1. Introduction to Parallel Processing
CS 147
November 12, 2004
Johnny Lai

2. Multi-Processor Computing System
Computing Elements
Applications
Programming paradigms P .
Microkernel
Multi-Processor Computing System
Threads Interface
Operating System
Hardware
Process
Processor
Thread P

3. Two Eras of Computing Architectures System Software/Compiler
Applications
P.S.Es
System Software
Sequential
Era
Parallel
Era
Commercialization
R & D Commodity

4. History of Parallel Processing
PP can be traced to a tablet dated around 100 BC.
Tablet has 3 calculating positions.
Infer that multiple positions:
Reliability/ Speed

5. Why Parallel Processing?
Computation requirements are ever increasing -- visualization, distributed databases, simulations, scientific prediction (earthquake), etc.
Sequential architectures reaching physical limitation (speed of light, thermodynamics)

6. Human Architecture! Growth Performance
Vertical
Horizontal
Growth
Age

7. Computational Power Improvement
Multiprocessor
C.P.I.
Uniprocessor
No. of Processors

8. Why Parallel Processing?
The Tech. of PP is mature and can be exploited commercially; significant R & D work on development of tools & environment.
Significant development in Networking technology is paving a way for heterogeneous computing.

9. Why Parallel Processing?
Hardware improvements like Pipelining, Superscalar, etc., are non-scalable and requires sophisticated Compiler Technology.
Vector Processing works well for certain kind of problems.

10. Parallel Program has & needs ...
Multiple “processes” active simultaneously solving a given problem, general multiple processors.
Communication and synchronization of its processes (forms the core of parallel programming efforts).

11. Processing Elements Architecture

12. Processing Elements Simple classification by Flynn:
(No. of instruction and data streams)
SISD - conventional
SIMD - data parallel, vector computing
MISD - systolic arrays
MIMD - very general, multiple approaches.
Current focus is on MIMD model, using general purpose processors.
(No shared memory)
There are many different ways to organize the processors and memory within a multiprocessor system, and different ways to classify these system

13. SISD : A Conventional Computer
Processor
Data Input
Data Output
Instructions
Speed is limited by the rate at which computer can transfer information internally.
Ex:PC, Macintosh, Workstations

14. The MISD Architecture
Data
Input
Stream
Output
Processor A B C
Instruction
Stream A
Stream B
Instruction Stream C
More of an intellectual exercise than a practicle configuration. Few built, but commercially not available

15. SIMD Architecture
Instruction
Stream
Processor A B C
Data Input
stream A
stream B
stream C
Data Output
Ci<= Ai * Bi
Ex: CRAY machine vector processing, Thinking machine cm*
Intel MMX (multimedia support)

16. MIMD Architecture
Instruction
Stream A
Instruction
Stream B
Instruction
Stream C
Data Output
stream A
Data Input
stream A
Processor A
Data Output
stream B
Data Input
stream B
Processor B
Data Output
stream C
Processor C
Data Input
stream C
Unlike SISD, MISD, MIMD computer works asynchronously.
Shared memory (tightly coupled) MIMD
Distributed memory (loosely coupled) MIMD

17. Shared Memory MIMD machine
Processor A
Processor B
Processor C
MEMORY
BUS
MEMORY
BUS
MEMORY
BUS
Global Memory System
Comm: Source PE writes data to GM & destination retrieves it
Easy to build, conventional OSes of SISD can be easily be ported
Limitation : reliability & expandibility. A memory component or any processor failure affects the whole system.
Increase of processors leads to memory contention.
Ex. : Silicon graphics supercomputers....

18. Distributed Memory MIMD
IPC
channel
IPC
channel
Processor A
Processor B
Processor C
MEMORY
BUS
MEMORY
BUS
MEMORY
BUS
Memory
System A
System B
System C
Communication : IPC on High Speed Network.
Network can be configured to ... Tree, Mesh, Cube, etc.
Unlike Shared MIMD
easily/ readily expandable
Highly reliable (any CPU failure does not affect the whole system)

19. Laws of caution.....
Speed of computers is proportional to the square of their cost.
i.e. cost = Speed
Speedup by a parallel computer increases as the logarithm of the number of processors.
Speedup = log2(no. of processors) C S
(speed = cost2) S P
log2P

20. Caution....
Very fast development in PP and related area have blurred concept boundaries, causing lot of terminological confusion : concurrent computing/ programming, parallel computing/ processing, multiprocessing, distributed computing, etc.

21. It’s hard to imagine a field that changes as rapidly as computing.

22. Caution....
Even well-defined distinctions like shared memory and distributed memory are merging due to new advances in technolgy.
Good environments for developments and debugging are yet to emerge.

23. Caution....
There is no strict delimiters for contributors to the area of parallel processing : CA,OS, HLLs, databases, computer networks, all have a role to play.
This makes it a Hot Topic of Research

24. Types of Parallel Systems
Shared Memory Parallel
Smallest extension to existing systems
Program conversion is incremental
Distributed Memory Parallel
Completely new systems
Programs must be reconstructed
Clusters
Slow communication form of Distributed

25. Operating Systems for PP
MPP systems having thousands of processors requires OS radically different fromcurrent ones.
Every CPU needs OS :
to manage its resources
to hide its details
Traditional systems are heavy, complex and not suitable for MPP

26. Operating System Models
Frame work that unifies features, services and tasks performed
Three approaches to building OS....
Monolithic OS
Layered OS
Microkernel based OS
Client server OS
Suitable for MPP systems
Simplicity, flexibility and high performance are crucial for OS.

27. Monolithic Operating System
Application
Programs
System Services
Hardware
User Mode
Kernel Mode
Better application Performance
Difficult to extend
Ex: MS-DOS

28. Memory & I/O Device Mgmt
Layered OS
Application
Programs
Application
Programs
User Mode
Kernel Mode
System Services
Memory & I/O Device Mgmt
Process Schedule
Hardware
Easier to enhance
Each layer of code access lower level interface
Low-application performance
Ex : UNIX

29. Traditional OS OS User Mode Kernel Mode OS Designer Application
Programs
User Mode
Kernel Mode OS
Hardware
OS Designer

30. New trend in OS design Servers Microkernel User Mode Kernel Mode
Application
Programs
Application
Programs
User Mode
Kernel Mode
Microkernel
Hardware

31. Microkernel/Client Server OS (for MPP Systems)
Application
Thread
lib.
File
Server
Network
Server
Display
Server
User
Kernel
Microkernel
Send
Reply
Hardware
Tiny OS kernel providing basic primitive (process, memory, IPC)
Traditional services becomes subsystems
Monolithic Application Perf. Competence
OS = Microkernel + User Subsystems
Ex: Mach, PARAS, Chorus, etc.

32. Few Popular Microkernel Systems
MACH, CMU
PARAS, C-DAC
Chorus
QNX,
(Windows)

33. Reference http://www.cs.mu.oz.au http://www.whatis.com
Computer System Organization & Architecture John D. Carpinelli
(^_^)