computer architecture introduction to mimd architectures ola flygt växjö university ...
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Computer Computer ArchitectureArchitecture
Introduction to MIMD Introduction to MIMD architecturesarchitecturesOla Flygt
Växjö Universityhttp://w3.msi.vxu.se/users/ofl/
[email protected]+46 470 70 86 49
Outline
{Multi-processor} {Multi-computer}15.1 Architectural concepts15.2 Problems of scalable computers15.3 Main design issues of scalable MI
MD computers
CH01
Multi-computer (distributed memory system):
Advantages and Disadvantages
+ Highly Scalable+ Message passing solves memory
access synchronization problem
- Load balancing problem- Deadlock in message passing- Need to physically copying data
between processes
Multi-processor (shared memory system):Advantages and Disadvantages
+ No need to partition data or program, uniprocessor programming techniques can be adapted
+ Communication between processor is efficient
- Synchronized access to share data in memory needed. Synchronising constructs (semaphores, conditional critical regions, monitors) result in nondeterministc behaviour which can lead programming errors that are difficult to discover
- Lack of scalability due to (memory) contention problem
Best of Both Worlds: Multicomputer using virtual shared
memory Also called distributed shared memory
architecture The local memories of multi-computer are
components of global address space: any processor can access the local memory of any
other processor
Three approaches: Non-uniform memory access (NUMA) machines Cache-only memory access (COMA) machines Cache-coherent non-uniform memory access
(CC-NUMA) machines
NUMA
Logically shared memory is physically distributed
Different access of local and remote memory blocks. Remote access takes much more time – latency
Sensitive to data and program distributionClose to distributed memory systems, yet
the programming paradigm is differentExample: Cray T3D
COMA
Each block of the shared memory works as local cache of a processor
Continuous, dynamic migration of dataHit-rate decreases the traffic on the
Interconnection NetworkSolutions for data-consistency increase
the same traffic (see cache coherency problem later)
Examples: KSR-1, DDM
CC-NUMA
A combination of NUMA and COMAInitially static data distribution, then
dynamic data migration Cache coherency problem is to be
solved COMA and CC-NUMA are used in newer
generation of parallel computersExamples: Convex SPP1000, Stanford
DASH, MIT Alewife