free Energy Calcula:on Using Steered MD Simple MD Steered MD: distance restraints on COM Lintao Bu 14 Tuesday, November 11, 14 Binging Free Energy Calcula:on Using Steered MD ‹w› = 20.3 ± 4.5 kcal/mol ‹∆F› = 14.4 ± 0.6 kcal/moll Jarzynski Cumula#ve Integral Extrapola#on Linear Extrapola#on § Simple MD Steered MD: distance restraints on COM Jarzynski’s Equality ΔFn = ΔF∞ + φ1 (1/n)τ § Linear Extrapola:on § Cumula:ve Integral Extrapola:on Ytreberg, Zuckerman, JCC, 2004,25:1749. 15 Tuesday, November 11, 14 Carbohydrate Binding Module (CBM) 16 Tuesday, November 11, 14 Crystal face specificity of the CBM • Is there a specific face of cellulose that the CBM binds to? • Verified this face specificity in cellulose Iβ Simple MD Mul:ple star:ng points Tuesday, November 11, 14 Crystal face specificity of the CBM • Is there a specific face of cellulose that the CBM binds to? • Verified this face specificity in cellulose Iβ Simple MD Mul:ple star:ng points Tuesday, November 11, 14 Crystal face specificity of the CBM • Is there a specific face of cellulose that the CBM binds to? • Verified this face specificity in cellulose Iβ Simple MD Mul:ple star:ng points Umbrella sampling Complex reac:on coordinate Tuesday, November 11, 14 How does glycosyla:on affect the CBM binding affinity? •Na:ve O-‐glycan at T1 and S3. Harrison et al., Eur. J. Biochem. 1998 Tuesday, November 11, 14 How does glycosyla:on affect the CBM binding affinity? •Na:ve O-‐glycan at T1 and S3. Tyrosines on binding face Harrison et al., Eur. J. Biochem. 1998 Tuesday, November 11, 14 How does glycosyla:on affect the CBM binding affinity? •Na:ve O-‐glycan at T1 and S3. Na:ve mannosyla:on Harrison et al., Eur. J. Biochem. 1998 Tuesday, November 11, 14 How does glycosyla:on affect the CBM binding affinity? •Na:ve O-‐glycan at T1 and S3. Engineered mannosyla:on Na:ve mannosyla:on Harrison et al., Eur. J. Biochem. 1998 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on Y 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on Y Bind Y 19 Tuesday, November 11, 14 Thermodynamic Cycles Y Thermodynamic Integra:on A Bind Y 19 Tuesday, November 11, 14 Thermodynamic Cycles Y Thermodynamic Integra:on A Bind Y Bind A 19 Tuesday, November 11, 14 Thermodynamic Cycles Y ∆GYbind Y Thermodynamic Integra:on A Bind ∆GAbind Bind A 19 Tuesday, November 11, 14 Thermodynamic Cycles Y ∆GYbind Thermodynamic Integra:on A Bind Y ∆GAbind Bind A ∆∆Gbind = ∆GYbind -‐ ∆GAbind 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on Mutate ∆GYmutate Y ∆GYbind Bind Y A ∆GAbind Bind A ∆∆Gbind = ∆GYbind -‐ ∆GAbind 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on Mutate ∆GYmutate Y ∆GYbind A ∆GAbind Bind Bind Mutate Y ∆GAmutate A ∆∆Gbind = ∆GYbind -‐ ∆GAbind 19 Tuesday, November 11, 14 Thermodynamic Cycles Thermodynamic Integra:on Mutate ∆GYmutate Y ∆GYbind A ∆GAbind Bind Bind Mutate Y ∆GAmutate A ∆∆Gbind = ∆GYbind -‐ ∆GAbind = ∆GYmutate -‐ ∆GAmutate 19 Tuesday, November 11, 14 Method Valida:on: Effect of Amino Acids A F Y W Exptl Calc Y5W -‐0.3 -‐0.4 Y5A posi#ve +2.6 Y5F +0.3 Y5W Cel7A wild type Linder et al., FEBS Lett, 1995 Linder et al., Protein Sci, 1995 National Renewable Energy Laboratory Tuesday, November 11, 14 20 Innovation for Our Energy Future Glycan effect on CBM binding affinity It is known that Y5W improves binding affinity (Kmut/Kwt) by a factor of 2 Courtney Taylor et al., submiCed National Renewable Energy Laboratory Tuesday, November 11, 14 Innovation for Our Energy Future Glycan effect on CBM binding affinity It is known that Y5W improves binding affinity (Kmut/Kwt) by a factor of 2 A single O-‐glycan on the CBM can alter the binding affinity by 20 to 40-‐fold Courtney Taylor et al., submiCed National Renewable Energy Laboratory Tuesday, November 11, 14 Innovation for Our Energy Future Glycan effect on CBM binding affinity It is known that Y5W improves binding affinity (Kmut/Kwt) by a factor of 2 A single O-‐glycan on the CBM can alter the binding affinity by 20 to 40-‐fold Courtney Taylor et al., submiCed National Renewable Energy Laboratory Tuesday, November 11, 14 Innovation for Our Energy Future Glycosylated structure: NMR Refinement Does the glycosylation change the structure of the CBM for better binding? NMR distance restraints produce 30 structures MD refinement selects 10 stable structures 30 structures, 500ns each. National Renewable Energy Laboratory Tuesday, November 11, 14 22 Innovation for Our Energy Future Glycosylated structure: NMR refinement ! Simple MD NMR refinement: NMR distance restraints Backbone clustering: One basic structure B A C ! Sugar orientation: One main orientation for each sugar A National Renewable Energy Laboratory Tuesday, November 11, 14 B 23 C Innovation for Our Energy Future Glycosylated CBM: NMR structure refinement ! Native Native + Engineered Sugar orientation is mostly aligned with binding surface National Renewable Energy Laboratory Tuesday, November 11, 14 24 Innovation for Our Energy Future Decrystalliza2on 25 Tuesday, November 11, 14 Decrystalliza2on scenarios middle Iα middle edge Gregg Beckham corner edge Iβ edge II edge III Beckham, et al, JPCB, 2011 26 Tuesday, November 11, 14 Decrystalliza2on simula2ons: edge chain Umbrella Sampling DMCO: Distance Matrix Constraints “Na:ve contacts” Beckham, et al, JPCB, 2011 27 Tuesday, November 11, 14 Decrystalliza2on simula2ons: Results corner highest E cost to remove lowest E cost to remove middle Beckham, et al, JPCB, 2011 Tuesday, November 11, 14 Decrystalliza2on simula2ons: Results corner highest E cost to remove more diges#ble than lowest E cost to remove more diges#ble than middle Beckham, et al, JPCB, 2011 Tuesday, November 11, 14 Twist in Cellulose Fibril Model 9 chain (3x3) Start: not twisted 29 Tuesday, November 11, 14 Twist in Cellulose Fibril Model 9 chain (3x3) Start: not twisted Minimum Energy: twisted 29 Tuesday, November 11, 14 Twist depends on Diameter Tuesday, November 11, 14 Twist depends on Diameter Tuesday, November 11, 14 H-bonding Intra-chain H-bonds Tuesday, November 11, 14 H-bonding Intra-chain H-bonds HO3-O5 Tuesday, November 11, 14 H-bonding Intra-chain H-bonds HO3-O5 HO2-O6 Tuesday, November 11, 14 H-bonding Intra-chain H-bonds HO3-O5 HO2-O6 Tuesday, November 11, 14 H-bonding Intra-chain H-bonds HO3-O5 HO2-O6 Tuesday, November 11, 14 Inter-chain H-bonds H-bonding Intra-chain H-bonds HO3-O5 HO2-O6 Tuesday, November 11, 14 Inter-chain H-bonds Twist due to O6-HO2 Tuesday, November 11, 14 H-bond Analysis Correct distance no pull or push Long distance Pulls to produce RH twist Tuesday, November 11, 14 H-Bond Analysis for Torque O3 O6 O3 O2 O5 O6 Tuesday, November 11, 14 O5 O2 H-Bond Analysis for Torque O2 O6 O3 O5 Tuesday, November 11, 14 Cellulose Biosynthesis Models • Contributors: James Malhews, Mike Crowley, Mike Himmel Tuesday, November 11, 14 Cellulose Biosynthesis Models • Contributors: James Malhews, Mike Crowley, Mike Himmel Tuesday, November 11, 14 Cellulose Biosynthesis Models How close to wall can Terminal complex func#on? • Contributors: James Malhews, Mike Crowley, Mike Himmel Tuesday, November 11, 14 Cellulose Biosynthesis Models How close to wall can Terminal complex func#on? • Contributors: James Malhews, Mike Crowley, Mike Himmel Tuesday, November 11, 14 36-‐chain fiber forma:on Harmonic COM restraint (HMCM) Modified to be 1D(plane), 2D(line), or 3D (point) Here we use restrain COM to a line 37 Tuesday, November 11, 14 36-‐chain fiber forma:on Harmonic COM restraint (HMCM) Modified to be 1D(plane), 2D(line), or 3D (point) Here we use restrain COM to a line 38 Tuesday, November 11, 14 TEST DRIVE GPU ACCELERATORS TODAY Accelerate your scientific discoveries: ü Reducing simulation time from hours to minutes ü Using the latest Tesla K40 GPUs FREE GPU Trial at: www.nvidia.com/ GPUTestDrive Tuesday, November 11, 14 UPCOMING GTC EXPRESS WEBINARS Wednesday, December 3 DIY Deep Learning for Vision: A Tutorial with Caffe Evan Shelhamer, UC Berkeley Tuesday, November 11, 14 REGISTRATION IS OPEN! March 17-20, 2015 | San Jose, CA www.gputechconf.com #GTC15 20% OFF GM15WEB CONNECT LEARN DISCOVER INNOVATE Connect with experts from NVIDIA and other organizations across a wide range of fields Get key learnings and hands-on training in the 400+ sessions and 150+ research posters Discover the latest technologies shaping the GPU ecosystem Hear about disruptive innovations as earlystage start-ups present their work 4 Days | 3400+ Attendees | 400+ Sessions | 150+ Research Posters 40+ Countries | 180+ Press & Analytics | 100+ Exhibitors Tuesday, November 11, 14
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