Accelerating computational science and engineering with leadership computing Jack C. Wells Director of Science Oak Ridge Leadership Computing Facility NVIDIA Theatre @ SC13 Office of Science
Big Problems Require Big Solutions Climate Change Energy Healthcare Competitiveness 2
What is the Leadership Computing Facility (LCF)? • Collaborative DOE Office of Science • Highly competitive user allocation program at ORNL and ANL programs (INCITE, ALCC). • Mission: Provide the computational • Projects receive 10x to 100x more and data resources required to solve resource than at other generally the most challenging problems. available centers. • 2-centers/2-architectures to address • LCF centers partner with users to diverse and growing computational enable science & engineering needs of the scientific community breakthroughs (Liaisons, Catalysts). 3
#2 Titan System (Cray XK7) 27.1 PF 24.5 PF 2.6 PF Peak Performance 18,688 compute nodes GPU CPU LINPACK Performance 17.59 PF Power 8.2 MW System Memory 710 TB total memory Gemini High Speed Interconnect 3D Torus Interconnect Storage Luster Filesystem 32 PB High-Performance Archive 29 PB Storage System (HPSS) I/O Nodes 512 Service and I/O nodes 4
High-impact science at OLCF: Four of Six SC13 Gordon Bell Finalists Used Titan Peter ¡Staar ¡ Massimo ¡Bernaschi ¡ Michael ¡Bussmann ¡ Salman ¡Habib ¡ ¡ETH ¡Zurich ¡ ICNR-‑IAC ¡Rome ¡ ¡HZDR ¡-‑ ¡Dresden ¡ Argonne ¡ High-‑Temperature ¡ Biofluidic ¡ Superconduc4vity ¡ Systems ¡ Plasma ¡Physics ¡ Cosmology ¡ Taking ¡a ¡Quantum ¡ 20 ¡Petaflops ¡ Radia2ve ¡Signatures ¡ HACC: ¡Extreme ¡ Leap ¡in ¡Time ¡to ¡ Simula2on ¡of ¡ of ¡the ¡Rela2vis2c ¡ Scaling ¡and ¡ Solu2on ¡for ¡ Protein ¡ Kelvin-‑Helmholtz ¡ Performance ¡ Simula2ons ¡of ¡High-‑T C ¡ Suspensions ¡in ¡ Instability ¡ Across ¡Diverse ¡ Superconductors ¡ Crowding ¡ ¡ Architectures ¡ Condi2ons ¡ Titan ¡ ¡ Titan ¡ ¡ Titan ¡ ¡ Sequoia ¡ ¡ (15.4 ¡PF) ¡ (20 ¡PF) ¡ (7.2 ¡PF) ¡ (13.9 ¡PF), ¡ ¡ Titan ¡ 5
Science challenges for LCF in next decade Combustion Science Climate Change Science Increase efficiency by Understand the dynamic 25%-50% and lower ecological and chemical emissions from internal evolution of the climate combustion engines using system with uncertainty advanced fuels and low- quantification of impacts. temperature combustion. Fusion Energy Biomass to Biofuels Develop predictive Enhance the understanding understanding of plasma and production of biofuels for properties, dynamics, and transportation and other bio- interactions with products from biomass. surrounding materials. Optimized Accelerator Designs Solar Energy Optimize designs as the next Improve photovoltaic generations of accelerators . efficiency and lower Detailed models are needed to cost for organic and provide efficient designs of new inorganic materials. light sources. 6
Solar energy Solar ener Key science challenges: Improve photovoltaic efficiency and lower cost for organic and inorganic materials. A photovoltaic material poses difficult challenges in the prediction of morphology, excited state phenomena, transport, and materials aging. Corse-grained MD simulation of phase-separation of a 1:1 weight ratio P3HT/PCBM mixture into donor (white) and acceptor (blue) domains. Science enabled by LCF Capabilities 2013-2016 2016-2020 • Understand growth, interface structure, and • Enable computational screening of stability of heterogeneous polymer blends materials for desired excited-state and necessary for efficient solar conversion. charge transport properties. • Simulations of structure, carrier transport, • Systems-level, multiphysics simulations and defect states in nanomaterials. of practical photovoltaic devices are enabled. • Describe excited state phenomena in homogeneous systems. • Uncertainty quantification enabled for critical integrated materials properties. 7
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LAMMPS Early Science Project Towards Rational Design of Efficient Organic Jan-Michael Carrillo, ORNL Mike Brown, ORNL Photovoltaic Materials Science Objectives and Impact PCBM P3HT (electron acceptor) (electron donor) • Organic photovoltaic (OPV) solar cells are promising renewable energy sources: – Low costs, high-flexibility, and light weight • Bulk-heterojunction (BHJ) active layer morphology and domain size is critical Corse-grained MD simulation of phase-separation for improving performance of a 1:1 weight ratio P3HT/PCBM mixture into donor (white) and acceptor (blue) domains. Titan Simulation: LAMMPS Preliminary Science Results • Titan simulations are 27x larger and 10x longer • Portability: Builds with CUDA or OpenCL – Converged P3HT:PCBM separation in 400ns • Speedups on Titan (GPU+CPU vs. CPU: CGMD time 2X to 15x (mixed precision) depending upon model and simulation • Prediction: Increasing polymer chain length will decrease the size of the electron donor domains – Speedup of 2.5-3x for OPV simulation used here • Prediction: PCBM (fullerene) loading parameter results in an increasing, then decreasing impact on P3HT domain size 9
Biomass to biofuels Biomass to biofuels Key science challenges: Enhance the understanding and production of biofules from biomass for transportation and other bio-products. The main challenge to overcome is the recalcitrance of biomass (cellulosic materials) to hydrolysis. Lignin interacting with crystalline cellulose. Science enabled by increasing LCF Capabilities 2013-2016 2016-2020 • Atomic-detail dynamical models of biomass • Understand the dynamics of enzymatic systems of several million atoms, permitting reactions on biomass by simulating detailed analysis of interactions interactions between microbial systems and cellulosic biomass • Simulations of pretreatment effects on multi- component biomass systems to understand • Design superior enzymes for the bottlenecks in bioconversion conversion of biomass 10
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INCITE Program Boosting Bioenergy and Overcoming Jeremy Smith Recalcitrance Oak Ridge National Laboratory 23 M Titan core hours Molecular Dynamics Simulations Science Objectives and Impact • Optimize biomass pretreatment process by understanding lignin-cellulose interactions on a molecular level • Overcome biomass recalcitrance caused by lignin and the tightly ordered structure of cellulose • Improve efficiency of the biofuel production process Interaction between cellulose fibril (blue) and lignin (pink and green) molecules. and make ethanol less costly Vizualization by M. Matheson (ORNL) Science Results Application Performance Published paper in Biomacromolecules in August • 2012: Used GROMACS on Jaguar to monitor 2013 interactions of 3 million atoms that included • Discovered amorphous cellulose is easier to crystalline and non-crystalline cellulose, lignin, break down because it associates less with and water lignin • 2013: Now run accelerated GROMACS that can take advantage of Titan’s GPUs, making • Phenomenon is not a result of direct interaction the application 10 times bigger and much between lignin and cellulose, but is a water- longer. Current simulations monitor 30 million atoms. mediated effect 12
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ALCC Program Non-Icing Surfaces for Cold Climate Masako Yamada Wind Turbines GE Global Research 40 M Titan core hours Molecular Dynamics Simulations Location of ice Science Objectives and Impact nucleation varies • Understand microscopic mechanism of water dependent on temperature droplets freezing on surfaces and contact angles. • Determine efficacy of non-icing surfaces at different Visualization by M. Matheson operation temperatures (ORNL) Science Results Replicated GE’s experimental results: Hydrophilic Hydrophobic • Hydrophobic surfaces delay the onset of nucleation Performance Achievements • The delay is less pronounced at lower • 5X speed-up from GPU acceleration temperatures • Achieved factor 40X speed-up from new interaction potential for water 14
Center for Accelerated Application Readiness (CAAR) • Focused effort to prepare • Application Teams applications for accelerated – OLCF application lead architectures – Cray engineer – NVIDIA developer • Goals: – Others: local tool & library developers, other – Work with code teams to develop computational scientists and implement strategies for • Single early science problem exposing hierarchical parallelism for our users applications targeted for each app – Maintain code portability across • Explore multiple approached for modern architectures each app – Learn from and share our results – Determine maximum acceleration • Selected six applications from – Determine reproducible path for different science domains and other applications algorithmic motifs 15
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