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
