ORNL Site Report
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Petaflops for the People
PETAFLOPS SPOTLIGHT: NERSC housands of researchers have used facilities of the Advanced T Scientific Computing Research (ASCR) program and its EXTREME-WEATHER Department of Energy (DOE) computing predecessors over the past four decades. Their studies of hurricanes, earthquakes, NUMBER-CRUNCHING green-energy technologies and many other basic and applied Certain problems lend themselves to solution by science problems have, in turn, benefited millions of people. computers. Take hurricanes, for instance: They’re They owe it mainly to the capacity provided by the National too big, too dangerous and perhaps too expensive Energy Research Scientific Computing Center (NERSC), the Oak to understand fully without a supercomputer. Ridge Leadership Computing Facility (OLCF) and the Argonne Leadership Computing Facility (ALCF). Using decades of global climate data in a grid comprised of 25-kilometer squares, researchers in These ASCR installations have helped train the advanced Berkeley Lab’s Computational Research Division scientific workforce of the future. Postdoctoral scientists, captured the formation of hurricanes and typhoons graduate students and early-career researchers have worked and the extreme waves that they generate. Those there, learning to configure the world’s most sophisticated same models, when run at resolutions of about supercomputers for their own various and wide-ranging projects. 100 kilometers, missed the tropical cyclones and Cutting-edge supercomputing, once the purview of a small resulting waves, up to 30 meters high. group of experts, has trickled down to the benefit of thousands of investigators in the broader scientific community. Their findings, published inGeophysical Research Letters, demonstrated the importance of running Today, NERSC, at Lawrence Berkeley National Laboratory; climate models at higher resolution. -
Unlocking the Full Potential of the Cray XK7 Accelerator
Unlocking the Full Potential of the Cray XK7 Accelerator ∗ † Mark D. Klein , and, John E. Stone ∗National Center for Supercomputing Application, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA †Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA Abstract—The Cray XK7 includes NVIDIA GPUs for ac- [2], [3], [4], and for high fidelity movie renderings with the celeration of computing workloads, but the standard XK7 HVR volume rendering software.2 In one example, the total system software inhibits the GPUs from accelerating OpenGL turnaround time for an HVR movie rendering of a trillion- and related graphics-specific functions. We have changed the operating mode of the XK7 GPU firmware, developed a custom cell inertial confinement fusion simulation [5] was reduced X11 stack, and worked with Cray to acquire an alternate driver from an estimate of over a month for data transfer to and package from NVIDIA in order to allow users to render and rendering on a conventional visualization cluster down to post-process their data directly on Blue Waters. Users are able just one day when rendered locally using 128 XK7 nodes to use NVIDIA’s hardware OpenGL implementation which has on Blue Waters. The fully-graphics-enabled GPU state is many features not available in software rasterizers. By elim- inating the transfer of data to external visualization clusters, currently considered an unsupported mode of operation by time-to-solution for users has been improved tremendously. In Cray, and to our knowledge Blue Waters is presently the one case, XK7 OpenGL rendering has cut turnaround time only Cray system currently running in this mode. -
Cray XT and Cray XE Y Y System Overview
Crayyy XT and Cray XE System Overview Customer Documentation and Training Overview Topics • System Overview – Cabinets, Chassis, and Blades – Compute and Service Nodes – Components of a Node Opteron Processor SeaStar ASIC • Portals API Design Gemini ASIC • System Networks • Interconnection Topologies 10/18/2010 Cray Private 2 Cray XT System 10/18/2010 Cray Private 3 System Overview Y Z GigE X 10 GigE GigE SMW Fibre Channels RAID Subsystem Compute node Login node Network node Boot /Syslog/Database nodes 10/18/2010 Cray Private I/O and Metadata nodes 4 Cabinet – The cabinet contains three chassis, a blower for cooling, a power distribution unit (PDU), a control system (CRMS), and the compute and service blades (modules) – All components of the system are air cooled A blower in the bottom of the cabinet cools the blades within the cabinet • Other rack-mounted devices within the cabinet have their own internal fans for cooling – The PDU is located behind the blower in the back of the cabinet 10/18/2010 Cray Private 5 Liquid Cooled Cabinets Heat exchanger Heat exchanger (XT5-HE LC only) (LC cabinets only) 48Vdc flexible Cage 2 buses Cage 2 Cage 1 Cage 1 Cage VRMs Cage 0 Cage 0 backplane assembly Cage ID controller Interconnect 01234567 Heat exchanger network cable Cage inlet (LC cabinets only) connection air temp sensor Airflow Heat exchanger (slot 3 rail) conditioner 48Vdc shelf 3 (XT5-HE LC only) 48Vdc shelf 2 L1 controller 48Vdc shelf 1 Blower speed controller (VFD) Blooewer PDU line filter XDP temperature XDP interface & humidity sensor -
This Is Your Presentation Title
Introduction to GPU/Parallel Computing Ioannis E. Venetis University of Patras 1 Introduction to GPU/Parallel Computing www.prace-ri.eu Introduction to High Performance Systems 2 Introduction to GPU/Parallel Computing www.prace-ri.eu Wait, what? Aren’t we here to talk about GPUs? And how to program them with CUDA? Yes, but we need to understand their place and their purpose in modern High Performance Systems This will make it clear when it is beneficial to use them 3 Introduction to GPU/Parallel Computing www.prace-ri.eu Top 500 (June 2017) CPU Accel. Rmax Rpeak Power Rank Site System Cores Cores (TFlop/s) (TFlop/s) (kW) National Sunway TaihuLight - Sunway MPP, Supercomputing Center Sunway SW26010 260C 1.45GHz, 1 10.649.600 - 93.014,6 125.435,9 15.371 in Wuxi Sunway China NRCPC National Super Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Computer Center in Cluster, Intel Xeon E5-2692 12C 2 Guangzhou 2.200GHz, TH Express-2, Intel Xeon 3.120.000 2.736.000 33.862,7 54.902,4 17.808 China Phi 31S1P NUDT Swiss National Piz Daint - Cray XC50, Xeon E5- Supercomputing Centre 2690v3 12C 2.6GHz, Aries interconnect 3 361.760 297.920 19.590,0 25.326,3 2.272 (CSCS) , NVIDIA Tesla P100 Cray Inc. DOE/SC/Oak Ridge Titan - Cray XK7 , Opteron 6274 16C National Laboratory 2.200GHz, Cray Gemini interconnect, 4 560.640 261.632 17.590,0 27.112,5 8.209 United States NVIDIA K20x Cray Inc. DOE/NNSA/LLNL Sequoia - BlueGene/Q, Power BQC 5 United States 16C 1.60 GHz, Custom 1.572.864 - 17.173,2 20.132,7 7.890 4 Introduction to GPU/ParallelIBM Computing www.prace-ri.eu How do -
Hardware Complexity and Software Challenges
Trends in HPC: hardware complexity and software challenges Mike Giles Oxford University Mathematical Institute Oxford e-Research Centre ACCU talk, Oxford June 30, 2015 Mike Giles (Oxford) HPC Trends June 30, 2015 1 / 29 1 { 10? 10 { 100? 100 { 1000? Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Question ?! How many cores in my Dell M3800 laptop? Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Question ?! How many cores in my Dell M3800 laptop? 1 { 10? 10 { 100? 100 { 1000? Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Question ?! How many cores in my Dell M3800 laptop? 1 { 10? 10 { 100? 100 { 1000? Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Top500 supercomputers Really impressive { 300× more capability in 10 years! Mike Giles (Oxford) HPC Trends June 30, 2015 3 / 29 My personal experience 1982: CDC Cyber 205 (NASA Langley) 1985{89: Alliant FX/8 (MIT) 1989{92: Stellar (MIT) 1990: Thinking Machines CM5 (UTRC) 1987{92: Cray X-MP/Y-MP (NASA Ames, Rolls-Royce) 1993{97: IBM SP2 (Oxford) 1998{2002: SGI Origin (Oxford) 2002 { today: various x86 clusters (Oxford) 2007 { today: various GPU systems/clusters (Oxford) 2011{15: GPU cluster (Emerald @ RAL) 2008 { today: Cray XE6/XC30 (HECToR/ARCHER @ EPCC) 2013 { today: Cray XK7 with GPUs (Titan @ ORNL) Mike Giles (Oxford) HPC Trends June 30, 2015 4 / 29 Top500 supercomputers Power requirements are raising the question of whether this rate of improvement is sustainable. -
Jaguar and Kraken -The World's Most Powerful Computer Systems
Jaguar and Kraken -The World's Most Powerful Computer Systems Arthur Bland Cray Users’ Group 2010 Meeting Edinburgh, UK May 25, 2010 Abstract & Outline At the SC'09 conference in November 2009, Jaguar and Kraken, both located at ORNL, were crowned as the world's fastest computers (#1 & #3) by the web site www.Top500.org. In this paper, we will describe the systems, present results from a number of benchmarks and applications, and talk about future computing in the Oak Ridge Leadership Computing Facility. • Cray computer systems at ORNL • System Architecture • Awards and Results • Science Results • Exascale Roadmap 2 CUG2010 – Arthur Bland Jaguar PF: World’s most powerful computer— Designed for science from the ground up Peak performance 2.332 PF System memory 300 TB Disk space 10 PB Disk bandwidth 240+ GB/s Based on the Sandia & Cray Compute Nodes 18,688 designed Red Storm System AMD “Istanbul” Sockets 37,376 Size 4,600 feet2 Cabinets 200 3 CUG2010 – Arthur Bland (8 rows of 25 cabinets) Peak performance 1.03 petaflops Kraken System memory 129 TB World’s most powerful Disk space 3.3 PB academic computer Disk bandwidth 30 GB/s Compute Nodes 8,256 AMD “Istanbul” Sockets 16,512 Size 2,100 feet2 Cabinets 88 (4 rows of 22) 4 CUG2010 – Arthur Bland Climate Modeling Research System Part of a research collaboration in climate science between ORNL and NOAA (National Oceanographic and Atmospheric Administration) • Phased System Delivery • Total System Memory – CMRS.1 (June 2010) 260 TF – 248 TB DDR3-1333 – CMRS.2 (June 2011) 720 TF • File Systems -
The Gemini Network
The Gemini Network Rev 1.1 Cray Inc. © 2010 Cray Inc. All Rights Reserved. Unpublished Proprietary Information. This unpublished work is protected by trade secret, copyright and other laws. Except as permitted by contract or express written permission of Cray Inc., no part of this work or its content may be used, reproduced or disclosed in any form. Technical Data acquired by or for the U.S. Government, if any, is provided with Limited Rights. Use, duplication or disclosure by the U.S. Government is subject to the restrictions described in FAR 48 CFR 52.227-14 or DFARS 48 CFR 252.227-7013, as applicable. Autotasking, Cray, Cray Channels, Cray Y-MP, UNICOS and UNICOS/mk are federally registered trademarks and Active Manager, CCI, CCMT, CF77, CF90, CFT, CFT2, CFT77, ConCurrent Maintenance Tools, COS, Cray Ada, Cray Animation Theater, Cray APP, Cray Apprentice2, Cray C90, Cray C90D, Cray C++ Compiling System, Cray CF90, Cray EL, Cray Fortran Compiler, Cray J90, Cray J90se, Cray J916, Cray J932, Cray MTA, Cray MTA-2, Cray MTX, Cray NQS, Cray Research, Cray SeaStar, Cray SeaStar2, Cray SeaStar2+, Cray SHMEM, Cray S-MP, Cray SSD-T90, Cray SuperCluster, Cray SV1, Cray SV1ex, Cray SX-5, Cray SX-6, Cray T90, Cray T916, Cray T932, Cray T3D, Cray T3D MC, Cray T3D MCA, Cray T3D SC, Cray T3E, Cray Threadstorm, Cray UNICOS, Cray X1, Cray X1E, Cray X2, Cray XD1, Cray X-MP, Cray XMS, Cray XMT, Cray XR1, Cray XT, Cray XT3, Cray XT4, Cray XT5, Cray XT5h, Cray Y-MP EL, Cray-1, Cray-2, Cray-3, CrayDoc, CrayLink, Cray-MP, CrayPacs, CrayPat, CrayPort, Cray/REELlibrarian, CraySoft, CrayTutor, CRInform, CRI/TurboKiva, CSIM, CVT, Delivering the power…, Dgauss, Docview, EMDS, GigaRing, HEXAR, HSX, IOS, ISP/Superlink, LibSci, MPP Apprentice, ND Series Network Disk Array, Network Queuing Environment, Network Queuing Tools, OLNET, RapidArray, RQS, SEGLDR, SMARTE, SSD, SUPERLINK, System Maintenance and Remote Testing Environment, Trusted UNICOS, TurboKiva, UNICOS MAX, UNICOS/lc, and UNICOS/mp are trademarks of Cray Inc. -
A Cray Manufactured XE6 System Called “HERMIT” at HLRS in Use in Autumn 2011
Press release For release 21/04/2011 More capacity to the PRACE Research Infrastructure: a Cray manufactured XE6 system called “HERMIT” at HLRS in use in autumn 2011 PRACE, the Partnership for Advanced Computing in Europe, adds more capacity to the PRACE Research Infrastructure as the Gauss Centre for Supercomputing (GCS) partner High Performance Computing Center of University Stuttgart (HLRS) will deploy the first installation step of a system called Hermit, a 1 Petaflop/s Cray system in the autumn of 2011. This first installation step is to be followed by a 4-5 Petaflop/s second step in 2013. The contract with Cray includes the delivery of a Cray XE6 supercomputer and the future delivery of Cray's next-generation supercomputer code-named "Cascade." The new Cray system at HLRS will serve as a supercomputing resource for researchers, scientists and engineers throughout Europe. HLRS is one of the leading centers in the European PRACE initiative and is currently the only large European high performance computing (HPC) center to work directly with industrial partners in automotive and aerospace engineering. HLRS is also a key partner of the Gauss Centre for Supercomputing (GCS), which is an alliance of the three major supercomputing centers in Germany that collectively provide one of the largest and most powerful supercomputer infrastructures in the world. "Cray is just the right partner as we enter the era of petaflops computing. Together with Cray's outstanding supercomputing technology, our center will be able to carry through the new initiative for engineering and industrial simulation. This is especially important as we work at the forefront of electric mobility and sustainable energy supply", said prof. -
CHABBI-DOCUMENT-2015.Pdf
ABSTRACT Software Support for Efficient Use of Modern Computer Architectures by Milind Chabbi Parallelism is ubiquitous in modern computer architectures. Heterogeneity of CPU cores and deep memory hierarchies make modern architectures difficult to program efficiently. Achieving top performance on supercomputers is difficult due to complex hardware, software, and their interactions. Production software systems fail to achieve top performance on modern architectures broadly due to three main causes: resource idleness, parallel overhead,anddata movement overhead.Thisdissertationpresentsnovelande↵ectiveperformance analysis tools, adap- tive runtime systems,andarchitecture-aware algorithms to understand and address these problems. Many future high performance systems will employ traditional multicore CPUs aug- mented with accelerators such as GPUs. One of the biggest concerns for accelerated systems is how to make best use of both CPU and GPU resources. Resource idleness arises in a parallel program due to insufficient parallelism and load imbalance, among other causes. To assess systemic resource idleness arising in GPU-accelerated architectures, we developed efficient profiling and tracing capabilities. We introduce CPU-GPU blame shifting—a novel technique to pinpoint and quantify the causes of resource idleness in GPU-accelerated archi- tectures. Parallel overheads arise due to synchronization constructs such as barriers and locks used in parallel programs. We developed a new technique to identify and eliminate redundant barriers at runtime in Partitioned Global Address Space programs. In addition, we developed a set of novel mutual exclusion algorithms that exploit locality in the memory hierarchy to improve performance on Non-Uniform Memory Access architectures. In modern architectures, inefficient or unnecessary memory accesses can severely degrade program performance. -
Technological Forecasting of Supercomputer Development: the March to Exascale Computing
Portland State University PDXScholar Engineering and Technology Management Faculty Publications and Presentations Engineering and Technology Management 10-2014 Technological Forecasting of Supercomputer Development: The March to Exascale Computing Dong-Joon Lim Portland State University Timothy R. Anderson Portland State University, [email protected] Tom Shott Portland State University, [email protected] Follow this and additional works at: https://pdxscholar.library.pdx.edu/etm_fac Part of the Engineering Commons Let us know how access to this document benefits ou.y Citation Details Lim, Dong-Joon; Anderson, Timothy R.; and Shott, Tom, "Technological Forecasting of Supercomputer Development: The March to Exascale Computing" (2014). Engineering and Technology Management Faculty Publications and Presentations. 46. https://pdxscholar.library.pdx.edu/etm_fac/46 This Post-Print is brought to you for free and open access. It has been accepted for inclusion in Engineering and Technology Management Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Technological forecasting of supercomputer development: The march to Exascale computing Dong-Joon Lim *, Timothy R. Anderson, Tom Shott Dept. of Engineering and Technology Management, Portland State University, USA Abstract- Advances in supercomputers have come at a steady pace over the past 20 years. The next milestone is to build an Exascale computer however this requires not only speed improvement but also significant enhancements for energy efficiency and massive parallelism. This paper examines technological progress of supercomputer development to identify the innovative potential of three leading technology paths toward Exascale development: hybrid system, multicore system and manycore system. -
OLCF AR 2016-17 FINAL 9-7-17.Pdf
Oak Ridge Leadership Computing Facility Annual Report 2016–2017 1 Outreach manager – Katie Bethea Writers – Eric Gedenk, Jonathan Hines, Katie Jones, and Rachel Harken Designer – Jason Smith Editor – Wendy Hames Photography – Jason Richards and Carlos Jones Stock images – iStockphoto™ Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory P.O. Box 2008, Oak Ridge, TN 37831-6161 Phone: 865-241-6536 Email: [email protected] Website: https://www.olcf.ornl.gov Facebook: https://www.facebook.com/oakridgeleadershipcomputingfacility Twitter: @OLCFGOV The research detailed in this publication made use of the Oak Ridge Leadership Computing Facility, a US Department of Energy Office of Science User Facility located at DOE’s Oak Ridge National Laboratory. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov. 2 Contents LETTER In a Record 25th Year, We Celebrate the Past and Look to the Future 4 SCIENCE Streamlining Accelerated Computing for Industry 6 A Seismic Mapping Milestone 8 The Shape of Melting in Two Dimensions 10 A Supercomputing First for Predicting Magnetism in Real Nanoparticles 12 Researchers Flip Script for Lithium-Ion Electrolytes to Simulate Better Batteries 14 A Real CAM-Do Attitude 16 FEATURES Big Data Emphasis and New Partnerships Highlight the Path to Summit 18 OLCF Celebrates 25 Years of HPC Leadership 24 PEOPLE & PROGRAMS Groups within the OLCF 28 OLCF User Group and Executive Board 30 INCITE, ALCC, DD 31 SYSTEMS & SUPPORT Resource Overview 32 User Experience 34 Education, Outreach, and Training 35 ‘TitanWeek’ Recognizes Contributions of Nation’s Premier Supercomputer 36 Selected Publications 38 Acronyms 41 3 In a Record 25th Year, We Celebrate the Past and Look to the Future installed at the turn of the new millennium—to the founding of the Center for Computational Sciences at the US Department of Energy’s Oak Ridge National Laboratory. -
Improving Programmability, Portability and Performance In
Programmability, Portability and Performance in Heterogeneous Accelerator-Based Systems R. Govindarajan High Performance Computing Lab. SERC & CSA, IISc [email protected] Dec. 2015 © RG@SERC,IISc Overview • Introduction • Challenges in Accelerator-Based Systems • Addressing Programming Challenges – Managing parallelism across Accelerator cores – Managing data transfer between CPU and GPU – Managing Synergistic Execution across CPU and GPU cores • Implementation and Evaluation • Conclusions © RG@SERC,IISc 2 Moore’s Law No. of Transistors doubles every 18 months Doubling of Performance every 18 months Source: Univ. of Wisconsin 3 Moore’s Law: Processor Architecture Roadmap ILP ProcessorsEPIC Super- scalar © RG@SERC,IISc 4 Multicores : The “Right Turn” 1 Core 1 6 GHz GHz 6 3 GHz 3 GHz 3 GHz 2 Core 4 Core 16 Core 1 Core 1 3 GHz 3 GHz Performance 1 Core 1 1 GHz GHz 1 Source: LinusTechTips 5 Moore’s Law: Processor Architecture Roadmap Accele- rators ILP ProcessorsEPIC Super- scalar © RG@SERC,IISc 6 Accelerators © RG@SERC,IISc 7 Accelerator Architecture: Fermi S2050 Source: icrontic.com © RG@SERC,IISc 8 Comparing CPU and GPU • 8 CPU cores @ 3 GHz • 2880 CUDA cores @ 1.67 GHz • 380 GFLOPS • 1500 GFLOPS C0 C1 C7 SLC IMC QPI PCI-e Memory Memory CPU © RG@SERC,IISc GPU 9 Accelerators: Hype or Reality? Rmax Power Rank Site Manufacturer Computer Country Cores [Pflops] [MW] National Tianhe-2, 1 SuperComputer Center NUDT China 3,120,000 33.86 17.80 Xeon E5 2691 and Xeon Phi 31S1 in Tianjin Titan Cray XK7, Oak Ridge National 2 Cray Opteron 6274