Unit 2.2 Parallel programming architecture .pptx

Unit 2.2 Parallel programming architecture .pptx

• 1.
  Parallel Programming Models
• 2.
  Introduction ⚫collection of programabstractions. ⚫designed for multiprocessors, multicomputer or vector/SIMD computers ⚫Five models: ✔Shared-Variable Model ✔Message-Passing Model ✔Data-Parallel Model ✔Object-oriented Model ✔Functional and Logic Models
• 3.
  Shared-Variable Model ✔ Tolimit the scope and rights, the process address space may be shared or restricted. Mechanisms for IPC: 1. IPC using shared variable: 2. IPC using message passing: Shared Variables in a common memory Process A Process B Process C Process D Process E
• 4.
  Following are someissues of Shared-variable Model: ▪ Shared-Variable communication: ▪ Critical Section(CS): ⚫ code segment accessing shared variables. ⚫ Requirements are – ⚫ Mutual exclusion ⚫ No deadlock in waiting ⚫ Non preemption ⚫ Eventual entry ▪ Protected Access: based on CS value ⚫ Multiprogramming ⚫ Multiprocessing – two types: ⚫ MIMD mode ⚫ MPMD mode ⚫ Multitasking ⚫ Multithreading
• 5.
  ▪Partitioning and Replication: ▪Program partitioning is a technique for decomposing a large program and data set into many small pieces for parallel execution by multiple processors. ▪ Program replication is referred to duplication of the same program code for parallel execution on multiple processor over different data sets. ▪Scheduling and Synchronization: ▪ Scheduling of divided program modules on parallel processor ▪ Two types are : ▪Static scheduling ▪Dynamic scheduling ▪Cache Coherence and Protection: If the value is returned on a read instruction is always the value written by the latest write instruction on the same memory location is called coherent.
• 6.
  Message-Passing Model ⚫Synchronous MessagePassing – ⚫ It is must synchronize the sender process and the receiver process in time and space ⚫Asynchronous Message Passing – ⚫It does not require message sending and receiving be synchronized in time and space ⚫Non blocking can be achieved ⚫Distributing the computations: ⚫Subprogram level is handled rather than at the instructional or fine grain process level in a tightly coupled multiprocessor
• 7.
  Data-Parallel Model ⚫It iseasier to write and to debug because parallelism is explicitly handled by hardware synchronization and flow control. ⚫It requires the use of pre-distributed data sets ⚫Synchronization is done at compile time rather than run time. ⚫the following are some issued handled ⚫Data Parallelism- ⚫Array Language Extensions ⚫Compiler support
• 8.
  Object-Oriented Model ⚫Concurrent OOP– 3 application demands ⚫There is increased use of interacting processes by individual users ⚫Workstation networks have become a cost-effective mechanism ⚫Multiprocessor technology in several variants has advanced to the point of providing supercomputing power ⚫An actor model ⚫It is presented as one framework for COOP ⚫They are self-contained , interactive, independent components of a computing system. ⚫Basic primitives are :create to , send to, become ⚫Parallelism in COOP: ⚫3 patterns- 1. pipeline concurrency 2.divide and conquer currency 3.cooperative problem solving
• 9.
  Functional and LogicModels ⚫Two types of language oriented programming models are ⚫Functional programming model ⚫It emphasizes functionality of a program ⚫No concepts of storage, assignment and branching ⚫All single-assignment and dataflow languages are functional in nature ⚫Some e.g. are Lisp, SISAL and strand 88 ⚫Logic programming model ⚫Based on logic ,logic programming tat suitable for dealing with large database. ⚫Some e.g. are ⚫ concurrent Prolog - ⚫Concurrent Parlog