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Title: High Performance Computing: Concepts, Methods,


1
High Performance Computing Concepts, Methods,
MeansAn Introduction
  • Prof. Thomas Sterling
  • Department of Computer Science
  • Louisiana State University
  • January 16, 2007

2
The Hammer of the Mind
  • The Hammer
  • Mankinds 1st tool
  • In the most general case applies a directed
    force to a concentrated point in our physical
    world to affect a desired change of state
  • Many implements of the physical world
  • Conventional means of inserting nails to wood
  • Includes knives, spears, arrows, screwdrivers,
    sledge-hammers, axes, clubs, etc.
  • Understanding
  • The force that drives our abstract world
  • Historically, 2 means by which the mind applies
    understanding
  • Empiricism acquiring knowledge through
    experience
  • Theory project beyond immediate experience to
    new knowledge
  • Supercomputing
  • The 3rd hammer of the mind for applying
    understanding
  • Explain the past
  • Predict the future
  • Control the present

3
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing as a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

4
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing as a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

5
Applying the Force of Understanding through the
Power of Supercomputing
6
Addressing the Big Questions
  • How to integrate technology into computing
    engines?
  • How to push the performance to extremes?
  • What are the enabling conditions?
  • What are the inhibiting factors?
  • How to manage supercomputer resources to deliver
    useful computing capabilities?
  • What are the hardware mechanisms?
  • What are the software policies?
  • How do users program such systems?
  • What languages and in what environments?
  • What are the semantics and strategies?
  • What grand challenge applications demand these
    capabilities?
  • What are the computational models and algorithms
    that can map the innate application properties to
    the physical medium of the machine?

7
Challenges in the Physical World Command Our
Abilities in the Abstract
  • Physical Sciences
  • Technology
  • Biology and Medical Science
  • Energy
  • Meteorology and Climate
  • Materials and Nanotechnology
  • National Security

8
A Growth-Factor of a Billion in Performance in a
Single Lifetime
1959 IBM 7094
1976 Cray 1
1996 T3E
1991 Intel Delta
2003 Cray X1
1949 Edsac
1823 Babbage Difference Engine
2001 Earth Simulator
1951 Univac 1
1982 Cray XMP
1988 Cray YMP
1964 CDC 6600
1997 ASCI Red
1943 Harvard Mark 1
9
Performance a cross-cutting issuethe Top-500
list of supercomputers
10
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing as a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

11
Definitions supercomputer
Supercomputer A computing system exhibiting
high-end performance capabilities and resource
capacities within practical constraints of
technology, cost, power, and reliability. Thomas
Sterling, 2007
Supercomputer a large very fast mainframe used
especially for scientific computations.
Merriam-Webster Online
Supercomputer any of a class of extremely
powerful computers. The term is commonly applied
to the fastest high-performance systems available
at any given time. Such computers are used
primarily for scientific and engineering work
requiring exceedingly high-speed computations.
Encyclopedia Britannica Online
12
Performance
  • Performance
  • A quantifiable measure of rate of doing
    (computational) work
  • Multiple such measures of performance
  • Delineated at the level of the basic operation
  • ops operations per second
  • ips instructions per second
  • flops floating operations per second
  • Rate at which a benchmark program takes to
    execute
  • A carefully crafted and controlled code used to
    compare systems
  • Linpack Rmax (Linpack flops)
  • gups (billion updates per second)
  • others
  • Two perspectives on performance
  • Peak performance
  • Maximum theoretical performance possible for a
    system
  • Sustained performance
  • Observed performance for a particular workload
    and run
  • Varies across workloads and possibly between runs

13
Key Parameters
  • Peak floating point performance
  • Main memory capacity
  • Bi-section bandwidth
  • I/O bandwidth
  • Secondary storage capacity
  • Organization
  • Class of system
  • nodes
  • processors per node
  • Accelerators
  • Network topology
  • Control strategy
  • MIMD
  • Vector, PVP
  • SIMD
  • SPMD

14
Scalability
  • The ability to deliver proportionally greater
    sustained performance through increased system
    resources
  • Strict Scaling
  • Fixed size application problem
  • Application size remains constant with increase
    in system size
  • Weak Scaling
  • Variable size application problem
  • Application size scales proportionally with
    system size
  • Capability computing
  • in most pure form strict scaling
  • Marketing claims tend toward this class
  • Capacity computing
  • Throughput computing
  • Includes job-stream workloads
  • In most simple form weak scaling
  • Cooperative computing
  • Interacting and coordinating concurrent processes
  • Not a widely used term
  • Also coordinated computing

15
Practical Constraints and Limitations
  • Cost
  • Deployment
  • Operational support
  • Power
  • Energy required to run the computer
  • Energy for support facilities
  • Energy for cooling (remove heat from machine)
  • Size
  • Floor space
  • Access way for power and signal cabling
  • Reliability
  • One factor of availability
  • Generality
  • How good is it across a range of problems
  • Usability
  • How hard is it to program and manage

16
Productivity a computing metric of merit
  • A rich measure of merit for computing
  • Captures key factors that determine overall
    impact
  • Exposes relationship between program development
    and program execution
  • Supercedes mere scalar parameters of assumed
    performance
  • Focuses attention on all (most) important
    contributors to overall effectiveness
  • Permits cogent comparative assessment of
    alternative system classes
  • Devised as part of DARPA HPCS Program Phase 1
  • T. Sterling
  • M. Snir
  • B. Smith
  • and others

17
Productivity Factors Directed Graph
Peak Performance (SP, CM)
Performance
Efficiency (E)
Programmability
Productivity (Y)
Application Construction (CS)
Portability
Maintainability
Availability (A)
Reliability
Accessibility
18
General Model of Productivity
19
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

20
Related Fields
  • Hardware
  • Device technologies
  • Logic circuit designs
  • Architecture
  • Software
  • System software
  • Programming methodologies
  • End user application problems
  • Problem area disciplines
  • Computational algorithms
  • Cross-cutting issues
  • Performance
  • Products and Market drivers
  • People
  • Packaging cost, space, power, reliability

21
Supercomputing A Discipline of Disciplines in
this course
  • Device technologies
  • Enabling technologies for logic, memory,
    communication
  • Circuit design
  • Computer architecture
  • semantics and structures
  • Programming
  • languages, tools, environments
  • Models of computation
  • governing principles
  • Compilers and runtime software
  • Maps application program to system resources,
    mechanisms, and semantics
  • Operating systems
  • Manages resources and provides virtual machine
  • Performance
  • modeling, measurement, benchmarking, and
    debugging
  • Algorithms
  • Numerical techniques
  • Means of exposing parallelism
  • Applications

22
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing as a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

23
Where Does Performance Come From?
  • Device Technology
  • Logic switching speed and device density
  • Memory capacity and access time
  • Communications bandwidth and latency
  • Computer Architecture
  • Instruction issue rate
  • Execution pipelining
  • Reservation stations
  • Branch prediction
  • Cache management
  • Parallelism
  • Parallelism number of operations per cycle per
    processor
  • Instruction level parallelism (ILP)
  • Vector processing
  • Parallelism number of processors per node
  • Parallelism number of nodes in a system

24
Moores Law
25
Microprocessor Clock Speed
26
Classes of Architecture forHigh Performance
Computers
  • Parallel Vector Processors (PVP)
  • NEC Earth Simulator, SX-6
  • Cray- 1, 2, XMP, YMP, C90, T90, X1
  • Fujitsu 5000 series
  • Massively Parallel Processors (MPP)
  • Intel Touchstone Delta Paragon
  • TMC CM-5
  • IBM SP-2 3, Blue Gene/Light
  • Cray T3D, T3E, Red Storm/Strider
  • Distributed Shared Memory (DSM)
  • SGI Origin
  • HP Superdome
  • Single Instruction stream Single Data stream
    (SIMD)
  • Goodyear MPP, MasPar 1 2, TMC CM-2
  • Commodity Clusters
  • Beowulf-class PC/Linux clusters
  • Constellations
  • HP Compaq SC, Linux NetworX MCR

27
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29
Why Fast Machines Run Slow
  • Latency
  • Waiting for access to memory or other parts of
    the system
  • Overhead
  • Extra work that has to be done to manage program
    concurrency and parallel resources the real work
    you want to perform
  • Starvation
  • Not enough work to do due to insufficient
    parallelism or poor load balancing among
    distributed resources
  • Contention
  • Delays due to fighting over what task gets to use
    a shared resource next. Network bandwidth is a
    major constraint.

30
The SIA ITRS Roadmap
31
Latency in a Single System
Ratio
Memory Access Time
CPU Time
THE WALL
32
Microprocessors no longer realize the full
potential of VLSI technology
52/year
19/year
301
74/year
1,0001
30,0001
33
Driving Issues/Trends
  • Multicore
  • Now 2
  • possibly 100s
  • will be million-way parallelism
  • Heterogeneity
  • GPU
  • Clearspeed
  • Cell SPE
  • Component I/O Pins
  • Off chip bandwidth not increasing with demand
  • Limited number of pins
  • Limited bandwidth per pin (pair)
  • Cache size per core may decline
  • Shared cache fragmentation
  • System Interconnect
  • Node bandwidth not increasing proportionally to
    core demand
  • Power
  • Mwatts at the high end millions of s per year

34
Topics
  • Supercomputing the big picture
  • What is a supercomputer?
  • Supercomputing as a multidisciplinary field
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

35
A Myth of Precedence
  • It is assumed by most that
  • Computers preceded supercomputers
  • Supercomputers emerged as a special purpose case
  • Definition
  • Supercomputer is a machine that greatly
    accelerates the rate of calculation with respect
    to alternative conventional means of the time
  • Contrary to popular belief
  • 1st computers were supercomputers
  • Supercomputers are general purpose
  • Mainstream computers were special purpose data
    processing systems

36
5. A Brief History of Supercomputing
  • Mechanical Computing (5.1)
  • Babbage, Hollerith, Aiken
  • Electronic Digital Calculating (5.2)
  • Atanasoff, Eckert, Mauchly
  • von Neumann Architecture (5.3)
  • Turing, von Neumann, Eckert, Mauchly, Foster,
    Wilkes
  • Semiconductor Technologies (5.4)
  • Birth of the Supercomputer (5.5)
  • Cray, Watanabe
  • The Golden Age (5.6)
  • Batcher, Dennis, S. Chen, Hillis, Dally, Blank,
    B. Smith
  • Common Era of Killer Micros (5.7)
  • Scott, Culler, Sterling/Becker, Goodhue, A. Chen,
    Tomkins
  • Petaflops (5.8)
  • Messina, Sterling, Stevens, P. Smith,

37
Synergy Drives Supercomputing Evolution
  • Technology
  • Enables digital technology
  • Defines balance of capabilities
  • Establishes relationship of relative costs
  • Architecture
  • Creates interface between computation and
    technology
  • Determines structures of technology-based
    components
  • Establishes low-level semantics of operation
  • Provides low-cost mechanisms
  • Model of Computation
  • Paradigm by which computation is manifest
  • Provides governing principles of architecture
    operation
  • Implies programming model and languages

38
Historical Trends are a Consequence of this
Interplay
  • Technology evolves as new fabrication methods,
    processes, and materials emerge through
    industrial research
  • New components replace old but with different
    operational properties and support requirements
  • Innovations in system structure are developed to
    exploit strengths of new components and
    compensate for their relative weaknesses as well
    as meet their requirements
  • When old architecture classes fail to fully
    exploit advancing technology, new model of
    computation is adopted to provides a better
    conceptual framework

39
Major Technology Generations(dates approximate)
  • Electromechanical
  • 19th century through 1st half of 20th century
  • Digital electronic with vacuum tubes
  • 1940s
  • Core memory
  • 1950
  • Transistors
  • 1947
  • SSI MSI RTL/DTL/TTL semiconductor
  • 1970
  • DRAM
  • 1970s
  • CMOS VLSI
  • 1990

40
Supercomputer Points of Transition
  • Automated calculating
  • 17th century
  • Stored program digital electronic
  • 1948
  • Vector
  • 1975
  • SIMD
  • 1980s
  • MPPs
  • 1991
  • Commodity Clusters
  • 1993/4

41
Historical Machines
  • Leibniz Stepped Reckoner
  • Babbage Difference Engine
  • Hollerith Tabulator
  • Harvard Mark 1
  • Un. of Pennsylvania Eniac
  • Cambridge Edsac
  • MIT Whirlwind
  • Cray 1
  • TMC CM-2
  • Intel Touchstone Delta
  • Beowulf
  • IBM Blue Gene/L

42
ENIAC(Electronic Numerical Integrator and
Computer )
  • Eckert and Mauchly, 1946.
  • Vacuum tubes.
  • Numerical solutions to problems in fields such as
    atomic energy and ballistic trajectories.

43
EDSAC(Electronic Delay Storage Automatic
Calculator)
  • Maurice Wilkes, 1949.
  • Mercury delay lines for memory and vacuum tubes
    for logic.
  • Used one of the first assemblers called Initial
    Orders.
  • Calculation of prime numbers, solutions of
    algebraic equations, etc.

44
MIT Whirlwind
  • Jay Forrester, 1949.
  • Fastest computer.
  • First computer to use magnetic core memory.
  • Displayed real time text and graphics on a large
    oscilloscope screen.

45
CRAY-1
  • Cray Research, 1976.
  • Pipelined vector arithmetic units.
  • Unique C-shape to help increase the signal speeds
    from one end to the other.

46
CM-2
  • Thinking Machines Corporation, 1987.
  • Hypercube architecture with 65,536 processors.
  • SIMD.
  • Performance in the range of GFLOPS.

47
INTEL Touchstone Delta
  • INTEL, 1990.
  • MIMD hypercube.
  • LINPACK rating of 13.9 GFLOPS .
  • Enough computing power for applications like
    real-time processing of satellite images and
    molecular models for AIDS research.

48
Beowulf
  • Thomas Sterling and Donald Becker, 1994.
  • Cluster formed of one head node and one/more
    compute nodes.
  • Nodes and network dedicated to the Beowulf.
  • Compute nodes are mass produced commodities.
  • Use open source software including Linux.

49
Beowulf Project
  • Wiglaf - 1994
  • 16 Intel 80486 100 MHz
  • VESA Local bus
  • 256 Mbytes memory
  • 6.4 Gbytes of disk
  • Dual 10 base-T Ethernet
  • 72 Mflops sustained
  • 40K
  • Hrothgar - 1995
  • 16 Intel Pentium100 MHz
  • PCI
  • 1 Gbyte memory
  • 6.4 Gbytes of disk
  • 100 base-T Fast Ethernet (hub)
  • 240 Mflops sustained
  • 46K
  • Hyglac-1996 (Caltech)
  • 16 Pentium Pro 200 MHz
  • PCI
  • 2 Gbytes memory
  • 49.6 Gbytes of disk
  • 100 base-T Fast Ethernet (switch)
  • 1.25 Gflops sustained
  • 50K

50
Earth Simulator
  • Japan, 1997.
  • Fastest supercomputer from 2002-2004 35.86
    TFLOPS.
  • 640 nodes with eight vector processors and 16
    gigabytes of computer memory at each node.

51
BlueGene/L
  • IBM, 2004.
  • Current fastest supercomputer - 207.3 TFLOPS .
  • First supercomputer ever to run over 100 TFLOPS
    sustained on a real world application, namely a
    three-dimensional molecular dynamics code
    (ddcMD).

52
Events in Supercomputing
  • Fortran compiler
  • Greatly simplified creation of complex
    application programs
  • Parallel processing
  • Enables more than one action to occur at a time
  • Pipeline structures
  • Increases clock rate and efficient use of
    resources
  • MPI
  • Universally adopted parallel programming model
  • Ethernet
  • Low cost interconnection network
  • Linpack
  • Most widely recognized benchmark for comparative
    study
  • Visualization
  • Facilitated interpretation of large data sets
  • Weak scaling
  • Dramatic increase in scalability of systems and
    achievable performance
  • Beowulf-class commodity clusters
  • Exploitation of economy of scale for significant
    improvement of performance to cost
  • Also, NOW network of workstations

53
Topics
  • Supercomputing the big picture
  • Supercomputing as a multidisciplinary field
  • What is a supercomputer?
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

54
A New HPC Course
  • An Introduction to all aspects of Supercomputing
  • In collaboration
  • Louisiana State University
  • University of Arkansas
  • Louisiana Technical University
  • Masaryk University, Czech republic
  • MCNC, North Carolina
  • Greatly expand student accessibility
  • Easier to learn
  • Available to students out of the mainstream
  • Multimedia
  • Hands-on interactive
  • Easily accessible for review/study
  • High Definition video over Internet
  • Specialized expertise available to the general
    community
  • Precision presentation for enhance learning
    experience

55
Goals of the Course
  • A first overview of the entire field of HPC
  • Basic concepts that govern the capability and
    effectiveness of supercomputers
  • Techniques and methods for applying HPC systems
  • Tools and environments that facilitate effective
    application of supercomputers
  • Hands-on experience with widely used systems and
    software
  • Performance measurement methods, benchmarks, and
    metrics
  • Practical real-world knowledge about the HPC
    community
  • Access by students outside the HPC mainstream

56
A Precursor to Future Pursuits
  • Understand concepts and challenges
  • for possible future research
  • advanced graduate studies
  • Basic methods of using and programming a
    supercomputer
  • for future computational scientists
  • Managing HPC systems
  • for future systems administrators
  • HPC system structures and engineering
  • for future system designers and developers

57
Technology Strategy
  • Interdisciplinary
  • Device technology and parallel computer
    architecture
  • Parallel programming models, languages, and tools
  • System software for resource management
  • Applications and algorithms
  • Web site managed
  • Lecture notes and source material
  • Problem sets
  • Video
  • On-demand streaming of class lectures
  • Additional side-bar material for expanded
    understanding
  • Subtitles for hearing impaired and non-native
    speakers
  • Hands on examples
  • Performance sensitivity and measurement
    cross-cutting interrelate disciplines

58
Course OverviewDivided into 7 Segments
  • S1 Introduction Clusters
  • S2 Architecture and Nodes
  • S3 MPI
  • S4 Enabling Technologies
  • S5 System Software
  • S6 Advanced Techniques
  • S7 Conclusions

59
Course Overview in 7 Segments
  • Introduction
  • An Overview
  • Commodity Clusters
  • Benchmarking
  • Throughput Computing
  • Architecture and Nodes
  • Parallel Computer Architecture
  • Single Node Architecture
  • Parallel thread computing
  • OpenMP programming
  • Performance factors and measurement (1)
  • MPI
  • Communicating sequential processes (CSP)
  • MPI programming
  • Performance measurement (2)
  • Parallel Algorithms
  • Enabling Technologies
  • Device Technologies
  • System Area Networks
  • System Software
  • Operating Systems
  • Schedulers and Middleware
  • Parallel file I/O
  • Advanced Techniques
  • Libraries
  • Visualization
  • Domain specific environments and Frameworks
  • Applications
  • Conclusions
  • Whats beyond the scope of this course
  • What form will the future of HPC take

60
Topics
  • Supercomputing the big picture
  • Supercomputing as a multidisciplinary field
  • What is a supercomputer?
  • Challenges and Opportunities
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

61
Segment 1 ClustersSkill Set
  • Login and establish control of cluster resources
  • Determine state of system resources and
    manipulate
  • Acquire, run, and measure benchmark performance
  • Launch and run user application codes
  • Collect ensemble result data using OS tools
  • Startup and apply Condor for performing
    concurrent jobs

62
Segment 1 ClustersKnowledge Factors
  • Overview of multidisciplinary field of HPC
  • Commodity cluster components and
    hardware/software architecture
  • Performance factors
  • Benchmarking and metrics
  • Throughput computing and Condor programming
  • History driven by interplay among technology,
    architecture, and programming models
  • Top 500 List

63
Topics
  • Supercomputing the big picture
  • Supercomputing as a multidisciplinary field
  • What is a supercomputer?
  • Challenges to supercomputing
  • A brief history of supercomputing
  • Overview of Course
  • Segment 1 knowledge factors skills
  • Resources and rules of engagement

64
Course Website
  • HPC Course Website can be accessed at
  • http//www.cct.lsu.edu/csc7600
  • Course Info
  • Syllabus
  • Schedule
  • Contact Information in the (People Section)
    email, IM, Phone etc..
  • All course announcements will be made via email
    and Website.
  • Lecture Slides will be made available on the
    course website (Course Material Section)
  • Videos of Lectures will be made available on the
    course website (Course Material Section) after
    every lecture.

64
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Contact Information
  • Prof. Thomas Sterling
  • tron_at_cct.lsu.edu
  • (225) 578-8982 (CCT Office)
  • Coates Hall Room 284, (225) 578-3320
  • Office Hours Tu Th,1230 300 PM
  • Teaching Assistant
  • Chirag Dekate
  • cdekate_at_cct.lsu.edu
  • (225) 578-8930
  • Office Hours (Coates 284)
  • Tu Th, 1230 300 PM
  • AIM / Yahoo / gTalk cdekate
  • Course Secretary
  • Ms. Terri Bordelon
  • tbordelon_at_cct.lsu.edu
  • 302 Johnston Hall (225) 578-5979

65
66
Grading Policy
66
67
Assignments
  • Segments 1-6 (inclusive) will have prescribed
    problem sets.
  • Students are required to turn in the problem sets
    no later than their due dates.
  • Cumulatively these problem sets account for 20
    of the overall grade for Graduate students (30
    for undergraduates)
  • IMPORTANT
  • Most of the assignments will need to be run on
    local supercomputing resources that are shared
    among several users.
  • Jobs that you submit WILL get stuck in a queue.
  • Queue ate my homework Not an acceptable
    excuse for not turning homework in.
  • Your are strongly encouraged to start work on
    assignments as and when they are assigned to
    avoid inevitable queue wait times.

67
68
Schedule
68
69
Schedule
69
70
Reference Material
  • No Required Textbook
  • Lecture notes (slides), required reading lists
    (URLs) provided at the end of lectures, some
    additional notes (on web site), and assignments
    would be primary sources of material for exams.
  • Students are strongly encouraged to pursue
    additional reading material available on the
    internet (and as part of projects).

70
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Compute Resources
71
72
Plagiarism
  • The LSU Code of Student Conduct defines
    plagiarism in Section 5.1.16
  • "Plagiarism is defined as the unacknowledged
    inclusion of someone else's words, structure,
    ideas, or data. When a student submits work as
    his/her own that includes the words, structure,
    ideas, or data of others, the source of this
    information must be acknowledged through
    complete, accurate, and specific references, and,
    if verbatim statements are included, through
    quotation marks as well. Failure to identify any
    source (including interviews, surveys, etc.),
    published in any medium (including on the
    internet) or unpublished, from which words,
    structure, ideas, or data have been taken,
    constitutes plagiarism
  • Plagiarism will not be tolerated and will be
    dealt with in accordance with and as outlined by
    the LSU Code of Student Conduct
  • http//appl003.lsu.edu/slas/dos.nsf/Content/Code
    ofConduct?OpenDocument

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Summary
  • History of supercomputing achieves performance
    gain of gt billion in single lifetime
  • Performance achieved
  • Technology clock rate (logic switching speed) and
    density
  • Parallelism through architecture and computing
    models
  • Algorithms and programming languages and tools
  • Performance degraded
  • Latency, overhead, contention, starvation
  • Cost, power consumption, size
  • Programming difficulties
  • Productivity considers all aspects of user goals

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