1b.1 Types of Parallel Computers Two principal types: Shared memory multiprocessor Distributed...
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1b.1
Types of Parallel Computers
Two principal types:
• Shared memory multiprocessor
• Distributed memory multicomputer
ITCS 4/5145 Cluster Computing, UNC-Charlotte, B. Wilkinson, 2006.
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1b.2
Shared Memory Multiprocessor
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1b.3
Conventional ComputerConsists 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
Processor
Instructions (to processor)Data (to or from processor)
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1b.4
Shared Memory Multiprocessor SystemNatural way to extend single processor model - have multiple processors connected to multiple memory modules, such that each processor can access any memory module :
Processors
Processor-memory Interconnections
Memory moduleOneaddressspace
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1b.5
Simplistic view of a small shared memory multiprocessor
Examples:• Dual Pentiums• Quad Pentiums
Processors Shared memory
Bus
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1b.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
L2 Cache
Bus interface
L1 cache
Processor
L2 Cache
Bus interface
L1 cache
Processor
L2 Cache
Bus interface
L1 cache
Processor
L2 Cache
Bus interface
L1 cache
Memory controller
Memory
I/O interface
I/O bus
Processor/memorybus
Shared memory
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1b.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.
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1b.8
Example
• Dual core Pentiums (Intel CoreTM2 Dual processors) -- Two processors in one package sharing a common L2 Cache. Introduced April 2005. (Also hyper-threaded)
• Xbox 360 game console -- triple core PowerPC microprocessor.
• PlayStation 3 Cell processor -- 9 core design.
References and more information:
http://www.intel.com/products/processor/core2duo/index.htm
http://en.wikipedia.org/wiki/Dual_core
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1b.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
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1b.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.
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1b.11
Message-Passing Multicomputer
Complete computers connected through an interconnection network:
Processor
Interconnectionnetwork
Local
Computers
Messages
memory
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1b.12
Interconnection Networks
• Limited and exhaustive interconnections• 2- and 3-dimensional meshes• Hypercube (not now common)• Using Switches:
– Crossbar– Trees– Multistage interconnection networks
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1b.13
Two-dimensional array (mesh)
Also three-dimensional - used in some large high performance systems.
LinksComputer/processor
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1b.14
Three-dimensional hypercube
000 001
010 011
100
110
101
111
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1b.15
Four-dimensional hypercube
Hypercubes popular in 1980’s - not now
0000 0001
0010 0011
0100
0110
0101
0111
1000 1001
1010 1011
1100
1110
1101
1111
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1b.16
Crossbar switch
SwitchesProcessors
Memories
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1b.17
Tree
Switchelement
Root
Links
Processors
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1b.18
Multistage Interconnection NetworkExample: Omega network
000
001
010
011
100
101
110
111
000
001
010
011
100
101
110
111
Inputs Outputs
2 ´ 2 switch elements(straight-through or
crossover connections)
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1b.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.
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1b.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.
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1b.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.
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1b.22
Cluster Interconnects
• Originally fast Ethernet on low cost clusters• Gigabit Ethernet - easy upgrade path
More Specialized/Higher Performance• Myrinet - 2.4 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
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1b.23
Dedicated cluster with a master node and compute nodes
User
Master node
Compute nodes
Dedicated Cluster
Ethernet interface
Switch
External network
Computers
Local network
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1b.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++, ...).