Parallel Architectures Based on Parallel Computing , M. J. Quinn
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Parallel ArchitecturesBased on Parallel Computing, M. J. Quinn
Ashok Srinivasanwww.cs.fsu.edu/~asriniva
Florida State University
CIS 5930-04 – Spring 2006: Parallel Computing
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Outline• Interconnection Networks
– Mesh– Binary Tree– Hypercube
• Architectures– Processor Arrays– Multiprocessors
• Centralized Multiprocessors• Distributed Multiprocessors
– Multicomputers• Flynn’s Taxonomy
– SISD– SIMD– MIMD
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Mesh
• 2-D Mesh– N = d2
– Diameter = 2 N0.5
– Bisection width = N0.5
– Edges/node = 4• 2-D Mesh with
wraparound (2-D torus)• 3-D Mesh• 3-D torus
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Binary Tree
• Binary Tree– Diameter = 2 log N– Bisection width = 1– Edges/node = 3
• Fat Tree– Double the number
of edges at each level up the tree
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Hypercube
• Hypercube– N = 2d
– Diameter = log N– Bisection width = N/2– Edges/node = log N
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Processor Arrays
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Centralized Multiprocessors
• Also called SMP/UMA
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Cache Coherence Problem
• Solve through snooping
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Distributed Multiprocessors
• Also called NUMA
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Cache Coherence - Directory Based Solution
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Multicomputers
• Also called a distributed memory system
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Flynn’s Taxonomy
• SISD– Single Instruction, Single Data– Traditional computer
• SIMD– Single Instruction, Multiple Data
• MIMD– Multiple Instruction, Multiple Data