DOCSLIB.ORG

Technical Manual for a Coupled Sea-Ice/Ocean Circulation Model (Version 3)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Technical Manual for a Coupled Sea-Ice/Ocean Circulation Model (Version 3) OCS Study MMS 2009-062 Technical Manual for a Coupled Sea-Ice/Ocean Circulation Model (Version 3) Katherine S. Hedström Arctic Region Supercomputing Center University of Alaska Fairbanks U.S. Department of the Interior Minerals Management Service Anchorage, Alaska Contract No. M07PC13368 OCS Study MMS 2009-062 Technical Manual for a Coupled Sea-Ice/Ocean Circulation Model (Version 3) Katherine S. Hedström Arctic Region Supercomputing Center University of Alaska Fairbanks Nov 2009 This study was funded by the Alaska Outer Continental Shelf Region of the Minerals Manage- ment Service, U.S. Department of the Interior, Anchorage, Alaska, through Contract M07PC13368 with Rutgers University, Institute of Marine and Coastal Sciences. The opinions, findings, conclusions, or recommendations expressed in this report or product are those of the authors and do not necessarily reflect the views of the U.S. Department of the Interior, nor does mention of trade names or commercial products constitute endorsement or recommenda- tion for use by the Federal Government. This document was prepared with LATEX xfig, and inkscape. Acknowledgments The ROMS model is descended from the SPEM and SCRUM models, but has been entirely rewritten by Sasha Shchepetkin, Hernan Arango and John Warner, with many, many other con- tributors. I am indebted to every one of them for their hard work. Bill Hibler first came up with the viscous-plastic rheology we are using. Paul Budgell has rewritten the dynamic sea-ice model, improving the solution procedure and making the water- stress term implicit in time, then changing it again to use the elastic-viscous-plastic rheology of Hunke and Dukowicz. I am very grateful that he is allowing us to use his version of the code. The sea-ice thermodynamics is derived from Sirpa Häkkinen’s implementation of the Mellor-Kantha scheme. She was kind enough to allow Paul and I to start with her code. Thanks to the internet community for providing great tools like Perl, patch, cpp, svn, and gmake to aid in software development (and to make it more fun). This work was supported in part by a grant of HPC resources from the Arctic Region Super- computing Center and the DoD High Performance Computing Modernization Program. Development and testing of the ROMS model has been funded by many, including the USGS Coastal and Marine Program, the Office of Naval Research, the National Ocean Partnership Pro- gram... UNIX is a registered trademark of the Open Group. Cygwin is a registered trademark of Red Hat, Inc. Abstract The Regional Ocean Modeling System (ROMS), authored by many, most notably Sasha Shchepetkin, is one approach to regional and basin-scale ocean modeling. This user’s manual for ROMS describes the model equations and algorithms, as well as additional user configurations necessary for specific applications. ROMS itself has now branched out as well - the version described here is that available through the myroms.org svn site with modifications to include sea ice and other minor changes. Contents 1 Introduction 1 2 Getting started 3 2.1 myroms.org . .3 2.2 Prerequisites . .3 2.3 Acquiring the ROMS code . .3 2.4 Compiling ROMS . .4 2.4.1 Environment Variables for make .........................4 2.4.2 Providing the Environment . .5 2.4.3 Build scripts . .6 2.5 Running ROMS . .6 2.6 Warnings and bugs . .7 3 Ocean Model Formulation 8 3.1 Equations of motion . .8 3.2 Vertical boundary conditions . .9 3.3 Horizontal boundary conditions . 10 3.4 Terrain-following coordinate system . 10 3.5 Horizontal curvilinear coordinates . 11 4 Numerical Solution Technique 13 4.1 Vertical and horizontal discretization . 13 4.1.1 Horizontal grid . 13 4.1.2 Vertical grid . 13 4.2 Masking of land areas . 14 4.2.1 Velocity . 14 4.2.2 Temperature, salinity and surface elevation . 14 4.2.3 Wetting and drying . 15 4.3 Time-stepping overview . 15 4.4 Conservation properties . 17 4.5 Depth-integrated equations . 18 4.6 Density in the mode coupling . 20 4.7 Time stepping: internal velocity modes and tracers . 22 4.8 Advection schemes . 22 4.8.1 Second-order Centered . 23 4.8.2 Fourth-order Centered . 23 4.8.3 Fourth-order Akima . 24 4.8.4 Third-order Upwind . 24 4.9 Determination of the vertical velocity and density fields . 25 4.10 Horizontal mixing . 25 4.10.1 Deviatory stress tensor . 25 4.10.2 Transverse stress tensor . 27 4.10.3 Rotated Transverse Stress Tensor . 27 4.10.4 Biharmonic . 28 4.11 Vertical mixing schemes . 28 4.11.1 Mellor-Yamada . 29 4.11.2 The Large, McWilliams and Doney parameterization . 30 4.12 Timestepping vertical viscosity and diffusion . 33 4.13 Boundary Conditions . 35 1 4.13.1 Gradient boundary condition . 35 4.13.2 Wall boundary condition . 35 4.13.3 Clamped boundary condition . 35 4.13.4 Flather boundary condition . 35 4.13.5 Chapman boundary condition . 35 4.13.6 Radiation boundary condition . 36 4.13.7 Mixed radiation-nudging boundary condition . 36 5 Ice Model Formulation 37 5.1 Dynamics . 37 5.2 Thermodynamics . 38 5.2.1 Ocean surface boundary conditions . 43 5.2.2 Frazil ice formation . 44 5.2.3 Differences from Mellor and Kantha . 45 6 Details of the Code 46 6.1 Directory structure . 46 6.2 Main subroutines . 48 6.2.1 master.F . 48 6.2.2 ocean_control.F . 48 6.2.3 ROMS_initialize . 48 6.2.4 ROMS_run . 49 6.2.5 ROMS_finalize . 49 6.2.6 main3d . 49 6.3 Initialization . 52 6.4 Modules . 52 6.5 Functionals . 54 6.6 Other subroutines and functions . 56 6.7 C preprocessor variables . 56 6.8 Important parameters . 65 6.9 Domain decomposition . 65 6.9.1 ROMS internal numbers . 66 6.9.2 MPI exchange . 68 6.9.3 Code syntax . 69 6.9.4 Input/output . 72 7 Configuring ROMS for a Specific Application 75 7.1 Configuring ROMS . 75 7.1.1 Case Name . 75 7.1.2 Case-specific Include File . 76 7.1.3 Functionals . 76 7.1.4 checkdefs.F .................................... 76 7.1.5 Model domain . 76 7.1.6 x, y grid....................................... 77 7.1.7 ξ, η grid....................................... 77 7.1.8 Initial conditions . 77 7.1.9 Equation of state . 78 7.1.10 Boundary conditions . 78 7.1.11 Model forcing . 78 7.1.12 ocean.in ...................................... 79 7.1.13 User variables and subroutines . 88 2 7.2 Upwelling/Downwelling Example . 88 7.2.1.
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.
    [Show full text]
  • Understanding the Cray X1 System
    NAS Technical Report NAS-04-014 December 2004 Understanding the Cray X1 System Samson Cheung, Ph.D. Halcyon Systems Inc. NASA Advanced Supercomputing Division NASA Ames Research Center [email protected] Abstract This paper helps the reader understand the characteristics of the Cray X1 vector supercomputer system, and provides hints and information to enable the reader to port codes to the system. It provides a comparison between the basic performance of the X1 platform and other platforms available at NASA Ames Research Center. A set of codes, solving the Laplacian equation with different parallel paradigms, is used to understand some features of the X1 compiler. An example code from the NAS Parallel Benchmarks is used to demonstrate performance optimization on the X1 platform.* Key Words: Cray X1, High Performance Computing, MPI, OpenMP Introduction The Cray vector supercomputer, which dominated in the ‘80s and early ‘90s, was replaced by clusters of shared-memory processors (SMPs) in the mid-‘90s. Universities across the U.S. use commodity components to build Beowulf systems to achieve high peak speed [1]. The supercomputer center at NASA Ames Research Center had over 2,000 CPUs of SGI Origin ccNUMA machines in 2003. This technology path provides excellent price/performance ratio; however, many application software programs (e.g., Earth Science, validated vectorized CFD codes) sustain only a small faction of the peak without a major rewrite of the code. The Japanese Earth Simulator demonstrated the low price/performance ratio by using vector processors connected to high-bandwidth memory and high-performance networking [2]. Several scientific applications sustain a large fraction of its 40 TFLOPS/sec peak performance.
    [Show full text]
  • Performance Evaluation of the Cray X1 Distributed Shared Memory Architecture
    Performance Evaluation of the Cray X1 Distributed Shared Memory Architecture Tom Dunigan Jeffrey Vetter Pat Worley Oak Ridge National Laboratory Highlights º Motivation – Current application requirements exceed contemporary computing capabilities – Cray X1 offered a ‘new’ system balance º Cray X1 Architecture Overview – Nodes architecture – Distributed shared memory interconnect – Programmer’s view º Performance Evaluation – Microbenchmarks pinpoint differences across architectures – Several applications show marked improvement ORNL/JV 2 1 ORNL is Focused on Diverse, Grand Challenge Scientific Applications SciDAC Genomes Astrophysics to Life Nanophase Materials SciDAC Climate Application characteristics vary dramatically! SciDAC Fusion SciDAC Chemistry ORNL/JV 3 Climate Case Study: CCSM Simulation Resource Projections Science drivers: regional detail / comprehensive model Machine and Data Requirements 1000 750 340.1 250 154 100 113.3 70.3 51.5 Tflops 31.9 23.4 Tbytes 14.5 10 10.6 6.6 4.8 3 2.2 1 1 dyn veg interactivestrat chem biogeochem eddy resolvcloud resolv trop chemistry Years CCSM Coupled Model Resolution Configurations: 2002/2003 2008/2009 Atmosphere 230kmL26 30kmL96 • Blue line represents total national resource dedicated Land 50km 5km to CCSM simulations and expected future growth to Ocean 100kmL40 10kmL80 meet demands of increased model complexity Sea Ice 100km 10km • Red line show s data volume generated for each Model years/day 8 8 National Resource 3 750 century simulated (dedicated TF) Storage (TB/century) 1 250 At 2002-3 scientific complexity, a century simulation required 12.5 days. ORNL/JV 4 2 Engaged in Technical Assessment of Diverse Architectures for our Applications º Cray X1 Cray X1 º IBM SP3, p655, p690 º Intel Itanium, Xeon º SGI Altix º IBM POWER5 º FPGAs IBM Federation º Planned assessments – Cray X1e – Cray X2 – Cray Red Storm – IBM BlueGene/L – Optical processors – Processors-in-memory – Multithreading – Array processors, etc.
    [Show full text]
  • Titan Introduction and Timeline Bronson Messer
    Titan Introduction and Timeline Presented by: Bronson Messer Scientific Computing Group Oak Ridge Leadership Computing Facility Disclaimer: There is no contract with the vendor. This presentation is the OLCF’s current vision that is awaiting final approval. Subject to change based on contractual and budget constraints. We have increased system performance by 1,000 times since 2004 Hardware scaled from single-core Scaling applications and system software is the biggest through dual-core to quad-core and challenge dual-socket , 12-core SMP nodes • NNSA and DoD have funded much • DOE SciDAC and NSF PetaApps programs are funding of the basic system architecture scalable application work, advancing many apps research • DOE-SC and NSF have funded much of the library and • Cray XT based on Sandia Red Storm applied math as well as tools • IBM BG designed with Livermore • Computational Liaisons key to using deployed systems • Cray X1 designed in collaboration with DoD Cray XT5 Systems 12-core, dual-socket SMP Cray XT4 Cray XT4 Quad-Core 2335 TF and 1030 TF Cray XT3 Cray XT3 Dual-Core 263 TF Single-core Dual-Core 119 TF Cray X1 54 TF 3 TF 26 TF 2005 2006 2007 2008 2009 2 Disclaimer: No contract with vendor is in place Why has clock rate scaling ended? Power = Capacitance * Frequency * Voltage2 + Leakage • Traditionally, as Frequency increased, Voltage decreased, keeping the total power in a reasonable range • But we have run into a wall on voltage • As the voltage gets smaller, the difference between a “one” and “zero” gets smaller. Lower voltages mean more errors.
    [Show full text]
  • Taking the Lead in HPC
    Taking the Lead in HPC Cray X1 Cray XD1 Cray Update SuperComputing 2004 Safe Harbor Statement The statements set forth in this presentation include forward-looking statements that involve risk and uncertainties. The Company wished to caution that a number of factors could cause actual results to differ materially from those in the forward-looking statements. These and other factors which could cause actual results to differ materially from those in the forward-looking statements are discussed in the Company’s filings with the Securities and Exchange Commission. SuperComputing 2004 Copyright Cray Inc. 2 Agenda • Introduction & Overview – Jim Rottsolk • Sales Strategy – Peter Ungaro • Purpose Built Systems - Ly Pham • Future Directions – Burton Smith • Closing Comments – Jim Rottsolk A Long and Proud History NASDAQ: CRAY Headquarters: Seattle, WA Marketplace: High Performance Computing Presence: Systems in over 30 countries Employees: Over 800 BuildingBuilding Systems Systems Engineered Engineered for for Performance Performance Seymour Cray Founded Cray Research 1972 The Father of Supercomputing Cray T3E System (1996) Cray X1 Cray-1 System (1976) Cray-X-MP System (1982) Cray Y-MP System (1988) World’s Most Successful MPP 8 of top 10 Fastest First Supercomputer First Multiprocessor Supercomputer First GFLOP Computer First TFLOP Computer Computer in the World SuperComputing 2004 Copyright Cray Inc. 4 2002 to 2003 – Successful Introduction of X1 Market Share growth exceeded all expectations • 2001 Market:2001 $800M • 2002 Market: about $1B
    [Show full text]
  • Tour De Hpcycles
    Tour de HPCycles Wu Feng Allan Snavely [email protected] [email protected] Los Alamos National San Diego Laboratory Supercomputing Center Abstract • In honor of Lance Armstrong’s seven consecutive Tour de France cycling victories, we present Tour de HPCycles. While the Tour de France may be known only for the yellow jersey, it also awards a number of other jerseys for cycling excellence. The goal of this panel is to delineate the “winners” of the corresponding jerseys in HPC. Specifically, each panelist will be asked to award each jersey to a specific supercomputer or vendor, and then, to justify their choices. Wu FENG [email protected] 2 The Jerseys • Green Jersey (a.k.a Sprinters Jersey): Fastest consistently in miles/hour. • Polka Dot Jersey (a.k.a Climbers Jersey): Ability to tackle difficult terrain while sustaining as much of peak performance as possible. • White Jersey (a.k.a Young Rider Jersey): Best "under 25 year-old" rider with the lowest total cycling time. • Red Number (Most Combative): Most aggressive and attacking rider. • Team Jersey: Best overall team. • Yellow Jersey (a.k.a Overall Jersey): Best overall supercomputer. Wu FENG [email protected] 3 Panelists • David Bailey, LBNL – Chief Technologist. IEEE Sidney Fernbach Award. • John (Jay) Boisseau, TACC @ UT-Austin – Director. 2003 HPCwire Top People to Watch List. • Bob Ciotti, NASA Ames – Lead for Terascale Systems Group. Columbia. • Candace Culhane, NSA – Program Manager for HPC Research. HECURA Chair. • Douglass Post, DoD HPCMO & CMU SEI – Chief Scientist. Fellow of APS. Wu FENG [email protected] 4 Ground Rules for Panelists • Each panelist gets SEVEN minutes to present his position (or solution).
    [Show full text]
  • Introduction to the Oak Ridge Leadership Computing Facility for CSGF Fellows Bronson Messer
    Introduction to the Oak Ridge Leadership Computing Facility for CSGF Fellows Bronson Messer Acting Group Leader Scientific Computing Group National Center for Computational Sciences Theoretical Astrophysics Group Oak Ridge National Laboratory Department of Physics & Astronomy University of Tennessee Outline • The OLCF: history, organization, and what we do • The upgrade to Titan – Interlagos processors with GPUs – Gemini Interconnect – Software, etc. • The CSGF Director’s Discretionary Program • Questions and Discussion 2 ORNL has a long history in 2007 High Performance Computing IBM Blue Gene/P ORNL has had 20 systems 1996-2002 on the lists IBM Power 2/3/4 1992-1995 Intel Paragons 1985 Cray X-MP 1969 IBM 360/9 1954 2003-2005 ORACLE Cray X1/X1E 3 Today, we have the world’s most powerful computing facility Peak performance 2.33 PF/s #2 Memory 300 TB Disk bandwidth > 240 GB/s Square feet 5,000 Power 7 MW Dept. of Energy’s Jaguar most powerful computer Peak performance 1.03 PF/s #8 Memory 132 TB Disk bandwidth > 50 GB/s Square feet 2,300 National Science Kraken Power 3 MW Foundation’s most powerful computer Peak Performance 1.1 PF/s Memory 248 TB #32 Disk Bandwidth 104 GB/s Square feet 1,600 National Oceanic and Power 2.2 MW Atmospheric Administration’s NOAA Gaea most powerful computer 4 We have increased system performance by 1,000 times since 2004 Hardware scaled from single-core Scaling applications and system software is the biggest through dual-core to quad-core and challenge dual-socket , 12-core SMP nodes • NNSA and DoD have funded
    [Show full text]
  • Cray.Com  Nasdaq: CRAY  Formed on April 1, 2000 As Cray Inc
    HPDC 2009 - München June 12, 2009 Wilfried Oed [email protected] Nasdaq: CRAY Formed on April 1, 2000 as Cray Inc. Headquartered in Seattle, WA Roughly 850 employees across 30 countries Four Major Development Sites Seattle, Washington Chippewa Falls, Wisconsin Minneapolis, Minnesota Austin, Texas June 12, 2009 Cray Proprietary Slide 2 The Cray Roadmap “Granite” “Baker”+ “Marble” Cray XT5HE Cray XT5m “Baker” Cray XT5 & XT5h Cray XT4 Cascade Program Adaptive Systems Processing Flexibility Productivity Focus CX1 Scalar Cray XMT Vector Multithreaded June 12, 2009 Cray Proprietary Slide 3 June 12, 2009 Peak Performance 1.38 Petaflops System Memory 300 Terabytes Disk Space 10.7 Petabytes Disk Bandwidth 240+ Gigabytes/second Processor Cores 150,000 June 12, 2009 Cray Proprietary Slide 5 Science Total Area Code Contact Cores Perf Notes Scaling Gordon Materials DCA++ Schulthess 150,144 1.3 PF* Weak Bell Winner Materials LSMS/WL ORNL 149,580 1.05 PF 64 bit Weak Gordon Seismology SPECFEM3D UCSD 149,784 165 TF Weak Bell Finalist Weather WRF Michalakes 150,000 50 TF Size of Data Strong 20 sim yrs/ Climate POP Jones 18,000 Size of Data Strong CPU day Combustion S3D Chen 144,000 83 TF Weak 20 billion Fusion GTC UC Irvine 102,000 Code Limit Weak Particles / sec Lin-Wang Gordon Materials LS3DF 147,456 442 TF Weak Wang Bell Winner June 12, 2009 Cray Proprietary Slide 6 June 12, 2009 Cray Proprietary Slide 7 6.4 GB/sec direct connect Characteristics HyperTransport Number of Cores 8 or 12 Peak Performance 86 Gflops/sec Shanghai (2.7) Peak Performance 124
    [Show full text]
  • Cray System Software Features for Cray X1 System ABSTRACT
    Cray System Software Features for Cray X1 System Don Mason Cray, Inc. 1340 Mendota Heights Road Mendota Heights, MN 55120 [email protected] ABSTRACT This paper presents an overview of the basic functionalities the Cray X1 system software. This includes operating system features, programming development tools, and support for programming model Introduction This paper is in four sections: the first section outlines the Cray software roadmap for the Cray X1 system and its follow-on products; the second section presents a building block diagram of the Cray X1 system software components, organized by programming models, programming environments and operating systems, then describes and details these categories; the third section lists functionality planned for upcoming system software releases; the final section lists currently available documentation, highlighting manuals of interest to current Cray T90, Cray SV1, or Cray T3E customers. Cray Software Roadmap Cray’s roadmap for platforms and operating systems is shown here: Figure 1: Cray Software Roadmap The Cray X1 system is the first scalable vector processor system that combines the characteristics of a high bandwidth vector machine like the Cray SV1 system with the scalability of a true MPP system like the Cray T3E system. The Cray X1e system follows the Cray X1 system, and the Cray X1e system is followed by the code-named Black Widow series systems. This new family of systems share a common instruction set architecture. Paralleling the evolution of the Cray hardware, the Cray Operating System software for the Cray X1 system builds upon technology developed in UNICOS and UNICOS/mk. The Cray X1 system operating system, UNICOS/mp, draws in particular on the architecture of UNICOS/mk by distributing functionality between nodes of the Cray X1 system in a manner analogous to the distribution across processing elements on a Cray T3E system.
    [Show full text]
  • Early Evaluation of the Cray XT5
    Early Evaluation of the Cray XT5 Patrick Worley, Richard Barrett, Jeffrey Kuehn Oak Ridge National Laboratory CUG 2009 May 6, 2009 Omni Hotel at CNN Center Atlanta, GA Acknowledgements • Research sponsored by the Climate Change Research Division of the Office of Biological and Environmental Research, by the Fusion Energy Sciences Program, and by the Office of Mathematical, Information, and Computational Sciences, all in the Office of Science, U.S. Department of Energy under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC. • This research used resources (Cray XT4 and Cray XT5) of the National Center for Computational Sciences at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC. • These slides have been authored by a contractor of the U.S. Government under contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. 2 Prior CUG System Evaluation Papers 1. CUG 2008: The Cray XT4 Quad-core : A First Look (Alam, Barrett, Eisenbach, Fahey, Hartman-Baker, Kuehn, Poole, Sankaran, and Worley) 2. CUG 2007: Comparison of Cray XT3 and XT4 Scalability (Worley) 3. CUG 2006: Evaluation of the Cray XT3 at ORNL: a Status Report (Alam, Barrett, Fahey, Messer, Mills, Roth, Vetter, and Worley) 4. CUG 2005: Early Evaluation of the Cray XD1 (Fahey, Alam, Dunigan, Vetter, and Worley) 5. CUG 2005: Early Evaluation of the Cray XT3 at ORNL (Vetter, Alam, Dunigan, Fahey, Roth, and Worley) 6.
    [Show full text]
  • Performance Comparison of Cray X1 and Cray Opteron Cluster with Other Leading Platforms Using HPCC and IMB Benchmarks*
    Performance Comparison of Cray X1 and Cray Opteron Cluster with Other Leading Platforms using HPCC and IMB Benchmarks* Subhash Saini1, Rolf Rabenseifner3, Brian T. N. Gunney2, Thomas E. Spelce2, Alice Koniges2, Don Dossa2, Panagiotis Adamidis3, Robert Ciotti1, 3 4 5 Sunil R. Tiyyagura , Matthias Müller , and Rod Fatoohi 1Advanced Supercomputing Division NASA Ames Research Center Moffett Field, California 94035, USA {ssaini, ciotti}@nas.nasa.gov 4ZIH, TU Dresden. Zellescher Weg 12, D-01069 Dresden, Germany 2Lawrence Livermore National Laboratory [email protected] Livermore, California 94550, USA {gunney, koniges, dossa1}@llnl.gov 5San Jose State University 3 One Washington Square High-Performance Computing-Center (HLRS) San Jose, California 95192 Allmandring 30, D-70550 Stuttgart, Germany [email protected] University of Stuttgart {adamidis, rabenseifner, sunil}@hlrs.de Abstract tem performance and global network issues [1,2]. In this paper, we use the HPCC suite as a first comparison of sys- tems. Additionally, the message-passing paradigm has The HPC Challenge (HPCC) benchmark suite and the become the de facto standard in programming high-end Intel MPI Benchmark (IMB) are used to compare and parallel computers. As a result, the performance of a ma- evaluate the combined performance of processor, memory jority of applications depends on the performance of the subsystem and interconnect fabric of six leading MPI functions as implemented on these systems. Simple supercomputers - SGI Altix BX2, Cray X1, Cray Opteron bandwidth and latency tests are two traditional metrics for Cluster, Dell Xeon cluster, NEC SX-8 and IBM Blue assessing the performance of the interconnect fabric of the Gene/L.
    [Show full text]
  • Performance Evaluation of the Cray X1 Distributed Shared Memory Architecture
    1 Performance Evaluation of the Cray X1 Distributed Shared Memory Architecture Thomas H. Dunigan, Jr., Jeffrey S. Vetter, James B. White III, Patrick H. Worley Oak Ridge National Laboratory Abstract—The Cray X1 supercomputer is a distributed shared like vector caches and multi-streaming processors. memory vector multiprocessor, scalable to 4096 processors and up to 65 terabytes of memory. The X1’s hierarchical design uses A. Multi-streaming Processor (MSP) the basic building block of the multi-streaming processor (MSP), The X1 has a hierarchical design with the basic building which is capable of 12.8 GF/s for 64-bit operations. The block being the multi-streaming processor (MSP), which is distributed shared memory (DSM) of the X1 presents a 64-bit capable of 12.8 GF/s for 64-bit operations (or 25.6 GF/s for global address space that is directly addressable from every MSP with an interconnect bandwidth per computation rate of one byte 32-bit operations). As illustrated in Figure 1, each MSP is per floating point operation. Our results show that this high comprised of four single-streaming processors (SSPs), each bandwidth and low latency for remote memory accesses with two 32-stage 64-bit floating-point vector units and one 2- translates into improved application performance on important way super-scalar unit. The SSP uses two clock frequencies, applications. Furthermore, this architecture naturally supports 800 MHz for the vector units and 400 MHz for the scalar unit. programming models like the Cray SHMEM API, Unified Each SSP is capable of 3.2 GF/s for 64-bit operations.
    [Show full text]