april 26, 20051 cse8380 parallel and distributed processing presentation hong yue department of...

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CSE8380 Parallel and Distributed CSE8380 Parallel and Distributed ProcessingProcessing

PresentationPresentation

CSE8380 Parallel and Distributed CSE8380 Parallel and Distributed ProcessingProcessing

PresentationPresentation Hong YueHong Yue

Department of Computer Science & EngineeringDepartment of Computer Science & EngineeringSouthern Methodist UniversitySouthern Methodist University

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Parallel Processing Parallel Processing MultianalysisMultianalysis

--- --- Compare Parallel Processing with Compare Parallel Processing with

Sequential ProcessingSequential Processing

Parallel Processing Parallel Processing MultianalysisMultianalysis

--- --- Compare Parallel Processing with Compare Parallel Processing with

Sequential ProcessingSequential Processing

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Why did I select this topic?Why did I select this topic?Why did I select this topic?Why did I select this topic?

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Outline Definition Characteristics of Parallel Processing and

Sequential Processing Implementation of Parallel Processing and

Sequential Processing Performance of Parallel Processing and

Sequential Processing Parallel Processing Evaluation Major Application of parallel processing

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Definition Parallel Processing Definition

Parallel Processing refers to the simultaneous use of multiple processors to execute the same task in order to obtain faster results. These processors either communicate each other to solve a problem or work completely independent, under the control of another processor which divides the problem into a number of parts to other processors and collects results from them.

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Definition .2

Sequential Processing Definition

Sequential processing refers to a computer architecture in which a single processor carries out a single task by series of operations in sequence. It is also called serial processing.

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Characteristics of Parallel Processing and Sequential

Processing

Characteristics of Parallel Processing

● Each processor can perform tasks

concurrently.

● Tasks may need to be synchronized.

● Processors usually share resources, such as data, disks, and other devices.

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Characteristics of Parallel Processing and Sequential

Processing .2

Characteristics of Sequential Processing

● Only one single processor performs task.

● The single processor performs a single

task.

● Task is executed in sequence.

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Implementation of parallel processing and sequential

processing

Executing single task

In sequential processing, the task is executed as a single large task.

In parallel processing, the task is divided into multiple smaller tasks, and each component task is executed on a separate processor.

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processing.2

Total Elapsed Time

Processor 1

Task (runtime)

Figure 1 Sequential Processing of a Large Task

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processing .3

Total Elapsed Time

Processor 1

Processor 2

Processor 3

Processor 4

Processor 5

Processor 6

Processor 7

Figure 2 Parallel Processing: Executing Component Tasks in Parallel

Component task (runtime)

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processing.4

Executing multiple independent task

● In sequential processing, independent tasks compete for a single resource. Only task 1 runs without having to wait. Task 2 must wait until task 1 has completed; task 3 must wait until tasks 1 and 2 have completed, and so on.

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processing .5

Executing multiple independent task

● By contrast, in parallel processing, for example, a parallel server on a symmetric multiprocessor, more CPU power is assigned to the tasks. Each independent task executes immediately on its own processor: no wait time is involved.

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processing .6

Total Elapsed Time

Processor 1

Processor 2

Processor 3

Processor 4

Processor 5

Processor 6

Processor 7

Figure 3 Sequential Processing of Multiple Independent Tasks

Task (runtime)

Wait

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processing .7

Total Elapsed Time

Figure 4 Parallel Processing: Executing Independent Tasks in Parallel

Task (runtime)

Processor 1

Processor 2

Processor 3

Processor 4

Processor 5

Processor 6

Processor 7

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Performance of parallel processing and sequential

processing

Sequential Processing Performance

● Take long time to execute task. ● Can’t handle too large task. ● Can’t handle large loads well. ● Return is diminishing. ● More increasingly expensive to make a

single processor faster.

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processing .2

Solution:

using parallel processing - use lots of relatively fast, cheap processors in parallel.

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processing .3

Parallel Processing Performance ● Cheaper, in terms of price and performance. ● Faster than equivalently expensive uniprocessor

machines. ● Scalable. The performance of a particular program

may be improved by execution on a large machine.

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processing .4

Parallel Processing Performance

● Reliable. In theory if processors fail we can simply use others.

● Can handle bigger problems.

● Communicate with each other readily, important in calculations.

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Parallel Processing Evaluation

Several ways to evaluate the parallel processing performance:

● Scale-up ● Speedup ● Efficiency ● Overall solution time ● Price/performance

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Parallel Processing Evaluation .2

Scale-up

Scale-up is enhanced throughput, refers to the ability of a system n times larger to perform an n times larger job, in the same time period as the original system. With added hardware, a formula for scale-up holds the time constant, and measures the increased size of the job which can be done.

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Sequential System:

Hardware

Time

100% Task

Parallel System:

Hardware

HardwareTime

Time

200% Task

Figure 5 Scale-up

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Scale-up measurement formula:

Scale-up =

Transaction volume of multiprocessors

Transaction volume of uniprocessor

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For example, if the uniprocessor system can process 100 transactions in a given amount of time, and the parallel system can process 200 transactions in this amount of time, then the value of scale-up would be equal to 200/100 = 2.

Value 2 indicates the ideal of linear scale-up: when twice as much, hardware can process twice the data volume in the same amount of time.

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Speedup

Speedup, the improved response time, defined as the time it takes a program to execute in sequential (with one processor) divided by the time it takes to execute in parallel (with many processors). It can be achieved by two ways: breaking up a large task into many small fragments and reducing wait time.

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Sequential System:

Hardware

Time

100% Task

Parallel System:

Hardware

Hardware

Time

Time

50% Task

50% Task

Figure 6 Speedup

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Speedup measurement formula:

Elapsed time of a uniprocessorSpeedup = Elapsed time of the multiprocessors

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For example, if the uniprocessor took 40 seconds to perform a task, and two parallel systems took 20 seconds, then the value of speedup = 40 / 20 = 2.

Value 2 indicates the ideal of linear speedup: when twice as much, hardware can perform the same task in half the time.

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Workload Scale-up Speedup

OLTP Yes No

DSS Yes Yes

Batch (Mixed) Yes Possible

Parallel Query Yes Yes

Table 1 Scale-up and Speedup for Different Types of Workload

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Figure 7 Linear and actual speedup

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Amdahl’s Law Amdahl's Law is a law governing the

speedup of using parallel processors on a problem, versus using only one sequential processor. Amdahl’s law attempts to give a maximum bound for speedup from the nature of the algorithm:

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Amdahl’s Law

S: purely sequential part P: parallel partS + P = 1 (for simplicity)

Maximum speedup =

S + P

S +

P

n

=

1

S +

P

n

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Figure 8 Example speedup: Amdahl & Gustafson

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Gustafson’s Law If the size of a problem is scaled up as the

number of processors increases, speedup very close to the ideal speedup is possible.

That is, a problem size is virtually never

independent of the number of processors.

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Gustafson’s Law

Maximum speedup =

S + (P * n)

S + P

= n + (1 - n) * S

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Efficiency

The relative efficiency can be a useful measure as to what percentage of a processor’s time is being spent in useful computation.

Efficiency =

Speedup * 100

Number of processors

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Figure 9 Optimum efficiency & actual efficiency

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Figure 10 Optimum number of processors in actual speedup

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Problems in Parallel Processing

Parallel processing is like a dog’s walking on its hind legs. It is not done well, but you are surprised to find it done at all.

----Steve Fiddes (University of Bristol)

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● Its software is heavily platform-dependent and has to be written for a specific machine.

● It also requires a different, more difficult

method of programming, since the software needs to appropriately, through algorithms, divide the work across each processor.

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● There isn't a wide array of shrink-wrapped software ready for use with parallel machines.

● Parallelization is problem-dependent and cannot be automated.

● Speedup is not guaranteed.

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Solution 1:

● Decide which architecture is most appropriate for a given application.

The characteristics of application should drive decision as to how it should be parallelized; the form of the parallelization should then determine what kind of underlying system, both hardware and software, is best suited to running your parallelized application.

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Solution 2:

● Clustering

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Major Applications of parallel processing

Clustering

● Clustering is a form of parallel processing that takes a group of workstations connected together in a local-area network and applies middleware to make them act like a parallel machine.

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Major Applications of parallel processing .2

Clustering

Clustering is a form of parallel processing that takes a group of workstations connected together in a local-area network and applies middleware to make them act like a parallel machine.

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Clustering ● Parallel processing using Linux Clusters can yield

supercomputer performance for some programs that perform complex computations or operate on large data sets. And it can accomplish this task by using cheap hardware.

● Clustering can be used at night when networks are idle, it is an inexpensive alternative to parallel-processing machines.

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Clustering can work with two separate but similar implementations:

● A Parallel Virtual Machine (PVM), is an environment that allows messages to pass between computers as it would in an actual parallel machine.

● A Message-Passing Interface (MPI), allows programmers to create message-passing parallel applications, using parallel input/output functions and dynamic process management.

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Reference

Andrew Boucher, “Parallel Machines”

Stephane vialle, “Past and Future Parallelism Challenges to Encompass sequential Processor evolution”

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The end

Thank you!