Dell High-Performance Computing Clusters and Reservoir Simulation Research at UT Austin.
|
|
|
- Myra Young
- 8 years ago
- Views:
Transcription
1 Dell High-Performance Computing Clusters and Reservoir Simulation Research at UT Austin Reza Rooholamini, Ph.D. Director Enterprise Solutions Dell Computer Corp.
2 Cost/Complexity Product Maturity Life Cycle in the Open Systems Market Heterogeneous SANs RISC systems Grids Project based SANs Proprietary Standardization 8P servers HPC Clusters Network Attached Storage 4P servers Simplicity/Volume/Choice Direct Attached Storage 1/2P servers Appliance Servers Workstation Desktops Fully Standardized 2 Enterprise Solutions
3 Our Vision Customers define our success: Begin with the customer. End with the customer Provide the best price/performance solutions to our customers in HPC Promote standardization to provide choice, lower cost of ownership, and simplicity in HPC solutions Evangelize new HPC technologies and selectively adopt the relevant ones for productization Derive the requirements for products by focusing on applications Provide a total solution: Hardware, software and services Partner with best of class in HPC 3 Enterprise Solutions
4 Building Block Approach Benchmark Parallel Benchmarks (NAS, HINT, Linpack ) and Parallel Applications Middleware MPI/Pro MPICH MVICH PVM OS OS Linux Windows Protocol TCP VIA GM Elan Interconnect Fast Ethernet Gigabit Ethernet Myrinet Quadrics Infiniband Platform Dell PowerEdge Servers (IA32 & IA64) 4 Enterprise Solutions
5 Dell and UT Austin Dell is sponsoring research in reservoir simulation at the Department of Petroleum and Geosystems Engineering Dr. Kamy Sepehrnoori is collaborating with Dell s HPCC team on performance studies, paper publications, and parallel simulator development Dell HPCC team includes graduates from Dr. Sepehrnoori s group specialized in Petroleum Engineering Dell has participated in Reservoir Simulation JIP (Joint Industry Project) in the past, and is planning to attend the upcoming meeting Dr. Sepehrnoori has access to Dell HPC lab for running large simulations, and is provided with hardware for development, testing, and performance studies of his program 5 Enterprise Solutions
6 A Performance Study of Parallel Reservoir Simulation on HPC Clusters Baris Guler Tau Leng Victor Mashayekhi Reza Rooholamini Dell Computer Corporation Kamy Sepehrnoori Center for Petroleum and Geosystems Engineering The University of Texas at Austin
7 Outline Background Software/Hardware Description Compositional Reservoir simulation on HPCs Results Summary Future Work
8 Reservoir Simulation Application Reservoir Forecasting Reservoir Performance optimization Sensitivity Analysis History Matching Risk Assessment through Stochastic Simulation Assessment of Uncertainity in Forecasting Value of Information Studies Reservoir Management
9 Reservoir Simulation Steps Data Input/Model Initialization Do Time Step Computation Solution of Non-Linear Partial Differential Equation Discretization Linearization and Newtonian Iteration Solution using Direct or Iterative Solvers Test for Convergence of Solution Data Output/Graphics Time-Step Increment End of Simulation Study Results Processing/Interpretation
10 Reservoir Simulation Hardware HPCs MPPs PCs/Workstations RISC Workstations Supercomputers Mainframes
11 Benefits of Parallel Processing Turn-around time Large-scale simulations Cost
12 Parallel Processing Massively Parallel Computers High Performance Computing Clusters
13 Benefits of Clusters Scalability High Performance Computing Low Cost Availability
14 Computational Mode Distributed processing Parallel processing
15 Distributed Processing Input Generator D 1 D 2 D 3 D n User Batch Queuing System to Simulation Program n >> m P 1 P 2 P 3 P m Database Post Processing
16 Input Data Cluster Simulation System FS 1 FS 2 FS FS m Cluster Scheduler Cluster Scheduler DS 1 DS 2 DS DS n Project Advisor User Input Output Data Generator Data Generator Archiver Post-Processor Processor
17
18 CPU-6 CPU-6 CPU-3 CPU-3 CPGE Parallel Processing CPU-1 CPU-1 CPU-2 CPU-2 CPU-5 CPU-5 FD RESERVOIR CPU-4 CPU-4 CPU-1 CPU-1 FD & DD
19 Domain Decomposition Ghost Layers Creation Communication Fundamental strategy for grid-based parallel simulation. Example: 10 x 15 grid 6 processors
20 Performance Issues in Parallel Processing Software Design Algorithm Parallelization Programming practice Load Balancing
21 Performance Issues in Parallel Processing Hardware Configuration CPU Cache Memory subsystem Front Side Bus I/O bandwidth Interconnect
22 Hardware - Interconnect Type Fast Ethernet Gigabit Ethernet Giganet Myrinet Infiniband 4x Quadrics Dolphin Speed(MBps) Latency(ms)
23 CPGE-1(Ararat) 12 Nodes / 16 Processors 1.0 GHz Intel Pentium III Xeon processors 256 MB of memory Diskless configuration 100 Mbps switched Fast Ethernet and Giganet interconnects
24 TACC-1(Tejas) 32 Nodes / 64 Processors 1.0 GHz Intel Pentium III processors 1 GB of memory/processor 225 MBps Myrinet-2000 interconnect
25 Parallel Reservoir Simulators Chevron-Texaco Conoco-Phillips Exxon-Mobil IFP and Beicip-Franlab Landmark Graphics Corporation Schlumberger-Geoquest Saudi Aramco UT CPGE, UT CSM Note : 93 clusters in Top500 supercomputer sites, 23 in Oil and Gas sector.
26 Compositional Reservoir Simulation on HPCs
27 Project Objectives Develop a general purpose adaptive simulator (GPAS) capable of: modeling of complex physical processes including EOS compositional, chemical, black-oil and thermal high resolution studies on supercomputers and highperformance cluster
28 HPC Initiatives Evaluate and compare performance of different cluster systems Test and analyze performance of different parallel simulators Identify areas of improvement in parallel algorithm design and cluster setup for optimal parallel reservoir simulation
29 Summary of Clusters Cluster CPU Type CPU Speed (MHz) CPUs Memory per CPU Interconnect CPGE-1 (Fuji) Pentium II x1=16 384MB Fast Ethernet CPGE-1 (Rocky) Pentium II Xeon 400 8x2=16 256MB Fast Ethernet CPGE-1 (Ararat) Pentium III Xeon x1+4x2=16 256MB Fast Ethernet DELL-1 (PE 1550) Pentium III x2=32 512MB Myrinet, Gigabit, Fast Ethernet DELL-2 (PE 2650) Intel Xeon DP x2=128 1GB Myrinet, Gigabit, Fast Ethernet TACC-1 (Tejas) Pentium III x2=64 512MB Myrinet TACC-2 (Longhorn) Power x16=64 2GB IBM SP Switch2
30 Parallel Simulators Tested GPAS VIP (2003r4)
31 CPGE Simulator (GPAS) EOS Compositional Peng-Robinson EOS Fully Implicit PETSc Linear Solvers Parallel (IPARS Framework)
32 Performance Results
33 Base Benchmark Problem Compositional model 3-component Peng-Robinson EOS Dry-gas cycling process Reservoir size: 800 x x 160 ft, homogeneous 2 wells, 1 Injector, 1 producer Grids: 16 x 224 x 8 (28,672 cells) Unknowns : 229, days of gas injection One dimensional domain decomposition
34 Single-Processors Execution Times(GPAS) Base Benchmark Problem Fuji with Pentium II 300MHz Rocky with Pentium II Xeon 400MHz PowerEdge 1550 with Pentium III 1.0GHz Ararat with Pentium III Xeon 1.0GHz TACC-Tejas with Pentium III 1.0Ghz Dell-PE2650 with Intel Xeon DP 2.4GHz Execution Time [sec]
35 Multi-Processors Execution Times(GPAS) Base Benchmark Problem Execution Time (seconds) Fuji Rocky Ararat PE 1550 PE 2650 Tejas Longhorn Number of Processors
36 Multi-Processors Speedups(GPAS) Base Benchmark Problem Sppedup Fuji(FE) Rocky(FE) Ararat(FE) PE 1550(FE) PE 2650(FE) Tejas(My) Longhorn(*) Ideal Number of Processors
37 Comparison of MPI-Interconnects Interconnects (GPAS) Base Benchmark Problem DELL PE 2650 (Single processor/node) MPICH-GIGABIT MPICH-GM - MYRINET MPI/PRO-GIGABIT MPICH-FE Ideal Speedup Number of Processors
38 Constant Problem Size per Processor(GPAS) Fuji Rocky Ararat Tejas 800 Execution Time [sec] ,1CPU 38400, 2CPUs 76800, 4CPUs , 8CPUs , 16CPUs Grid Dimensions, Number of CPUs , 32CPUs
39 Modified Benchmark Problem Compositional model 3-component Peng-Robinson EOS Dry-gas cycling process Reservoir size: 7.3 x 24.2 x.1 miles Grids: 77 x 256 x 10 (197,120 cells) Unknowns : 1.57 million Anisotropic, Layered Permeability with Kv/Kh = wells, 54 Injectors, 24 producers, staggered line drive Injectors and Producers are completed fully 100 days of gas injection One dimensional domain decomposition
40 Multi-Processors Execution Times(GPAS) Modified Benchmark Problem DELL PE 2650 GBit-SINGLE My-SINGLE FE-SINGLE My-DUAL Execution Time (Seconds) Number of Processors
41 Multi-Processors Speedups(GPAS) Modified Benchmark Problem 72 DELL PE 2650 GIGABIT-SINGLE MYRINET-SINGLE FAST ETH-SINGLE MYRINET-DUAL Ideal Speedup Number of Processors
42 Commercial Parallel Simulator
43 REMARKS Our goal was to run the simulators in parallel mode and evaluate their performance for typical cases Our goal was to analyze the different issues involved in using the simulators in parallel and approaches to improved performance and design We did not Tune simulators for optimum performance Compare or match material balance errors of the simulator runs
44 Benchmark Problem for VIP Compositional model Modified SPE3 comparison project 9-component Peng Robinson EOS Gas condensate with gas cycling process Reservoir size: 10 miles x 4 miles x 160ft Grids: 180 x 72 x 4 (51,840 cells) 1 million unknowns Flow barriers present (using Transmissibility modifiers) 20 wells, 10 Injectors, 10 producers 10 years of cycling followed by 5 years of production
45 Multi-Processors Performance VIP
46 Multi-Processors Execution Times(VIP) MODIFIED SPE3 COMPARISON PROBLEM Elapsed Time (sec) Fuji Rocky Number of Processors
47 Multi-Processors Speedups(VIP) MODIFIED SPE3 COMPARISON PROBLEM Fuji Rocky Ideal Speedup Processors
48 Constant Problem Size per Processor(VIP) MODIFIED SPE3 COMPARISON PROBLEM Fuji Rocky Execution Time [sec] , 1CPU 51480, 2CPUs , 4CPUs , 8CPUs ,16CPUs Number of Cells, Number of CPUs
49 Million Cell Commercial Benchmark Problem for VIP IMPES scheme 7-component Peng Robinson EOS Grid: 100 x 100 x 100 (1 Million cells) 16 million unknowns Stochastically characterized data field 11 wells 49 Year run
50 Performance Speedups - VIP MILLION GRIDBLOCK PROBLEM DELL PE VIP(*) Ideal Speedup Number of Processors
51 Summary Tested GPAS and analyzed performance on new hardware Benchmarked performance of new clusters Compared performance of different interconnects and MPI libraries Tested commercial reservoir simulator VIP in parallel mode
52 Acknowledgements US Department of Energy Reservoir Simulation Joint Industry Project Members Dell Computer Corporation
Introduction to High Performance Cluster Computing. Cluster Training for UCL Part 1
Introduction to High Performance Cluster Computing Cluster Training for UCL Part 1 What is HPC HPC = High Performance Computing Includes Supercomputing HPCC = High Performance Cluster Computing Note: these
P013 INTRODUCING A NEW GENERATION OF RESERVOIR SIMULATION SOFTWARE
1 P013 INTRODUCING A NEW GENERATION OF RESERVOIR SIMULATION SOFTWARE JEAN-MARC GRATIEN, JEAN-FRANÇOIS MAGRAS, PHILIPPE QUANDALLE, OLIVIER RICOIS 1&4, av. Bois-Préau. 92852 Rueil Malmaison Cedex. France
Clusters: Mainstream Technology for CAE
Clusters: Mainstream Technology for CAE Alanna Dwyer HPC Division, HP Linux and Clusters Sparked a Revolution in High Performance Computing! Supercomputing performance now affordable and accessible Linux
Agenda. HPC Software Stack. HPC Post-Processing Visualization. Case Study National Scientific Center. European HPC Benchmark Center Montpellier PSSC
HPC Architecture End to End Alexandre Chauvin Agenda HPC Software Stack Visualization National Scientific Center 2 Agenda HPC Software Stack Alexandre Chauvin Typical HPC Software Stack Externes LAN Typical
Cluster Scalability of ANSYS FLUENT 12 for a Large Aerodynamics Case on the Darwin Supercomputer
Cluster Scalability of ANSYS FLUENT 12 for a Large Aerodynamics Case on the Darwin Supercomputer Stan Posey, MSc and Bill Loewe, PhD Panasas Inc., Fremont, CA, USA Paul Calleja, PhD University of Cambridge,
SPE 51885. Abstract. Copyright 1999, Society of Petroleum Engineers, Inc.
SPE 51885 A Fully Implicit Parallel EOS Compositional Simulator for Large Scale Reservoir Simulation. P. Wang, S. Balay 1, K.Sepehrnoori, J. Wheeler, J. Abate, B. Smith 1, G.A. Pope. The University of
Improved LS-DYNA Performance on Sun Servers
8 th International LS-DYNA Users Conference Computing / Code Tech (2) Improved LS-DYNA Performance on Sun Servers Youn-Seo Roh, Ph.D. And Henry H. Fong Sun Microsystems, Inc. Abstract Current Sun platforms
A Framework For Application Performance Understanding and Prediction
A Framework For Application Performance Understanding and Prediction Laura Carrington Ph.D. Lab (Performance Modeling & Characterization) at the 1 About us An NSF lab see www.sdsc.edu/ The mission of the
benchmarking Amazon EC2 for high-performance scientific computing
Edward Walker benchmarking Amazon EC2 for high-performance scientific computing Edward Walker is a Research Scientist with the Texas Advanced Computing Center at the University of Texas at Austin. He received
A Flexible Cluster Infrastructure for Systems Research and Software Development
Award Number: CNS-551555 Title: CRI: Acquisition of an InfiniBand Cluster with SMP Nodes Institution: Florida State University PIs: Xin Yuan, Robert van Engelen, Kartik Gopalan A Flexible Cluster Infrastructure
High Performance Computing in CST STUDIO SUITE
High Performance Computing in CST STUDIO SUITE Felix Wolfheimer GPU Computing Performance Speedup 18 16 14 12 10 8 6 4 2 0 Promo offer for EUC participants: 25% discount for K40 cards Speedup of Solver
Large Scale Parallel Reservoir Simulations on a Linux PC-Cluster 1
Large Scale Parallel Reservoir Simulations on a Linux PC-Cluster 1 Walid A. Habiballah and M. Ehtesham Hayder Petroleum Engineering Application Services Department Saudi Aramco, Dhahran 31311, Saudi Arabia
Large-Scale Reservoir Simulation and Big Data Visualization
Large-Scale Reservoir Simulation and Big Data Visualization Dr. Zhangxing John Chen NSERC/Alberta Innovates Energy Environment Solutions/Foundation CMG Chair Alberta Innovates Technology Future (icore)
A Theory of the Spatial Computational Domain
A Theory of the Spatial Computational Domain Shaowen Wang 1 and Marc P. Armstrong 2 1 Academic Technologies Research Services and Department of Geography, The University of Iowa Iowa City, IA 52242 Tel:
Cluster Computing at HRI
Cluster Computing at HRI J.S.Bagla Harish-Chandra Research Institute, Chhatnag Road, Jhunsi, Allahabad 211019. E-mail: jasjeet@mri.ernet.in 1 Introduction and some local history High performance computing
LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance
11 th International LS-DYNA Users Conference Session # LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance Gilad Shainer 1, Tong Liu 2, Jeff Layton 3, Onur Celebioglu
Multicore Parallel Computing with OpenMP
Multicore Parallel Computing with OpenMP Tan Chee Chiang (SVU/Academic Computing, Computer Centre) 1. OpenMP Programming The death of OpenMP was anticipated when cluster systems rapidly replaced large
ECLIPSE Performance Benchmarks and Profiling. January 2009
ECLIPSE Performance Benchmarks and Profiling January 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox, Schlumberger HPC Advisory Council Cluster
Comparing the performance of the Landmark Nexus reservoir simulator on HP servers
WHITE PAPER Comparing the performance of the Landmark Nexus reservoir simulator on HP servers Landmark Software & Services SOFTWARE AND ASSET SOLUTIONS Comparing the performance of the Landmark Nexus
IBM Deep Computing Visualization Offering
P - 271 IBM Deep Computing Visualization Offering Parijat Sharma, Infrastructure Solution Architect, IBM India Pvt Ltd. email: parijatsharma@in.ibm.com Summary Deep Computing Visualization in Oil & Gas
Cellular Computing on a Linux Cluster
Cellular Computing on a Linux Cluster Alexei Agueev, Bernd Däne, Wolfgang Fengler TU Ilmenau, Department of Computer Architecture Topics 1. Cellular Computing 2. The Experiment 3. Experimental Results
- An Essential Building Block for Stable and Reliable Compute Clusters
Ferdinand Geier ParTec Cluster Competence Center GmbH, V. 1.4, March 2005 Cluster Middleware - An Essential Building Block for Stable and Reliable Compute Clusters Contents: Compute Clusters a Real Alternative
PERFORMANCE CONSIDERATIONS FOR NETWORK SWITCH FABRICS ON LINUX CLUSTERS
PERFORMANCE CONSIDERATIONS FOR NETWORK SWITCH FABRICS ON LINUX CLUSTERS Philip J. Sokolowski Department of Electrical and Computer Engineering Wayne State University 55 Anthony Wayne Dr. Detroit, MI 822
Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Intel Xeon Processor E7 v2 Family-Based Platforms
Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Family-Based Platforms Executive Summary Complex simulations of structural and systems performance, such as car crash simulations,
High Performance. CAEA elearning Series. Jonathan G. Dudley, Ph.D. 06/09/2015. 2015 CAE Associates
High Performance Computing (HPC) CAEA elearning Series Jonathan G. Dudley, Ph.D. 06/09/2015 2015 CAE Associates Agenda Introduction HPC Background Why HPC SMP vs. DMP Licensing HPC Terminology Types of
PARALLEL & CLUSTER COMPUTING CS 6260 PROFESSOR: ELISE DE DONCKER BY: LINA HUSSEIN
1 PARALLEL & CLUSTER COMPUTING CS 6260 PROFESSOR: ELISE DE DONCKER BY: LINA HUSSEIN Introduction What is cluster computing? Classification of Cluster Computing Technologies: Beowulf cluster Construction
Boosting Data Transfer with TCP Offload Engine Technology
Boosting Data Transfer with TCP Offload Engine Technology on Ninth-Generation Dell PowerEdge Servers TCP/IP Offload Engine () technology makes its debut in the ninth generation of Dell PowerEdge servers,
Recommended hardware system configurations for ANSYS users
Recommended hardware system configurations for ANSYS users The purpose of this document is to recommend system configurations that will deliver high performance for ANSYS users across the entire range
Introduction. Need for ever-increasing storage scalability. Arista and Panasas provide a unique Cloud Storage solution
Arista 10 Gigabit Ethernet Switch Lab-Tested with Panasas ActiveStor Parallel Storage System Delivers Best Results for High-Performance and Low Latency for Scale-Out Cloud Storage Applications Introduction
MOSIX: High performance Linux farm
MOSIX: High performance Linux farm Paolo Mastroserio [mastroserio@na.infn.it] Francesco Maria Taurino [taurino@na.infn.it] Gennaro Tortone [tortone@na.infn.it] Napoli Index overview on Linux farm farm
On-Demand Supercomputing Multiplies the Possibilities
Microsoft Windows Compute Cluster Server 2003 Partner Solution Brief Image courtesy of Wolfram Research, Inc. On-Demand Supercomputing Multiplies the Possibilities Microsoft Windows Compute Cluster Server
PRIMERGY server-based High Performance Computing solutions
PRIMERGY server-based High Performance Computing solutions PreSales - May 2010 - HPC Revenue OS & Processor Type Increasing standardization with shift in HPC to x86 with 70% in 2008.. HPC revenue by operating
FLOW-3D Performance Benchmark and Profiling. September 2012
FLOW-3D Performance Benchmark and Profiling September 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: FLOW-3D, Dell, Intel, Mellanox Compute
Exploiting Remote Memory Operations to Design Efficient Reconfiguration for Shared Data-Centers over InfiniBand
Exploiting Remote Memory Operations to Design Efficient Reconfiguration for Shared Data-Centers over InfiniBand P. Balaji, K. Vaidyanathan, S. Narravula, K. Savitha, H. W. Jin D. K. Panda Network Based
Overlapping Data Transfer With Application Execution on Clusters
Overlapping Data Transfer With Application Execution on Clusters Karen L. Reid and Michael Stumm reid@cs.toronto.edu stumm@eecg.toronto.edu Department of Computer Science Department of Electrical and Computer
Icepak High-Performance Computing at Rockwell Automation: Benefits and Benchmarks
Icepak High-Performance Computing at Rockwell Automation: Benefits and Benchmarks Garron K. Morris Senior Project Thermal Engineer gkmorris@ra.rockwell.com Standard Drives Division Bruce W. Weiss Principal
Microsoft Exchange Server 2003 Deployment Considerations
Microsoft Exchange Server 3 Deployment Considerations for Small and Medium Businesses A Dell PowerEdge server can provide an effective platform for Microsoft Exchange Server 3. A team of Dell engineers
Fast Setup and Integration of ABAQUS on HPC Linux Cluster and the Study of Its Scalability
Fast Setup and Integration of ABAQUS on HPC Linux Cluster and the Study of Its Scalability Betty Huang, Jeff Williams, Richard Xu Baker Hughes Incorporated Abstract: High-performance computing (HPC), the
Finite Elements Infinite Possibilities. Virtual Simulation and High-Performance Computing
Microsoft Windows Compute Cluster Server 2003 Partner Solution Brief Finite Elements Infinite Possibilities. Virtual Simulation and High-Performance Computing Microsoft Windows Compute Cluster Server Runs
A GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS
A GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS SUDHAKARAN.G APCF, AERO, VSSC, ISRO 914712564742 g_suhakaran@vssc.gov.in THOMAS.C.BABU APCF, AERO, VSSC, ISRO 914712565833
Enabling Technologies for Distributed Computing
Enabling Technologies for Distributed Computing Dr. Sanjay P. Ahuja, Ph.D. Fidelity National Financial Distinguished Professor of CIS School of Computing, UNF Multi-core CPUs and Multithreading Technologies
The Lattice Project: A Multi-Model Grid Computing System. Center for Bioinformatics and Computational Biology University of Maryland
The Lattice Project: A Multi-Model Grid Computing System Center for Bioinformatics and Computational Biology University of Maryland Parallel Computing PARALLEL COMPUTING a form of computation in which
Cluster Implementation and Management; Scheduling
Cluster Implementation and Management; Scheduling CPS343 Parallel and High Performance Computing Spring 2013 CPS343 (Parallel and HPC) Cluster Implementation and Management; Scheduling Spring 2013 1 /
Scaling Study of LS-DYNA MPP on High Performance Servers
Scaling Study of LS-DYNA MPP on High Performance Servers Youn-Seo Roh Sun Microsystems, Inc. 901 San Antonio Rd, MS MPK24-201 Palo Alto, CA 94303 USA youn-seo.roh@sun.com 17-25 ABSTRACT With LS-DYNA MPP,
Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage
White Paper Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage A Benchmark Report August 211 Background Objectivity/DB uses a powerful distributed processing architecture to manage
Principles and characteristics of distributed systems and environments
Principles and characteristics of distributed systems and environments Definition of a distributed system Distributed system is a collection of independent computers that appears to its users as a single
LS DYNA Performance Benchmarks and Profiling. January 2009
LS DYNA Performance Benchmarks and Profiling January 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center The
GPU System Architecture. Alan Gray EPCC The University of Edinburgh
GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems
Accelerating CFD using OpenFOAM with GPUs
Accelerating CFD using OpenFOAM with GPUs Authors: Saeed Iqbal and Kevin Tubbs The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide
ECLIPSE Best Practices Performance, Productivity, Efficiency. March 2009
ECLIPSE Best Practices Performance, Productivity, Efficiency March 29 ECLIPSE Performance, Productivity, Efficiency The following research was performed under the HPC Advisory Council activities HPC Advisory
OpenMP Programming on ScaleMP
OpenMP Programming on ScaleMP Dirk Schmidl schmidl@rz.rwth-aachen.de Rechen- und Kommunikationszentrum (RZ) MPI vs. OpenMP MPI distributed address space explicit message passing typically code redesign
A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures
11 th International LS-DYNA Users Conference Computing Technology A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures Yih-Yih Lin Hewlett-Packard Company Abstract In this paper, the
Using PCI Express Technology in High-Performance Computing Clusters
Using Technology in High-Performance Computing Clusters Peripheral Component Interconnect (PCI) Express is a scalable, standards-based, high-bandwidth I/O interconnect technology. Dell HPC clusters use
The PHI solution. Fujitsu Industry Ready Intel XEON-PHI based solution. SC2013 - Denver
1 The PHI solution Fujitsu Industry Ready Intel XEON-PHI based solution SC2013 - Denver Industrial Application Challenges Most of existing scientific and technical applications Are written for legacy execution
Parallel Computing with MATLAB
Parallel Computing with MATLAB Scott Benway Senior Account Manager Jiro Doke, Ph.D. Senior Application Engineer 2013 The MathWorks, Inc. 1 Acceleration Strategies Applied in MATLAB Approach Options Best
Enabling Technologies for Distributed and Cloud Computing
Enabling Technologies for Distributed and Cloud Computing Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Multi-core CPUs and Multithreading
Recent Advances in HPC for Structural Mechanics Simulations
Recent Advances in HPC for Structural Mechanics Simulations 1 Trends in Engineering Driving Demand for HPC Increase product performance and integrity in less time Consider more design variants Find the
Accelerating From Cluster to Cloud: Overview of RDMA on Windows HPC. Wenhao Wu Program Manager Windows HPC team
Accelerating From Cluster to Cloud: Overview of RDMA on Windows HPC Wenhao Wu Program Manager Windows HPC team Agenda Microsoft s Commitments to HPC RDMA for HPC Server RDMA for Storage in Windows 8 Microsoft
Control 2004, University of Bath, UK, September 2004
Control, University of Bath, UK, September ID- IMPACT OF DEPENDENCY AND LOAD BALANCING IN MULTITHREADING REAL-TIME CONTROL ALGORITHMS M A Hossain and M O Tokhi Department of Computing, The University of
Sockets vs. RDMA Interface over 10-Gigabit Networks: An In-depth Analysis of the Memory Traffic Bottleneck
Sockets vs. RDMA Interface over 1-Gigabit Networks: An In-depth Analysis of the Memory Traffic Bottleneck Pavan Balaji Hemal V. Shah D. K. Panda Network Based Computing Lab Computer Science and Engineering
Phire Architect Hardware and Software Requirements
Phire Architect Hardware and Software Requirements Copyright 2014, Phire. All rights reserved. The Programs (which include both the software and documentation) contain proprietary information; they are
The GRID according to Microsoft
JM4Grid 2008 The GRID according to Microsoft Andrea Passadore passa@dist.unige.it l.i.d.o.- DIST University of Genoa Agenda Windows Compute Cluster Server 2003 Overview Applications Windows HPC Server
Understanding the Benefits of IBM SPSS Statistics Server
IBM SPSS Statistics Server Understanding the Benefits of IBM SPSS Statistics Server Contents: 1 Introduction 2 Performance 101: Understanding the drivers of better performance 3 Why performance is faster
White Paper. Recording Server Virtualization
White Paper Recording Server Virtualization Prepared by: Mike Sherwood, Senior Solutions Engineer Milestone Systems 23 March 2011 Table of Contents Introduction... 3 Target audience and white paper purpose...
Workshop on Parallel and Distributed Scientific and Engineering Computing, Shanghai, 25 May 2012
Scientific Application Performance on HPC, Private and Public Cloud Resources: A Case Study Using Climate, Cardiac Model Codes and the NPB Benchmark Suite Peter Strazdins (Research School of Computer Science),
Performance Characteristics of a Cost-Effective Medium-Sized Beowulf Cluster Supercomputer
Res. Lett. Inf. Math. Sci., 2003, Vol.5, pp 1-10 Available online at http://iims.massey.ac.nz/research/letters/ 1 Performance Characteristics of a Cost-Effective Medium-Sized Beowulf Cluster Supercomputer
Interconnect Efficiency of Tyan PSC T-630 with Microsoft Compute Cluster Server 2003
Interconnect Efficiency of Tyan PSC T-630 with Microsoft Compute Cluster Server 2003 Josef Pelikán Charles University in Prague, KSVI Department, Josef.Pelikan@mff.cuni.cz Abstract 1 Interconnect quality
High Performance Computing. Course Notes 2007-2008. HPC Fundamentals
High Performance Computing Course Notes 2007-2008 2008 HPC Fundamentals Introduction What is High Performance Computing (HPC)? Difficult to define - it s a moving target. Later 1980s, a supercomputer performs
REM-Rocks: A Runtime Environment Migration Scheme for Rocks based Linux HPC Clusters
REM-Rocks: A Runtime Environment Migration Scheme for Rocks based Linux HPC Clusters Tong Liu, Saeed Iqbal, Yung-Chin Fang, Onur Celebioglu, Victor Masheyakhi and Reza Rooholamini Dell Inc. {Tong_Liu,
Integrated Grid Solutions. and Greenplum
EMC Perspective Integrated Grid Solutions from SAS, EMC Isilon and Greenplum Introduction Intensifying competitive pressure and vast growth in the capabilities of analytic computing platforms are driving
Technical Computing Suite Job Management Software
Technical Computing Suite Job Management Software Toshiaki Mikamo Fujitsu Limited Supercomputer PRIMEHPC FX10 PRIMERGY x86 cluster Outline System Configuration and Software Stack Features The major functions
SERVER CLUSTERING TECHNOLOGY & CONCEPT
SERVER CLUSTERING TECHNOLOGY & CONCEPT M00383937, Computer Network, Middlesex University, E mail: vaibhav.mathur2007@gmail.com Abstract Server Cluster is one of the clustering technologies; it is use for
2. COMPUTER SYSTEM. 2.1 Introduction
2. COMPUTER SYSTEM 2.1 Introduction The computer system at the Japan Meteorological Agency (JMA) has been repeatedly upgraded since IBM 704 was firstly installed in 1959. The current system has been completed
The Green Index: A Metric for Evaluating System-Wide Energy Efficiency in HPC Systems
202 IEEE 202 26th IEEE International 26th International Parallel Parallel and Distributed and Distributed Processing Processing Symposium Symposium Workshops Workshops & PhD Forum The Green Index: A Metric
CMS Tier-3 cluster at NISER. Dr. Tania Moulik
CMS Tier-3 cluster at NISER Dr. Tania Moulik What and why? Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach common goal. Grids tend
Oracle Database Scalability in VMware ESX VMware ESX 3.5
Performance Study Oracle Database Scalability in VMware ESX VMware ESX 3.5 Database applications running on individual physical servers represent a large consolidation opportunity. However enterprises
Sun Constellation System: The Open Petascale Computing Architecture
CAS2K7 13 September, 2007 Sun Constellation System: The Open Petascale Computing Architecture John Fragalla Senior HPC Technical Specialist Global Systems Practice Sun Microsystems, Inc. 25 Years of Technical
Parallel Large-Scale Visualization
Parallel Large-Scale Visualization Aaron Birkland Cornell Center for Advanced Computing Data Analysis on Ranger January 2012 Parallel Visualization Why? Performance Processing may be too slow on one CPU
Cluster Grid Interconects. Tony Kay Chief Architect Enterprise Grid and Networking
Cluster Grid Interconects Tony Kay Chief Architect Enterprise Grid and Networking Agenda Cluster Grid Interconnects The Upstart - Infiniband The Empire Strikes Back - Myricom Return of the King 10G Gigabit
Linux clustering. Morris Law, IT Coordinator, Science Faculty, Hong Kong Baptist University
Linux clustering Morris Law, IT Coordinator, Science Faculty, Hong Kong Baptist University PII 4-node clusters started in 1999 PIII 16 node cluster purchased in 2001. Plan for grid For test base HKBU -
THE EXPAND PARALLEL FILE SYSTEM A FILE SYSTEM FOR CLUSTER AND GRID COMPUTING. José Daniel García Sánchez ARCOS Group University Carlos III of Madrid
THE EXPAND PARALLEL FILE SYSTEM A FILE SYSTEM FOR CLUSTER AND GRID COMPUTING José Daniel García Sánchez ARCOS Group University Carlos III of Madrid Contents 2 The ARCOS Group. Expand motivation. Expand
1000-Channel IP System Architecture for DSS
Solution Blueprint Intel Core i5 Processor Intel Core i7 Processor Intel Xeon Processor Intel Digital Security Surveillance 1000-Channel IP System Architecture for DSS NUUO*, Qsan*, and Intel deliver a
Impact of Latency on Applications Performance
Impact of Latency on Applications Performance Rossen Dimitrov and Anthony Skjellum {rossen, tony}@mpi-softtech.com MPI Software Technology, Inc. 11 S. Lafayette Str,. Suite 33 Starkville, MS 39759 Tel.:
Leveraging Windows HPC Server for Cluster Computing with Abaqus FEA
Leveraging Windows HPC Server for Cluster Computing with Abaqus FEA This white paper outlines the benefits of using Windows HPC Server as part of a cluster computing solution for performing realistic simulation.
Performance Across the Generations: Processor and Interconnect Technologies
WHITE Paper Performance Across the Generations: Processor and Interconnect Technologies HPC Performance Results ANSYS CFD 12 Executive Summary Today s engineering, research, and development applications
Experiences of numerical simulations on a PC cluster Antti Vanne December 11, 2002
xperiences of numerical simulations on a P cluster xperiences of numerical simulations on a P cluster ecember xperiences of numerical simulations on a P cluster Introduction eowulf concept Using commodity
Distributed RAID Architectures for Cluster I/O Computing. Kai Hwang
Distributed RAID Architectures for Cluster I/O Computing Kai Hwang Internet and Cluster Computing Lab. University of Southern California 1 Presentation Outline : Scalable Cluster I/O The RAID-x Architecture
Building Clusters for Gromacs and other HPC applications
Building Clusters for Gromacs and other HPC applications Erik Lindahl lindahl@cbr.su.se CBR Outline: Clusters Clusters vs. small networks of machines Why do YOU need a cluster? Computer hardware Network
Making A Beowulf Cluster Using Sun computers, Solaris operating system and other commodity components
Making A Beowulf Cluster Using Sun computers, Solaris operating system and other commodity components 1. INTRODUCTION: Peter Wurst and Christophe Dupré Scientific Computation Research Center Rensselaer
Toward a practical HPC Cloud : Performance tuning of a virtualized HPC cluster
Toward a practical HPC Cloud : Performance tuning of a virtualized HPC cluster Ryousei Takano Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology
Performance of the Cloud-Based Commodity Cluster. School of Computer Science and Engineering, International University, Hochiminh City 70000, Vietnam
Computer Technology and Application 4 (2013) 532-537 D DAVID PUBLISHING Performance of the Cloud-Based Commodity Cluster Van-Hau Pham, Duc-Cuong Nguyen and Tien-Dung Nguyen School of Computer Science and
Performance Characteristics of VMFS and RDM VMware ESX Server 3.0.1
Performance Study Performance Characteristics of and RDM VMware ESX Server 3.0.1 VMware ESX Server offers three choices for managing disk access in a virtual machine VMware Virtual Machine File System
Very special thanks to Wolfgang Gentzsch and Burak Yenier for making the UberCloud HPC Experiment possible.
Digital manufacturing technology and convenient access to High Performance Computing (HPC) in industry R&D are essential to increase the quality of our products and the competitiveness of our companies.
Silviu Panica, Marian Neagul, Daniela Zaharie and Dana Petcu (Romania)
Silviu Panica, Marian Neagul, Daniela Zaharie and Dana Petcu (Romania) Outline Introduction EO challenges; EO and classical/cloud computing; EO Services The computing platform Cluster -> Grid -> Cloud
RLX Technologies Server Blades
Jane Wright Product Report 10 July 2003 RLX Technologies Server Blades Summary RLX Technologies has designed its product line to support parallel applications with high-performance compute clusters of
supercomputing. simplified.
supercomputing. simplified. INTRODUCING WINDOWS HPC SERVER 2008 R2 SUITE Windows HPC Server 2008 R2, Microsoft s third-generation HPC solution, provides a comprehensive and costeffective solution for harnessing
Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging
Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging In some markets and scenarios where competitive advantage is all about speed, speed is measured in micro- and even nano-seconds.
Performance Evaluation of the XDEM framework on the OpenStack Cloud Computing Middleware
Performance Evaluation of the XDEM framework on the OpenStack Cloud Computing Middleware 1 / 17 Performance Evaluation of the XDEM framework on the OpenStack Cloud Computing Middleware X. Besseron 1 V.
Hari Reddy High Performance Computing Solutions Development Systems and Technology Group IBM 6609 Carriage Drive Colleyville, TX 76034
PERFORMANCE EVALUATION OF STATIC AND DYNAMIC LOAD-BALANCING SCHEMES FOR A PARALLEL COMPUTATIONAL FLUID DYNAMICS SOFTWARE (CFD) APPLICATION (FLUENT) DISTRIBUTED ACROSS CLUSTERS OF HETEROGENEOUS SYMMETRIC
Current Trend of Supercomputer Architecture
Current Trend of Supercomputer Architecture Haibei Zhang Department of Computer Science and Engineering haibei.zhang@huskymail.uconn.edu Abstract As computer technology evolves at an amazingly fast pace,