1. Types of Parallel Computers
Two principal types:
Shared memory multiprocessor
Distributed memory multicomputer
ITCS 4/5145 Cluster Computing, UNC-Charlotte, B. Wilkinson, 2006.

2. Shared Memory Multiprocessor

3. Conventional Computer
Consists of a processor executing a program stored in a (main) memory:
Each main memory location located by its address. Addresses start at 0 and extend to 2b - 1 when there are b bits (binary digits) in address.
Main memory
Instr
uctions (to processor)
Data (to or from processor)
Processor

4. Shared Memory Multiprocessor System
Natural way to extend single processor model - have multiple processors connected to multiple memory modules, such that each processor can access any memory module :
Memory module
One
address
space
Processor-memory Interconnections
Processors

5. Simplistic view of a small shared memory multiprocessor
Processors
Shared memory
Bus
Examples:
Dual Pentiums
Quad Pentiums

6. Real computer system have cache memory between the main memory and processors. Level 1 (L1) cache and Level 2 (L2) cache Example Quad Shared Memory Multiprocessor
Processor
Processor
Processor
Processor
L1 cache
L1 cache
L1 cache
L1 cache
L2 Cache
L2 Cache
L2 Cache
L2 Cache
Bus interface
Bus interface
Bus interface
Bus interface
Processor/
memory b us
I/O interf
ace
Memory controller
I/O b us
Memory
Shared memory

7. “Recent” innovation Dual-core and multi-core processors
Two or more independent processors in one package
Actually an old idea but not put into wide practice until recently.
Since L1 cache is usually inside package and L2 cache outside package, dual-/multi-core processors usually share L2 cache.

8. Example
Dual core Pentiums (Intel CoreTM2 Dual processors) -- Two processors in one package sharing a common L2 Cache. Introduced April (Also hyper-threaded)
Xbox 360 game console -- triple core PowerPC microprocessor.
PlayStation 3 Cell processor -- 9 core design.
References and more information:

9. Programming Shared Memory Multiprocessors Several possible ways
1. Use Threads - programmer decomposes program into individual parallel sequences, (threads), each being able to access shared variables declared outside threads.
Example Pthreads
2. Use library functions and preprocessor compiler directives with a sequential programming language to declare shared variables and specify parallelism.
Example OpenMP - industry standard. Consists of library functions, compiler directives, and environment variables - needs OpenMP compiler

10. 3. Use a modified sequential programming language -- added syntax to declare shared variables and specify parallelism.
Example UPC (Unified Parallel C) - needs a UPC compiler.
4. Use a specially designed parallel programming language -- with syntax to express parallelism. Compiler automatically creates executable code for each processor (not now common).
5. Use a regular sequential programming language such as C and ask parallelizing compiler to convert it into parallel executable code. Also not now common.

11. Message-Passing Multicomputer
Complete computers connected through an interconnection network:
Interconnection
network
Messages
Processor
Local
memory
Computers

12. Interconnection Networks
Limited and exhaustive interconnections
2- and 3-dimensional meshes
Hypercube (not now common)
Using Switches:
Crossbar
Trees
Multistage interconnection networks

13. Two-dimensional array (mesh)
Computer/
Links
processor
Also three-dimensional - used in some large high performance systems.

14. Three-dimensional hypercube

15. Four-dimensional hypercube
Hypercubes popular in 1980’s - not now

16. Crossbar switch
Memor
ies
Processors
Switches

17. Tree
Root
Switch
Links
element
Processors

18. Multistage Interconnection Network Example: Omega network2
2 switch elements
(straight-through or
crossover connections)
000
000
001
001
010
010
011
011
Inputs
Outputs
100
100
101
101
110
110
111
111

19. Networked Computers as a Computing Platform
A network of computers became a very attractive alternative to expensive supercomputers and parallel computer systems for high-performance computing in early 1990’s.
Several early projects. Notable:
Berkeley NOW (network of workstations) project.
NASA Beowulf project.

20. Key advantages:
Very high performance workstations and PCs readily available at low cost.
The latest processors can easily be incorporated into the system as they become available.
Existing software can be used or modified.

21. Beowulf Clusters*
A group of interconnected “commodity” computers achieving high performance with low cost.
Typically using commodity interconnects - high speed Ethernet, and Linux OS.
* Beowulf comes from name given by NASA Goddard Space Flight Center cluster project.

22. Cluster Interconnects
Originally fast Ethernet on low cost clusters
Gigabit Ethernet - easy upgrade path
More Specialized/Higher Performance
Myrinet Gbits/sec - disadvantage: single vendor
cLan
SCI (Scalable Coherent Interface)
QNet
Infiniband - may be important as infininband interfaces may be integrated on next generation PCs

23. Dedicated cluster with a master node and compute nodes
User
Computers
Dedicated Cluster
External network
Ethernet interface
Master node
Switch
Local network
Compute nodes

24. Software Tools for Clusters
Based upon Message Passing Parallel Programming:
Parallel Virtual Machine (PVM) - developed in late 1980’s. Became very popular.
Message-Passing Interface (MPI) - standard defined in 1990s.
Both provide a set of user-level libraries for message passing. Use with regular programming languages (C, C++, ...).