1. Types of Parallel Computers
Two principal approaches:
Shared memory multiprocessor
Distributed memory multicomputer
ITCS 4/5145 Parallel Programming, UNC-Charlotte, B. Wilkinson, Jan 12, 2012

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
Memory controller
Memory
Shared memory

7. “Recent” innovation (since 2005)
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 with the limits of making single processors faster principally caused by:
Power dissipation (power wall) and clock frequency limitations
Limits in parallelism within a single instruction stream
Memory speed limitations (memory wall)

8. Power dissipation Clock frequency
“The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software” Herb Sutter,

9. Single quad core shared memory multiprocessor
Chip
Processor
L1 cache
L2 Cache
Memory controller
Memory
Shared memory

10. Multiple quad-core multiprocessors (example coit-grid05.uncc.edu)
L1 cache
Processor
L1 cache
L2 Cache
possible L3 cache
Memory controller
Memory
Shared memory

11. Programming Shared Memory Multiprocessors
Several possible ways – we will concentrate upon using threads
Threads - individual parallel sequences (threads), each thread having their own local variables but being able to access shared variables declared outside threads.
1. Low–level thread libraries - programmer calls thread routines to create and control the threads. Example Pthreads, Java threads.
2. Higher level library functions and preprocessor compiler directives.
Example OpenMP - industry standard. Consists of library functions, compiler directives, and environment variables

12. Tasks – rather than program with threads, which are closely linked to the physical hardware, can program with parallel “tasks”. Promoted by Intel with their TBB (Thread Building Blocks) tools.
Other alternatives include parallelizing compilers compiling regular sequential programs and making them parallel programs, and special parallel languages (both not now common).

13. Message-Passing Multicomputer

14. Message-Passing Multicomputer
Complete computers connected through an interconnection network:
Many interconnection networks explored in the 1970s and 1980s
including 2- and 3-dimensional meshes, hypercubes, and multistage interconnection networks
Interconnection
network
Messages
Processor
Local
memory
Computers

15. 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 1990s.
Several early projects. Notable:
Berkeley NOW (network of workstations) project.
NASA Beowulf project.

16. 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.

17. 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.

18. Cluster Interconnects
Originally fast Ethernet on low cost clusters
Gigabit Ethernet - easy upgrade path
More specialized/higher performance interconnects available including Myrinet and Infiniband.

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

20. Software Tools for Clusters
Based upon message passing programming model
User-level libraries provided for explicitly specifying messages to be sent between executing processes on each computer .
Use with regular programming languages (C, C++, ...).
Can be quite difficult to program correctly as we shall see.

21. GPU clusters
Recent trend for clusters – incorporating GPUs for high performance.
At least three of the five fastest computers in the world are GPU clusters

22. Next step
Learn the message passing programming model, some MPI routines, write a message-passing program and test on the cluster.