1. By,
S . Adlin Jeena
D. Jagadeeswari

2. Introduction
 collection of program abstractions.
 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
 To limit 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 some issues 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 Message Passing –
 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 is easier 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 Logic Models
 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