Leadership Computing Directions at Oak Ridge National Laboratory: Navigating the Transition to Heterogeneous Architectures
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An Operational Perspective on a Hybrid and Heterogeneous Cray XC50 System
An Operational Perspective on a Hybrid and Heterogeneous Cray XC50 System Sadaf Alam, Nicola Bianchi, Nicholas Cardo, Matteo Chesi, Miguel Gila, Stefano Gorini, Mark Klein, Colin McMurtrie, Marco Passerini, Carmelo Ponti, Fabio Verzelloni CSCS – Swiss National Supercomputing Centre Lugano, Switzerland Email: {sadaf.alam, nicola.bianchi, nicholas.cardo, matteo.chesi, miguel.gila, stefano.gorini, mark.klein, colin.mcmurtrie, marco.passerini, carmelo.ponti, fabio.verzelloni}@cscs.ch Abstract—The Swiss National Supercomputing Centre added depth provides the necessary space for full-sized PCI- (CSCS) upgraded its flagship system called Piz Daint in Q4 e daughter cards to be used in the compute nodes. The use 2016 in order to support a wider range of services. The of a standard PCI-e interface was done to provide additional upgraded system is a heterogeneous Cray XC50 and XC40 system with Nvidia GPU accelerated (Pascal) devices as well as choice and allow the systems to evolve over time[1]. multi-core nodes with diverse memory configurations. Despite Figure 1 clearly shows the visible increase in length of the state-of-the-art hardware and the design complexity, the 37cm between an XC40 compute module (front) and an system was built in a matter of weeks and was returned to XC50 compute module (rear). fully operational service for CSCS user communities in less than two months, while at the same time providing significant improvements in energy efficiency. This paper focuses on the innovative features of the Piz Daint system that not only resulted in an adaptive, scalable and stable platform but also offers a very high level of operational robustness for a complex ecosystem. -
New CSC Computing Resources
New CSC computing resources Atte Sillanpää, Nino Runeberg CSC – IT Center for Science Ltd. Outline CSC at a glance New Kajaani Data Centre Finland’s new supercomputers – Sisu (Cray XC30) – Taito (HP cluster) CSC resources available for researchers CSC presentation 2 CSC’s Services Funet Services Computing Services Universities Application Services Polytechnics Ministries Data Services for Science and Culture Public sector Information Research centers Management Services Companies FUNET FUNET and Data services – Connections to all higher education institutions in Finland and for 37 state research institutes and other organizations – Network Services and Light paths – Network Security – Funet CERT – eduroam – wireless network roaming – Haka-identity Management – Campus Support – The NORDUnet network Data services – Digital Preservation and Data for Research Data for Research (TTA), National Digital Library (KDK) International collaboration via EU projects (EUDAT, APARSEN, ODE, SIM4RDM) – Database and information services Paituli: GIS service Nic.funet.fi – freely distributable files with FTP since 1990 CSC Stream Database administration services – Memory organizations (Finnish university and polytechnics libraries, Finnish National Audiovisual Archive, Finnish National Archives, Finnish National Gallery) 4 Current HPC System Environment Name Louhi Vuori Type Cray XT4/5 HP Cluster DOB 2007 2010 Nodes 1864 304 CPU Cores 10864 3648 Performance ~110 TFlop/s 34 TF Total memory ~11 TB 5 TB Interconnect Cray QDR IB SeaStar Fat tree 3D Torus CSC -
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. -
Red Storm Infrastructure at Sandia National Laboratories
Red Storm Infrastructure Robert A. Ballance, John P. Noe, Milton J. Clauser, Martha J. Ernest, Barbara J. Jennings, David S. Logsted, David J. Martinez, John H. Naegle, and Leonard Stans Sandia National Laboratories∗ P.O. Box 5800 Albquuerque, NM 87185-0807 May 13, 2005 Abstract Large computing systems, like Sandia National Laboratories’ Red Storm, are not deployed in isolation. The computer, its infrastructure support, and the operations of both have to be designed to support the mission of the or- ganization. This talk and paper will describe the infrastructure and operational requirements of Red Storm along with a discussion of how Sandia is meeting those requirements. It will include discussions of the facilities that surround and support Red Storm: shelter, power, cooling, security, networking, interactions with other Sandia platforms and capabilities, operations support, and user services. 1 Introduction mary components: Stewart Brand, in How Buildings Learn[Bra95], has doc- 1. The physical setting, such as buildings, power, and umented many of the ways in which buildings — often cooling, thought to be static structures – evolve over time to meet the needs of their occupants and the constraints of their 2. The computational setting which includes the re- environments. Machines learn in the same way. This is lated computing systems that support ASC efforts not the learning of “Machine learning” in Artificial Intel- on Red Storm, ligence; it is the very human result of making a machine 3. The networking setting that ties all of these to- smarter as it operates within its distinct environment. gether, and Deploying a large computational resource teaches many lessons, including the need to meet the expecta- 4. -
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. -
A New UK Service for Academic Research
The newsletter of EPCC, the supercomputing centre at the University of Edinburgh news Issue 74 Autumn 2013 In this issue Managing research data HPC for business Simulating soft materials Energy efficiency in HPC Intel Xeon Phi ARCHER A new UK service for academic research ARCHER is a 1.56 Petaflop Cray XC30 supercomputer that will provide the next UK national HPC service for academic research Also in this issue Dinosaurs! From the Directors Contents 3 PGAS programming 7th International Conference Autumn marks the start of the new ARCHER service: a 1.56 Petaflop Profile Cray XC30 supercomputer that will provide the next UK national Meet the people at EPCC HPC service for academic research. 4 New national HPC service We have been involved in running generation of exascale Introducing ARCHER national HPC services for over 20 supercomputers which will be years and ARCHER will continue many, many times more powerful Big Data our tradition of supporting science than ARCHER. Big Data activities 7 Data preservation and in the UK and in Europe. are also playing an increasingly infrastructure important part in our academic and Our engagement with industry work. supercomputing takes many forms 9 HPC for industry - from racing dinosaurs at science We have always prided ourselves Making business better festivals, to helping researchers get on the diversity of our activities. more from HPC by improving This issue of EPCC News 11 Simulation algorithms and creating new showcases just a fraction of them. Better synthesised sounds software tools. EPCC staff -
Merrimac – High-Performance and Highly-Efficient Scientific Computing with Streams
MERRIMAC – HIGH-PERFORMANCE AND HIGHLY-EFFICIENT SCIENTIFIC COMPUTING WITH STREAMS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Mattan Erez May 2007 c Copyright by Mattan Erez 2007 All Rights Reserved ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (William J. Dally) Principal Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Patrick M. Hanrahan) I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Mendel Rosenblum) Approved for the University Committee on Graduate Studies. iii iv Abstract Advances in VLSI technology have made the raw ingredients for computation plentiful. Large numbers of fast functional units and large amounts of memory and bandwidth can be made efficient in terms of chip area, cost, and energy, however, high-performance com- puters realize only a small fraction of VLSI’s potential. This dissertation describes the Merrimac streaming supercomputer architecture and system. Merrimac has an integrated view of the applications, software system, compiler, and architecture. We will show how this approach leads to over an order of magnitude gains in performance per unit cost, unit power, and unit floor-space for scientific applications when compared to common scien- tific computers designed around clusters of commodity general-purpose processors. -
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 -
Developing Integrated Data Services for Cray Systems with a Gemini Interconnect
Developing Integrated Data Services for Cray Systems with a Gemini Interconnect Ron A. Oldeld Todd Kordenbrock Jay Lofstead May 1, 2012 Abstract Over the past several years, there has been increasing interest in injecting a layer of compute resources between a high-performance computing application and the end storage devices. For some projects, the objective is to present the parallel le system with a reduced set of clients, making it easier for le-system vendors to support extreme-scale systems. In other cases, the objective is to use these resources as staging areas to aggregate data or cache bursts of I/O operations. Still others use these staging areas for in-situ analysis on data in-transit between the application and the storage system. To simplify our discussion, we adopt the general term Integrated Data Services to represent these use-cases. This paper describes how we provide user-level, integrated data services for Cray systems that use the Gemini Interconnect. In particular, we describe our implementation and performance results on the Cray XE6, Cielo, at Los Alamos National Laboratory. 1 Introduction den on the le system and potentially improve over- all ecency of the application workow. Current In our quest toward exascale systems and applica- work to enable these coupling and workow scenar- tions, one topic that is frequently discussed is the ios are focused on the data issues to resolve resolu- need for more exible execution models. Current tion and mesh mismatches, time scale mismatches, models for capability class high-performance comput- and make data available through data staging tech- ing (HPC) systems are essentially static, requiring niques [20, 34, 21, 11, 2, 40, 10]. -
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 -
Through the Years… When Did It All Begin?
& Through the years… When did it all begin? 1974? 1978? 1963? 2 CDC 6600 – 1974 NERSC started service with the first Supercomputer… ● A well-used system - Serial Number 1 ● On its last legs… ● Designed and built in Chippewa Falls ● Launch Date: 1963 ● Load / Store Architecture ● First RISC Computer! ● First CRT Monitor ● Freon Cooled ● State-of-the-Art Remote Access at NERSC ● Via 4 acoustic modems, manually answered capable of 10 characters /sec 3 50th Anniversary of the IBM / Cray Rivalry… Last week, CDC had a press conference during which they officially announced their 6600 system. I understand that in the laboratory developing this system there are only 32 people, “including the janitor”… Contrasting this modest effort with our vast development activities, I fail to understand why we have lost our industry leadership position by letting someone else offer the world’s most powerful computer… T.J. Watson, August 28, 1963 4 2/6/14 Cray Higher-Ed Roundtable, July 22, 2013 CDC 7600 – 1975 ● Delivered in September ● 36 Mflop Peak ● ~10 Mflop Sustained ● 10X sustained performance vs. the CDC 6600 ● Fast memory + slower core memory ● Freon cooled (again) Major Innovations § 65KW Memory § 36.4 MHz clock § Pipelined functional units 5 Cray-1 – 1978 NERSC transitions users ● Serial 6 to vector architectures ● An fairly easy transition for application writers ● LTSS was converted to run on the Cray-1 and became known as CTSS (Cray Time Sharing System) ● Freon Cooled (again) ● 2nd Cray 1 added in 1981 Major Innovations § Vector Processing § Dependency -
Cray XC30™ Supercomputer Intel® Xeon® Processor Daughter Card
Intel, Xeon, Aries and the Intel Logo are trademarks of Intel Corporation in the U.S. and/or other countries. All other trademarks mentioned herein are the properties of their respective owners. 20121024JRC 2012 Cray Inc. All rights reserved. Specifications are subject to change without notice. Cray is a registered trademark, Cray XC30, Cray Linux Environment, Cray SHMEM, and NodeKare are trademarks of Cray In. Cray XC30™ Supercomputer Intel® Xeon® Processor Daughter Card Adaptive Supercomputing through Flexible Design Supercomputer users procure their machines to satisfy specific and demanding The Cray XC30 supercomputer series requirements. But they need their system to grow and evolve to maximize the architecture has been specifically machine’s lifetime and their return on investment. To solve these challenges designed from the ground up to be today and into the future, the Cray XC30 supercomputer series network and adaptive. A holistic approach opti- compute technology have been designed to easily accommodate upgrades mizes the entire system to deliver and enhancements. Users can augment their system “in-place” to upgrade to sustained real-world performance and higher performance processors, or add coprocessor/accelerator components extreme scalability across the collective to build even higher performance Cray XC30 supercomputer configurations. integration of all hardware, network- ing and software. Cray XC30 Supercomputer — Compute Blade The Cray XC30 series architecture implements two processor engines per One key differentiator with this compute node, and has four compute nodes per blade. Compute blades stack adaptive supercomputing platform is 16 to a chassis, and each cabinet can be populated with up to three chassis, the flexible method of implementing culminating in 384 sockets per cabinet.