its just about technology which improves our parallelism.

2.  A programming model is a collection of program
abstraction providing a programmer a simplified
and transparent view of computer H/W and S/W.
 Parallel programming model is designed for vector
computers.
 Fundamental issues in parallel programming.
 Creation, suspension, reactivation, termination.

3. Five model are designed that exploits
parallelism-:
 Shared-variable model.
 Message-passing model.
 Data parallel model.
 Object oriented model.
 Functional and logic model.

4.  In shared variable model parallelism depends on
how IPC is implemented.
 IPC implemented in parallel programming by two
ways.
 IPC using shared variable.
 IPC using message passing.

5.  IPC with shared variable
 IPC with message passing

6.  Critical section.
 Memory consistency.
 Atomicity with memory operation.
 Fast synchronization.
 Shared data structure.

7.  Two process communicate with each other by
passing message through a network.
 Delay caused by message passing is much longer
than shared variable model in a same memory.
 Two message passing approach are introduced here.

8. Synchronous message passing-:
 Its synchronizes the sender and receiver process
with time and space just like telephone call.
 No shared memory.
 No need of mutual exclusion.
 No buffer are used in communication channel.
 It can be blocked by channel being busy.

9. Asynchronous message passing-:
 Does not need to synchronize the sender and
receiver in time and space.
 Non blocking can be achieved.
 Buffer are used to hold the message along the path
of connecting channel.
 Message passing programming is gradually
changing, once the virtual memory from all nodes
are combined.

10.  It require the use of pre-distributed data set.
 Interconnected data structure are also needed to
facilitate data exchange operation.
 It emphasizes local computation and data routing
operation such as permutation, replication, reduction
and parallel prefix.
 It can be implemented on either SIMD or SPMD
multicomputer, depending on the grain size of
program.

11.  Object are created and manipulated dynamically.
 Processing is performed using object.
 Concurrent programming model are built up from
low level object such as processes, queue and
semaphore.
 C-OOP achieve parallelism using three methods.

12.  Pipeline concurrency.
 Divide and conquer concurrency.
 Co-operating problem solving.

13.  Two language-oriented programming for parallel
processing are purposed.
 Functional programming model such as LISP,
SISAL, Strand 88.
 Logic programming model as prolog.
 Based on predicate logic, logic programming is
suitable for solving large database queries.

14.  Language feature for parallel programming into six
categories according to functionality.
Optimization features
 Used for program restructuring and compilation
directives.
 Sequentially coded program into parallel code.
 Automated parallelization.
 Semi-automated parallelization.

15. Availability feature
 Its use to enhance the user- friendliness.
 Make language portable to large class of parallel
computers.
 Scalability.
 Compatibility.
 Portability.

16. Synchronization/ communication feature
 Shared variable for IPC.
 Single assignment language.
 Send/receive for message passing.
 Logical shared memory such as the row space in
Linda.
 Remote procedure call.
 Data flow languages such as id.
.

17. Control of parallelism
 Coarse, medium or fine grain.
 Explicit versus implicit parallelism.
 Loop parallelism in iteration.
 Shared task queue.
 Divide and conquer paradigm.
 Shared abstract data type.

18. Data parallelism feature
 It specified how data are accessed and distributed
 Runtime automatic decomposition.
 Mapping specification.
 Virtual processor support.
 Direct access to shared data.

19. Process management features
 These feature are needed to support the efficient
creation of parallel processes.
 Implementation of multithreading or multitasking.
 Dynamic process creation at runtime.
 Automatic load balancing.
 Light weight processes.

20.  Special language construct and data array
expression for exploiting parallelism in program.
 First is FORTRAN 90 array notation.
 Parallel flow control is achieve using do across and
do all type of keyword which is use in the
FORTRAN 90.
 Same we also use FORK and JOIN method.

21.  The role of compiler to remove the burden of
program optimization and code generation.
 A parallelizing compiler consist of the three major
phases.
 Flow analysis.
 Optimization.
 Code generation.

22. “Compilation phases in parallel code generation”

23. THANK YOU