Memory Classification, Networks

Classification of Parallel Computers

graph TD
    PS[Parallel System] --> SIMD
    PS --> MIMD
    MIMD --> DM[Distributed Memory]
    DM --> MPP
    DM --> NOW
    DM --> Clusters
    MIMD --> SM[Shared Memory]
    SM --> UMA
    SM --> NUMA
    SM --> COMA
    NUMA --> ccNUMA
    NUMA --> ncNUMA

Classification of Memory

Distributed Memory

• Architecture
• Programming Model: Message passing.
• Types
  • MPP Massive Parallel System
    • Specialized system
    • Unified Operating system
    • Most Super computers run this
  • NOW Network of Workstations
    • Loosely connected group of individual computers with “standard” network
  • Cluster
    • Network of Computers with high performance network.
    • Separate instance of operating system.

Shared Memory

• Programming model: Shared variables.
• Simultaneous access to the same variable is not allowed.
• Types
  • NUMA
    • Each Processor has has a local memory which can be accessed by other processors.
    • DIR → Directory. Stores information regarding the files in cache. Like a registry.
    • During memory access the DIR is read to know if the file should be accessed from the cache or from memory.
  • ccNUMA: Cache Coherent non-Uniform Memory Access
  • nccNUMA: non-ccNUMA
  • COMA: Cache only memory access
    • TAG → It is an identifier for each Datum.
    • Datum migrates to where it is needed, hence you need a TAG to identify the data.
    • Here the memory is accessed through the cache only.
    • This means that this if the same data is being used, the access operation is much faster.

Understanding the architecture is essential to writing efficient programs.

• Example:
  • Caches are accessed in form of cache lines of fixed size - like a bus transporting people, it has the same energy is used to transport 1 person and 100 passengers.
  • To avoid Cache misses, data should be processed continuously. (cache miss: it occurs when the program looks at the DIR and doesn’t find what’s required hence it searches for it in memory which is expensive)
• Code example

  for (int i = 0; i < x; i++) {
      for (int j = 0; j < y; j++) {
          data1[i + x * j] = func(i, j); // Data 1 
          data2[j + y * i] = func(i, j); // Data 2
      }
  }

  • data2 is faster. Because the loop is inside (continuous j).
  • data1 has to come out of the inner loop. Hence it is slower.

Networks

• Distance between nodes
  • Shortest path in # edges between two nodes. (#→ number of something )
• Degree
  • Largest number of edges at a node.
• Diameter
  • Largest distance between two nodes.

Examples

• Fully connected Network
• Linear network
• Ring network or 1D Torus
• dD Torus (d→ dimension)
• D-Dimensional hypercube
  • Nodes $N=2^d$
  • Node connected with $d$ neighbours.
  • indexed by $d$ bits.
  • Diameter = $d$ = ${\log }_{2}N$
  • Degree = $d$ = ${\log }_{2}N$
  • Node index differ from neighbor’s index by 1 bit.
  • Patch through the network changes bit at a time
    • 000→001→101→111
  • Distance between nodes = # Bits different in the index
  • Hamming Distance: number of non-matching bits two words of same length.(How many bits have to be changed to go from one data to another data.)