Parallel computing involves performing multiple calculations simultaneously to solve large problems more efficiently. It can be categorized into three types: bit-level, instruction-level, and task parallelism, with various applications such as data mining and medical imaging. The future of parallel computing is geared towards integrating more processors into chips and developing new programming languages to optimize processing capabilities.

1. Parallel Computing and its
applications

2. Parallel Computing
 Parallel computing is a type of computation in which
many calculations or the execution of processes are
carried out simultaneously.[1] Large problems can
often be divided into smaller ones, which can then be
solved at the same time.

3. Types of Parallel Computing:
 There are several Types of Parallel Computing
which are used World wide.
1) Bit-level Parallelism.
2)Instruction level Parallelism.
3)Task Parallelism.

4. Bit Level Parallelism:
 When an 8-bit processor needs to add two 16-bit
integers,it’s to be done in two steps.
 The processor must first add the 8 lower-order
bits from each integer using the standard addition
instruction,
 Then add the 8 higher-order bits using an add-
with-carry instruction and the carry bit from the
lower order addition

5. Instruction Level Parallelism:
 The instructions given to a computer for
processing can be divided into groups, or re-
ordered and then processed without changing the
final result.
 This is known as instruction-level parallelism.
An Example
1. e = a + b
2. f = c + d
3. g = e * f
Here, instruction 3 is dependent on instruction 1
and 2 .
However,instruction 1 and 2 can be independently
processed.

6. Task Parallelism:
 Task Parallelism is a form of parallelization in
which different processors run the program
among different codes of distribution. It is also
called as Function Parallelism.

7. Applications of Parallel
Computing:
 This decomposing technique is used in
application requiring processing of large amount
of data in sophisticated ways. For example;
 Data bases, Data mining.
 Networked videos and Multimedia technologies.
 Medical imaging and diagnosis.
 Advanced graphics and virtual reality.
 Collaborative work environments.

8. Why Use Parallel Computing?
 Main Reasons:
i. Save time and/or money
ii. Solve Large Problem:e.g.- Web search
engines/databases processing millions of
transactions per second.
iii. Provide concurrency
iv. Use of non-local resources
v. Limits to serial computing:
Transmission
speeds - the speed of a serial computer is
directly dependent upon how fast data can
move through hardware, transmission limit of

9. Why Use ParallelComputing ?
 Current computer architectures are increasingly
relying upon hardware level parallelism to
improve performance.
 Multiple execution units
 Pipelined instructions
 Multi-core

10. Difference With Distributed
Computing
 When different processors/computers work on a
single common goal,it is parallel computing.
Eg.Ten men pulling a rope to lift up one
rock,supercomputers implement parallel
computing.
Distributed computing is where several different
computers work separately on a multi-faceted
computing workload.
Eg.Ten men pulling ten ropes to lift ten different
rocks,employees working in an office doing their
own work

11. Approaches To Parallel
Computing
 Flynn’s Taxonomy:
 SISD(Single Instruction Single Data)
 SIMD(Single Instruction Multiple Data)
 MISD(Multiple Instruction Single Data)
 MIMD(Multiple Instruction Multiple Data)

12. Implementation Of Parallel Comput
ing In Software
 When implemented in software(or rather
algorithms), the terminology calls it ‘parallel
programming’
 An algorithm is split into pieces and then
executed, as seen earlier.
 Implementation Of Parallel Computing In
Hardware:
When implemented in hardware, it is called as
‘parallel processing’
Typically,when a chunk of load for execution is
divided for processing by units like
cores,processors,CPUs,etc.

13. Future of Parallel Computing:
 It is expected to lead to other major changes in
the industry. Major companies like INTEL Corp
and Advanced Micro Devices Inc has already
integrated four processors in a single chip. Now
what needed is the simultaneous translation and
break through in technologies, the race for results
in parallel computing is in full swing. Another
great challenge is to write a software program to
divide computer processors into chunks. This
could only be done with the new programming
language to revolutionize the every piece of
software written. Parallel computing may change
the way computer work in the future and how. We