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CS 258 Parallel Computer Architecture Lecture 1 Introduction to Parallel Architecture

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Emerging Technologies. Interleaving. Bus protocols. RAID. VLSI. Input/Output and Storage ... even through few of you will become PP designers ... – PowerPoint PPT presentation

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Title: CS 258 Parallel Computer Architecture Lecture 1 Introduction to Parallel Architecture


1
CS 258 Parallel Computer ArchitectureLecture
1Introduction to Parallel Architecture
  • January 23, 2002
  • Prof John D. Kubiatowicz

2
Computer Architecture Is
  • the attributes of a computing system as seen
    by the programmer, i.e., the conceptual structure
    and functional behavior, as distinct from the
    organization of the data flows and controls the
    logic design, and the physical implementation.
  • Amdahl, Blaaw, and Brooks, 1964

SOFTWARE
3
Instruction Set Architecture (ISA)
software
instruction set
hardware
  • Great picture for uniprocessors.
  • Rapidly crumbling, however!
  • Can this be true for multiprocessors???
  • Much harder to say.

4
What is Parallel Architecture?
  • A parallel computer is a collection of processing
    elements that cooperate to solve large problems
    fast
  • Some broad issues
  • Models of computation PRAM? BSP? Sequential
    Consistency?
  • Resource Allocation
  • how large a collection?
  • how powerful are the elements?
  • how much memory?
  • Data access, Communication and Synchronization
  • how do the elements cooperate and communicate?
  • how are data transmitted between processors?
  • what are the abstractions and primitives for
    cooperation?
  • Performance and Scalability
  • how does it all translate into performance?
  • how does it scale?

5
Computer Architecture Topics (252)
Input/Output and Storage
Disks, WORM, Tape
RAID
Emerging Technologies Interleaving Bus protocols
DRAM
Coherence, Bandwidth, Latency
Memory Hierarchy
L2 Cache
Network Communication
Other Processors
L1 Cache
Addressing, Protection, Exception Handling
VLSI
Instruction Set Architecture
Pipelining and Instruction Level Parallelism
Pipelining, Hazard Resolution, Superscalar,
Reordering, Prediction, Speculation, Vector,
Dynamic Compilation
6
Computer Architecture Topics (258)
Shared Memory, Message Passing, Data Parallelism
M
P
M
P
M
P
M
P
  
Network Interfaces
S
Interconnection Network
Processor-Memory-Switch
Topologies, Routing, Bandwidth, Latency, Reliabili
ty
Multiprocessors Networks and Interconnections Ever
ything in previous slide but more so!
7
What will you get out of CS258?
  • In-depth understanding of the design and
    engineering of modern parallel computers
  • technology forces
  • Programming models
  • fundamental architectural issues
  • naming, replication, communication,
    synchronization
  • basic design techniques
  • cache coherence, protocols, networks, pipelining,
  • methods of evaluation
  • from moderate to very large scale
  • across the hardware/software boundary
  • Study of REAL parallel processors
  • Research papers, white papers
  • Natural consequences??
  • Massive Parallelism ? Reconfigurable computing?
  • Message Passing Machines ? NOW ? Peer-to-peer
    systems?

8
Will it be worthwhile?
  • Absolutely!
  • even through few of you will become PP designers
  • The fundamental issues and solutions translate
    across a wide spectrum of systems.
  • Crisp solutions in the context of parallel
    machines.
  • Pioneered at the thin-end of the platform pyramid
    on the most-demanding applications
  • migrate downward with time
  • Understand implications for software
  • Network attachedstorage, MEMs, etc?

9
Why Study Parallel Architecture?
  • Role of a computer architect
  • To design and engineer the various levels of a
    computer system to maximize performance and
    programmability within limits of technology and
    cost.
  • Parallelism
  • Provides alternative to faster clock for
    performance
  • Applies at all levels of system design
  • Is a fascinating perspective from which to view
    architecture
  • Is increasingly central in information processing
  • How is instruction-level parallelism related to
    course-grained parallelism??

10
Is Parallel Computing Inevitable?
  • This was certainly not clear just a few years ago
  • Today, however
  • Application demands Our insatiable need for
    computing cycles
  • Technology Trends Easier to build
  • Architecture Trends Better abstractions
  • Economics Cost of pushing uniprocessor
  • Current trends
  • Todays microprocessors have multiprocessor
    support
  • Servers and workstations becoming MP Sun, SGI,
    DEC, COMPAQ!...
  • Tomorrows microprocessors are multiprocessors

11
Can programmers handle parallelism?
  • Humans not as good at parallel programming as
    they would like to think!
  • Need good model to think of machine
  • Architects pushed on instruction-level
    parallelism really hard, because it is
    transparent
  • Can compiler extract parallelism?
  • Sometimes
  • How do programmers manage parallelism??
  • Language to express parallelism?
  • How to schedule varying number of processors?
  • Is communication Explicit (message-passing) or
    Implicit (shared memory)?
  • Are there any ordering constraints on
    communication?

12
Granularity
  • Is communication fine or coarse grained?
  • Small messages vs big messages
  • Is parallelism fine or coarse grained
  • Small tasks (frequent synchronization) vs big
    tasks
  • If hardware handles fine-grained parallelism,
    then easier to get incremental scalability
  • Fine-grained communication and parallelism harder
    than coarse-grained
  • Harder to build with low overhead
  • Custom communication architectures often needed
  • Ultimate course grained communication
  • GIMPS (Great Internet Mercenne Prime Search)
  • Communication once a month

13
CS258 Staff
  • InstructorProf John D. Kubiatowicz
  • Office 673 Soda Hall, 643-6817 kubitron_at_cs
  • Office Hours Thursday 130 - 300 or by appt.
  • Class Wed, Fri, 100 - 230pm 310 Soda Hall
  • Administrative Veronique Richard,
  • Office 676 Soda Hall, 642-4334 nicou_at_cs
  • Web page http//www.cs/kubitron/courses/cs258-S0
    2/
  • Lectures available online lt1130AM day of
    lecture
  • Email cs258_at_kubi.cs.berkeley.edu
  • Clip signup link on web page (as soon as it is up)

14
Why Me? The Alewife Multiprocessor
  • Cache-coherence Shared Memory
  • Partially in Software!
  • User-level Message-Passing
  • Rapid Context-Switching
  • Asynchronous network
  • One node/board

15
TextBook Two leaders in field
  • Text Parallel Computer Architecture
  • A Hardware/Software Approach,
  • By David Culler Jaswinder Singh
  • Covers a range of topics
  • We will not necessarily cover
  • them in order.

16
Lecture style
  • 1-Minute Review
  • 20-Minute Lecture/Discussion
  • 5- Minute Administrative Matters
  • 25-Minute Lecture/Discussion
  • 5-Minute Break (water, stretch)
  • 25-Minute Lecture/Discussion
  • Instructor will come to class early stay after
    to answer questions

Attention
20 min.
Break
In Conclusion, ...
Time
17
Research Paper Reading
  • As graduate students, you are now researchers.
  • Most information of importance to you will be in
    research papers.
  • Ability to rapidly scan and understand research
    papers is key to your success.
  • So you will read lots of papers in this course!
  • Quick 1 paragraph summaries will be due in class
  • Students will take turns discussing papers
  • Papers will be scanned and on web page.

18
How will grading work?
  • No TA This term!
  • Tentative breakdown
  • 20 homeworks / paper presentations
  • 30 exam
  • 40 project (teams of 2)
  • 10 participation

19
Application Trends
  • Application demand for performance fuels advances
    in hardware, which enables new applns, which...
  • Cycle drives exponential increase in
    microprocessor performance
  • Drives parallel architecture harder
  • most demanding applications
  • Programmers willing to work really hard to
    improve high-end applications
  • Need incremental scalability
  • Need range of system performance with
    progressively increasing cost

20
Speedup
  • Speedup (p processors)
  • Common mistake
  • Compare parallel program on 1 processor to
    parallel program on p processors
  • Wrong!
  • Should compare uniprocessor program on 1
    processor to parallel program on p processors
  • Why? Keeps you honest
  • It is easy to parallelize overhead.

21
Amdahl's Law
  • Speedup due to enhancement E
  • ExTime w/o E Performance w/ E
  • Speedup(E) -------------
    -------------------
  • ExTime w/ E Performance w/o
    E
  • Suppose that enhancement E accelerates a fraction
    F of the task by a factor S, and the remainder of
    the task is unaffected

22
Amdahls Law for parallel programs?
Best you could ever hope to do
Worse Overhead may kill your performance!
23
Metrics of Performance
Application
Answers per month Operations per second
Programming Language
Compiler
(millions) of Instructions per second
MIPS (millions) of (FP) operations per second
MFLOP/s
ISA
Datapath
Megabytes per second
Control
Function Units
Cycles per second (clock rate)
Transistors
Wires
Pins
24
Commercial Computing
  • Relies on parallelism for high end
  • Computational power determines scale of business
    that can be handled
  • Databases, online-transaction processing,
    decision support, data mining, data warehousing
    ...
  • TPC benchmarks (TPC-C order entry, TPC-D decision
    support)
  • Explicit scaling criteria provided
  • Size of enterprise scales with size of system
  • Problem size not fixed as p increases.
  • Throughput is performance measure (transactions
    per minute or tpm)

25
TPC-C Results for March 1996
  • Parallelism is pervasive
  • Small to moderate scale parallelism very
    important
  • Difficult to obtain snapshot to compare across
    vendor platforms

26
Scientific Computing Demand
27
Engineering Computing Demand
  • Large parallel machines a mainstay in many
    industries
  • Petroleum (reservoir analysis)
  • Automotive (crash simulation, drag analysis,
    combustion efficiency),
  • Aeronautics (airflow analysis, engine efficiency,
    structural mechanics, electromagnetism),
  • Computer-aided design
  • Pharmaceuticals (molecular modeling)
  • Visualization
  • in all of the above
  • entertainment (films like Toy Story)
  • architecture (walk-throughs and rendering)
  • Financial modeling (yield and derivative
    analysis)
  • etc.

28
Applications Speech and Image Processing
  • Also CAD, Databases, . . .
  • 100 processors gets you 10 years, 1000 gets you
    20 !

29
Is better parallel arch enough?
  • AMBER molecular dynamics simulation program
  • Starting point was vector code for Cray-1
  • 145 MFLOP on Cray90, 406 for final version on
    128-processor Paragon, 891 on 128-processor Cray
    T3D

30
Summary of Application Trends
  • Transition to parallel computing has occurred for
    scientific and engineering computing
  • In rapid progress in commercial computing
  • Database and transactions as well as financial
  • Usually smaller-scale, but large-scale systems
    also used
  • Desktop also uses multithreaded programs, which
    are a lot like parallel programs
  • Demand for improving throughput on sequential
    workloads
  • Greatest use of small-scale multiprocessors
  • Solid application demand exists and will increase

31
Technology Trends
  • Today the natural building-block is also fastest!

32
Cant we just wait for it to get faster?
  • Microprocessor performance increases 50 - 100
    per year
  • Transistor count doubles every 3 years
  • DRAM size quadruples every 3 years
  • Huge investment per generation is carried by huge
    commodity market

180
160
140
DEC
120
alpha
Integer
FP
100
IBM
HP 9000
80
RS6000
750
60
540
MIPS
MIPS
40
M2000
Sun 4
M/120
20
260
0
1987
1988
1989
1990
1991
1992
33
Technology A Closer Look
  • Basic advance is decreasing feature size ( ??)
  • Circuits become either faster or lower in power
  • Die size is growing too
  • Clock rate improves roughly proportional to
    improvement in ?
  • Number of transistors improves like ????(or
    faster)
  • Performance gt 100x per decade
  • clock rate lt 10x, rest is transistor count
  • How to use more transistors?
  • Parallelism in processing
  • multiple operations per cycle reduces CPI
  • Locality in data access
  • avoids latency and reduces CPI
  • also improves processor utilization
  • Both need resources, so tradeoff
  • Fundamental issue is resource distribution, as in
    uniprocessors

34
Growth Rates
  • 30 per year

40 per year
35
Architectural Trends
  • Architecture translates technologys gifts into
    performance and capability
  • Resolves the tradeoff between parallelism and
    locality
  • Current microprocessor 1/3 compute, 1/3 cache,
    1/3 off-chip connect
  • Tradeoffs may change with scale and technology
    advances
  • Understanding microprocessor architectural trends
  • gt Helps build intuition about design issues or
    parallel machines
  • gt Shows fundamental role of parallelism even in
    sequential computers

36
Phases in VLSI Generation
37
Architectural Trends
  • Greatest trend in VLSI generation is increase in
    parallelism
  • Up to 1985 bit level parallelism 4-bit -gt 8 bit
    -gt 16-bit
  • slows after 32 bit
  • adoption of 64-bit now under way, 128-bit far
    (not performance issue)
  • great inflection point when 32-bit micro and
    cache fit on a chip
  • Mid 80s to mid 90s instruction level parallelism
  • pipelining and simple instruction sets,
    compiler advances (RISC)
  • on-chip caches and functional units gt
    superscalar execution
  • greater sophistication out of order execution,
    speculation, prediction
  • to deal with control transfer and latency
    problems
  • Next step thread level parallelism? Bit-level
    parallelism?

38
How far will ILP go?
  • Infinite resources and fetch bandwidth, perfect
    branch prediction and renaming
  • real caches and non-zero miss latencies

39
Threads Level Parallelism on board
MEM
  • Micro on a chip makes it natural to connect many
    to shared memory
  • dominates server and enterprise market, moving
    down to desktop
  • Alternative many PCs sharing one complicated
    pipe
  • Faster processors began to saturate bus, then bus
    technology advanced
  • today, range of sizes for bus-based systems,
    desktop to large servers

No. of processors in fully configured commercial
shared-memory systems
40
What about Multiprocessor Trends?
41
Bus Bandwidth
42
What about Storage Trends?
  • Divergence between memory capacity and speed even
    more pronounced
  • Capacity increased by 1000x from 1980-95, speed
    only 2x
  • Gigabit DRAM by c. 2000, but gap with processor
    speed much greater
  • Larger memories are slower, while processors get
    faster
  • Need to transfer more data in parallel
  • Need deeper cache hierarchies
  • How to organize caches?
  • Parallelism increases effective size of each
    level of hierarchy, without increasing access
    time
  • Parallelism and locality within memory systems
    too
  • New designs fetch many bits within memory chip
    follow with fast pipelined transfer across
    narrower interface
  • Buffer caches most recently accessed data
  • Processor in memory?
  • Disks too Parallel disks plus caching

43
Economics
  • Commodity microprocessors not only fast but CHEAP
  • Development costs tens of millions of dollars
  • BUT, many more are sold compared to
    supercomputers
  • Crucial to take advantage of the investment, and
    use the commodity building block
  • Multiprocessors being pushed by software vendors
    (e.g. database) as well as hardware vendors
  • Standardization makes small, bus-based SMPs
    commodity
  • Desktop few smaller processors versus one larger
    one?
  • Multiprocessor on a chip?


44
Can anyone afford high-end MPPs???
  • ASCI (Accellerated Strategic Computing
    Initiative) ASCI White Built by IBM
  • 12.3 TeraOps, 8192 processors (RS/6000)
  • 6TB of RAM, 160TB Disk
  • 2 basketball courts in size
  • Program it??? Message passing

45
Consider Scientific Supercomputing
  • Proving ground and driver for innovative
    architecture and techniques
  • Market smaller relative to commercial as MPs
    become mainstream
  • Dominated by vector machines starting in 70s
  • Microprocessors have made huge gains in
    floating-point performance
  • high clock rates
  • pipelined floating point units (e.g.,
    multiply-add every cycle)
  • instruction-level parallelism
  • effective use of caches (e.g., automatic
    blocking)
  • Plus economics
  • Large-scale multiprocessors replace vector
    supercomputers

46
Raw Uniprocessor Performance LINPACK
47
Raw Parallel Performance LINPACK
  • Even vector Crays became parallel
  • X-MP (2-4) Y-MP (8), C-90 (16), T94 (32)
  • Since 1993, Cray produces MPPs too (T3D, T3E)

48
500 Fastest Computers
49
Summary Why Parallel Architecture?
  • Increasingly attractive
  • Economics, technology, architecture, application
    demand
  • Increasingly central and mainstream
  • Parallelism exploited at many levels
  • Instruction-level parallelism
  • Multiprocessor servers
  • Large-scale multiprocessors (MPPs)
  • Focus of this class multiprocessor level of
    parallelism
  • Same story from memory system perspective
  • Increase bandwidth, reduce average latency with
    many local memories
  • Spectrum of parallel architectures make sense
  • Different cost, performance and scalability

50
Where is Parallel Arch Going?
Old view Divergent architectures, no predictable
pattern of growth.
Application Software
System Software
Systolic Arrays
SIMD
Architecture
Message Passing
Dataflow
Shared Memory
  • Uncertainty of direction paralyzed parallel
    software development!

51
Today
  • Extension of computer architecture to support
    communication and cooperation
  • Instruction Set Architecture plus Communication
    Architecture
  • Defines
  • Critical abstractions, boundaries, and primitives
    (interfaces)
  • Organizational structures that implement
    interfaces (hw or sw)
  • Compilers, libraries and OS are important bridges
    today
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