Parallel Computing In India
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We all know that the silicon based chips are reaching a physical limit in processing speed, as they are constrained by the speed of electricity, light and certain thermodynamic laws. A viable solution to overcome this limitation is to connect multiple processors working in coordination with each other to solve grand challenge problems. Hence, high performance computing requires the use of Massively Parallel Processing (MPP) systems containing thousands of power full CPUs.

Processing of multiple tasks simultaneously on multiple processors is called Parallel Processing. The parallel program consists of multiple active processes simultaneously solving a given problem. A given task is divided into multiple sub tasks using divide-and-conquer technique and each one of them are processed on different CPUs. Programming on multiprocessor system using divide-and-conquer technique is called Parallel Processing.
The development of parallel processing is being influenced by many factors. The prominent among them include the following:

1.Computational requirements are ever increasing, both in the area of scientific and business computing. The technical computing problems, which require high-speed computational power, are related to life sciences, aerospace, geographical information systems, mechanical design and analysis, etc.
2.Sequential architectures reaching physical limitation, as they are constrained by the speed of light and thermodynamics laws. Speed with which sequential CPUs can operate is reaching saturation point ( no more vertical growth ), and hence an alternative way to get high computational speed is to connect multiple CPUs ( opportunity for horizontal growth ).

3.Hardware improvements in pipelining, super scalar, etc, are non scalable and requires sophisticated compiler technology. Developing such compiler technology is difficult task.

4.Vector processing works well for certain kind of problems. It is suitable for only scientific problems ( involving lots of matrix operations). It is not useful to other areas such as database.

5.The technology of parallel processing is mature and can be exploited commercially, there is already significant research and development work on development tools and environment is achieved.

6.Significant development in networking technology is paving a way for heterogeneous computing.
India launched a major initiative in parallel computing in 1988. There are five or six independent project and implimentations to construct parallel processing systems. This was motivated by the need for advanced computing, a vision of developing its own technology, and difficulties (political and economic) obtaining commercial products.

The creation of the Center for Development of Advanced Computing (C-DAC) and concurrently other efforts at National Aerospace Laboratory (NAL), Bangalore, Advanced Numerical Research & Analysis Group (ANURAG), Hyderabad, Bhabha Atomic Research Center (BARC), Bombay, Center for Development of Telematics (C-DOT), Bangalore, marked the beginning of high performance computing in India.
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26-11-2010, 04:59 PM

Parallel Computing

What is computing
types of computing
What is serial computing
Draw backs of serial computing
What is parallel computing
Advantages of parallel computing over serial
Applications of parallel computing

types of computing
1) Serial computing

2) Parallel Computing

Draw Backs of Serial computing

Low Speed of processing
Time consuming
Not efficient & Not convenient computing

Advantages of Parallel computing over serial

High Speed of proceesing
Less time required for computing than serial computing
More efficient & convenient computing
Most of the Systems use the parallel computing

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.pptx   parallel com.pptx (Size: 249.71 KB / Downloads: 163)
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16-03-2011, 11:58 AM

Presented by
Justin Reschke

.ppt   Parallel_Computing-1.ppt (Size: 210 KB / Downloads: 243)
Parallel Computing
Concepts and Terminology:
What is Parallel Computing?

Traditionally software has been written for serial computation.
Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem.
Concepts and Terminology:
Why Use Parallel Computing?

Saves time – wall clock time
Cost savings
Overcoming memory constraints
It’s the future of computing
Concepts and Terminology:
Flynn’s Classical Taxonomy

Distinguishes multi-processor architecture by instruction and data
SISD – Single Instruction, Single Data
SIMD – Single Instruction, Multiple Data
MISD – Multiple Instruction, Single Data
MIMD – Multiple Instruction, Multiple Data
Flynn’s Classical Taxonomy:
Only one instruction and data stream is acted on during any one clock cycle
Flynn’s Classical Taxonomy:
All processing units execute the same instruction at any given clock cycle.
Each processing unit operates on a different data element.
Flynn’s Classical Taxonomy:
Different instructions operated on a single data element.
Very few practical uses for this type of classification.
Example: Multiple cryptography algorithms attempting to crack a single coded message.
Flynn’s Classical Taxonomy:
Can execute different instructions on different data elements.
Most common type of parallel computer.
Concepts and Terminology:
General Terminology

Task – A logically discrete section of computational work
Parallel Task – Task that can be executed by multiple processors safely
Communications – Data exchange between parallel tasks
Synchronization – The coordination of parallel tasks in real time
Concepts and Terminology:
More Terminology
Granularity – The ratio of computation to communication
 Coarse – High computation, low communication
 Fine – Low computation, high communication
Parallel Overhead
 Synchronizations
 Data Communications
 Overhead imposed by compilers, libraries, tools, operating systems, etc.
Parallel Computer Memory Architectures:
Shared Memory Architecture

All processors access all memory as a single global address space.
Data sharing is fast.
Lack of scalability between memory and CPUs
Parallel Computer Memory Architectures:
Distributed Memory

Each processor has its own memory.
Is scalable, no overhead for cache coherency.
Programmer is responsible for many details of communication between processors.
Parallel Programming Models
Exist as an abstraction above hardware and memory architectures
 Shared Memory
 Threads
 Messaging Passing
 Data Parallel
Parallel Programming Models:
Shared Memory Model

Appears to the user as a single shared memory, despite hardware implementations.
Locks and semaphores may be used to control shared memory access.
Program development can be simplified since there is no need to explicitly specify communication between tasks.
Parallel Programming Models:
Threads Model

A single process may have multiple, concurrent execution paths.
Typically used with a shared memory architecture.
Programmer is responsible for determining all parallelism.
Parallel Programming Models:
Message Passing Model

Tasks exchange data by sending and receiving messages.
Typically used with distributed memory architectures.
Data transfer requires cooperative operations to be performed by each process. Ex.- a send operation must have a receive operation.
MPI (Message Passing Interface) is the interface standard for message passing.
Parallel Programming Models:
Data Parallel Model

Tasks performing the same operations on a set of data. Each task working on a separate piece of the set.
Works well with either shared memory or distributed memory architectures.
Designing Parallel Programs:
Automatic Parallelization

 Compiler analyzes code and identifies opportunities for parallelism
 Analysis includes attempting to compute whether or not the parallelism actually improves performance.
 Loops are the most frequent target for automatic parallelism.
Designing Parallel Programs:
Manual Parallelization

Understand the problem
 A Parallelizable Problem:
Calculate the potential energy for each of several thousand independent conformations of a molecule. When done find the minimum energy conformation.
 A Non-Parallelizable Problem:
The Fibonacci Series
 All calculations are dependent
Designing Parallel Programs:
Domain Decomposition

Each task handles a portion of the data set.
Designing Parallel Programs:
Functional Decomposition
Each task performs a function of the overall work
Parallel Algorithm Examples:
Array Processing

Serial Solution
 Perform a function on a 2D array.
 Single processor iterates through each element in the array
Possible Parallel Solution
 Assign each processor a partition of the array.
 Each process iterates through its own partition.
Parallel Algorithm Examples:
Odd-Even Transposition Sort

Basic idea is bubble sort, but concurrently comparing odd indexed elements with an adjacent element, then even indexed elements.
If there are n elements in an array and there are n/2 processors. The algorithm is effectively O(n)!
Initial array:
 6, 5, 4, 3, 2, 1, 0
6, 4, 5, 2, 3, 0, 1
4, 6, 2, 5, 0, 3, 1
4, 2, 6, 0, 5, 1, 3
2, 4, 0, 6, 1, 5, 3
2, 0, 4, 1, 6, 3, 5
0, 2, 1, 4, 3, 6, 5
0, 1, 2, 3, 4, 5, 6
Worst case scenario.
Phase 1
Phase 2
Phase 1
Phase 2
Phase 1
Phase 2
Phase 1
Other Parallelizable Problems
The n-body problem
Floyd’s Algorithm
 Serial: O(n^3), Parallel: O(n log p)
Game Trees
Divide and Conquer Algorithms
Parallel computing is fast.
There are many different approaches and models of parallel computing.
Parallel computing is the future of computing.
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.doc   PARALLEL.doc (Size: 88 KB / Downloads: 23)


This presentation covers some of the advanced concepts of the parallel computing memory architecture and implementations of these architectures. Beginning with a brief overview and some concepts and terminology associated with parallel computing, this paper consists of advanced topics like hybrid shared -distributed memory .This paper also contains the terminologies used in parallel computing. Designing and developing parallel programs has characteristically been a very manual process but in this paper it has been explained how automatically it has been made. Designing of parallel programs have been discussed using domain decomposition and functional decomposition methods .These topics are followed by a discussion on a number of issues related to designing parallel programs. The last portion of the presentation is spent examining how to parallelize several different types of serial programs.


Traditionally, software has been written for serial computation to be run on a single computer having a single Central Processing Unit (CPU) A problem is broken into a discrete series of instructions. Instructions are executed one after another. Only one instruction may execute at any moment in time. In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem. To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently. Each part is further broken down to a series of instructions. Instructions from each part execute simultaneously on different CPUs.


The primary reasons for using parallel computing: Save time - wall clock time, Solve larger problems ,Provide concurrency (do multiple things at the same time) .Other reasons might include: Taking advantage of non-local resources - using available compute resources on a wide area network, or even the Internet when local compute resources are scarce. Cost savings - using multiple "cheap" computing resources instead of paying for time on a supercomputer. Overcoming memory constraints - single computers have very finite memory resources. For large problems, using the memories of multiple computers may overcome this obstacle. Limits to serial computing - both physical and practical reasons pose significant constraints to simply building ever faster parallel computers: The future: during the past 10 years


Manual Vs Automatic parallelization

Designing and developing parallel programs has characteristically been a very manual process. The programmer is typically responsible for both identifying and actually implementing parallelism. Very often, manually developing parallel codes is a time consuming, complex, error-prone and iterative process. For a number of years now, various tools have been available to assist the programmer with converting serial programs into parallel programs. The most common type of tool used to automatically parallelize a serial program is a parallelizing compiler or pre-processor.


However, there are several important caveats that apply to automatic parallelization: Wrong results may be produced, Performance may actually degrade, Much less flexible than manual parallelization, Limited to a subset (mostly loops) of code .May actually not parallelize code if the analysis suggests there are inhibitors or the code is too complex.

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