![Page 1: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/1.jpg)
Parallel Algorithms -
Design,Performance &Analysis
Issues
Dr. Seema BawaProfessor & Head, Dept. of Computer Sc. & Engineering
Head, Centre for Information Super Highway
Thapar Uniersity, Patiala
![Page 2: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/2.jpg)
Parallel Processing
• Goal:
– speedup computationally intensive tasks
• Method:
– multiple processes execute sub-tasks
• Requirement:
– sharing information among the processes
![Page 3: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/3.jpg)
Why Parallel Processing?
Computation requirements are ever increasing-visualization, distributed databases, simulations, scientific prediction (earthquake), etc.
Sequential architectures reaching physical limitations (e.g., speed of light, thermodynamics)
![Page 4: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/4.jpg)
Architecture
• Single processor:
– single instruction stream
– single data stream
– von Neumann model
• Multiple processors:
– Flynn’s taxonomy
![Page 5: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/5.jpg)
MISD
SISD
MIMD
SIMD
1 Many
1M
any
Data Streams
Inst
ruct
ion
Str
eam
s
Flynn’s Taxonomy
![Page 6: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/6.jpg)
Single Instruction, Single Data
(SISD)
• Typical Sequential machine
• Can execute one instruction at a time on
one piece of data
• Disregards Pipelining and vector
processing
![Page 7: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/7.jpg)
SISD : A Conventional Computer
Speed is limited by the rate at which computer can
transfer information internally.
ProcessorData Input Data Output
Instru
ction
s
Ex:PC, Macintosh, Workstations
![Page 8: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/8.jpg)
The MISD Architecture
More of an intellectual exercise than a practical
configuration. Few built, but commercially not available
Data
Input
Stream
Data
Output
Stream
Processor
A
Processor
B
Processor
C
Instruction
Stream A
Instruction
Stream B
Instruction Stream C
![Page 9: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/9.jpg)
SIMD Architecture
Ex: CRAY machine vector processing, Thinking machine cm*
Intel MMX (multimedia support)
Ci<= Ai * Bi
Instruction
Stream
Processor
A
Processor
B
Processor
C
Data Input
stream A
Data Input
stream B
Data Input
stream C
Data Output
stream A
Data Output
stream B
Data Output
stream C
![Page 10: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/10.jpg)
Single Instruction, Multiple Data
(SIMD)• Array Processor
– Multiple Memory Units
– Interconnection Network
– One control unit that broadcasts instructions to all processors which execute on their own memory
• Vector Processor– One Memory
– One Processor
– Specialized Functional Unit that executes one instruction on pipeline of data
![Page 11: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/11.jpg)
Array Processor
Control Unit
P P P P
M M M M
Interconnection Network
![Page 12: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/12.jpg)
Vector Processor
M M M M
Interconnection Network
PFunctional Unit
![Page 13: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/13.jpg)
Unlike SISD, MISD, MIMD computer works asynchronously.
Shared memory (tightly coupled) MIMD
Distributed memory (loosely coupled) MIMD
MIMD Architecture
Processor
A
Processor
B
Processor
C
Data Input
stream A
Data Input
stream B
Data Input
stream C
Data Output
stream A
Data Output
stream B
Data Output
stream C
Instruction
Stream AInstruction
Stream B
Instruction
Stream C
![Page 14: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/14.jpg)
Multiple Instruction, Multiple
Data (MIMD)• Complete & Independent Processors
• Shared Memory
– One memory accessible to all processors
– Instructions can execute on any memory location
• Distributed Memory
– Memory distributed with processors
– Processors execute instructions on their data
– Data passed among processors with messages
![Page 15: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/15.jpg)
M
E
M
O
R
Y
B
U
S
Shared Memory MIMD machine
Comm: Source PE writes data to GM & destination retrieves it
Easy to build, conventional OSes of SISD can be easily be ported
Limitation : reliability & expandibility. A memory component or any
processor failure affects the whole system.
Increase of processors leads to memory contention.
Ex. : Silicon graphics supercomputers....
M
E
M
O
R
Y
B
U
S
Global Memory System
Processor
A
Processor
B
Processor
C
M
E
M
O
R
Y
B
U
S
![Page 16: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/16.jpg)
M
E
M
O
R
Y
B
U
S
Distributed Memory MIMD
Communication : IPC (Inter-Process Communication) via High SpeedNetwork.
Network can be configured to ... Tree, Mesh, Cube, etc.
Unlike Shared MIMD
easily/ readily expandable
Highly reliable (any CPU failure does not affect the whole system)
Processor
A
Processor
B
Processor
C
M
E
M
O
R
Y
B
U
S
M
E
M
O
R
Y
B
U
S
Memory
System AMemory
System B
Memory
System C
IPC
channel
IPC
channel
![Page 17: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/17.jpg)
Parallel vs. Distributed
Computing
• Parallel:
– several processing elements concurrently solving
a single problem
• Distributed:
– processing elements do not share memory or
system clock
![Page 18: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/18.jpg)
Parallelization
• Functional & Data parallelism :
– functional: different operations on same
data elements
– data: same operations on different data
elements
![Page 19: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/19.jpg)
Principles of Parallel Algorithm Design
• A sequential algorithm is essentially a recipe or a
sequence of basic steps for solving a given problem.
• A parallel algorithm is a recipe that tells us how to
solve a given problem using multiple processors.
• A parallel algorithm has the added dimension of
concurrency and the algorithm designer must specify
sets of steps that can be executed in parallel.
![Page 20: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/20.jpg)
Specifying a Parallel Algorithm includes:
• Identifying portions of the work that can be performed concurrently.
Mapping the concurrent pieces of work onto multiple processes running in parallel.
• Distributing the input, output, and intermediate data associated with the program.
• Managing accesses to data shared by multiple processors.
![Page 21: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/21.jpg)
Specifying a Parallel Algorithm includes:
• Synchronizing the processors at various
stages of the parallel program execution.
![Page 22: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/22.jpg)
Parallel Algorithm Design
Two key steps in the design of parallel
algorithms :
Dividing a computation into smaller
computations
Assigning them to different processors
![Page 23: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/23.jpg)
Decomposition & Tasks• Decomposition : The processes of
dividing a computation into smaller parts, some or all of which may potentially be executed in parallel.
• Tasks :are programmer-defined units of computation into which the main computation is subdivided by means of decomposition
![Page 24: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/24.jpg)
Decomposition & Tasks
(Contd..)
Example :- Matrix multiplication
![Page 25: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/25.jpg)
Decomposition & Tasks
(Contd..)
![Page 26: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/26.jpg)
Task Dependency Graphs
Example:Matrix Multiplication
![Page 27: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/27.jpg)
Example: Database Query
Processing
QUERY
MODEL=”Accord” AND YEAR=”1999”
(COLOR=”Green”ORCOLOR=”Black”)
![Page 28: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/28.jpg)
Example: Database Query
Processing
![Page 29: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/29.jpg)
Granularity & Concurrency
• The number and size of tasks into which
a problem is decomposed determines
the granularity of the decomposition.
TYPES:
o FINE GRAINED
o COARSE GRAINED
![Page 30: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/30.jpg)
Processes and Mapping
• Process is an abstract entity that uses the
code and data corresponding to a task to
produce the output of that task within a finite
amount of time after the task is activated by
the parallel program
• The mechanism by which tasks are assigned
to processes for execution is called mapping.
![Page 31: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/31.jpg)
Decomposition Techniques
Recursive decomposition
Data decomposition
Exploratory decomposition
Speculative decomposition
![Page 32: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/32.jpg)
Characteristics of Tasks
Task Generation
Task Sizes
Size of Data Associated with Tasks
![Page 33: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/33.jpg)
Characteristics of Inter-task
Interactions
• STATIC
• DYNAMIC
![Page 34: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/34.jpg)
Mapping Techniques for Load
Balancing
• STATIC
• DYNAMIC
![Page 35: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/35.jpg)
Performance Metrics for
Parallel Systems
Execution Time
The parallel run time is the time that
elapses from the moment that a parallel
computation starts to the moment that
the last processor finishes execution.
Tp = Ts / p.
![Page 36: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/36.jpg)
Total Parallel Overhead
Total overhead or overhead function of a
parallel system as the total time collectively
spent by all the processors over and above that
required by the fastest known sequential
algorithm for solving the same problem on a
single processor.
![Page 37: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/37.jpg)
Speedup
It is defined as the ratio of the time
taken to solve a problem on a single
processor to the time required to solve
the same problem on a parallel computer
with p identical processors.
![Page 38: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/38.jpg)
Efficiency
It is defined as the ratio of speedup to the
number
of processors. In an ideal parallel system,
speedup is equal to p and efficiency is equal
to one.
![Page 39: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/39.jpg)
Efficiency
• A parallel algorithm is efficient iff– it is fast (e.g. polynomial time) and
– the product of the parallel time and number of processors is close to the time of at the best know sequential algorithm
T sequential T parallel N processors
• A parallel algorithms is optimal iff this product is of the same order as the best known sequential time
![Page 40: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/40.jpg)
COST
The COST of solving a problem on a
parallel system as the product of parallel run
time and the number of processors used.
A parallel system is said to be cost-optimal if the cost of
solving a problem on a parallel computer is proportional
to the execution time of the fastest-known sequential
algorithm on a single processor
![Page 41: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/41.jpg)
EXAMPLE ILLUSTRATING
THE PERFORMANCE
METRICS
![Page 42: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/42.jpg)
EXAMPLE ILLUSTRATING
THE PERFORMANCE
METRICS
![Page 43: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/43.jpg)
EXAMPLE ILLUSTRATING
THE PERFORMANCE
METRICS
![Page 44: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/44.jpg)
Scalability of Parallel Systems
The scalability of a parallel system is a
measure of its capacity to increase
speedup
in proportion to the number of
processors. It reflects a parallel system’s
ability to utilize increasing processing
resources effectively
![Page 45: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/45.jpg)
Parallel Algorithm Analysis
Computation complexity Tcomp(n,p)
Communication complexity Tcomm(n,p)
![Page 46: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/46.jpg)
ALGORITHM ANALYSIS
TECNIQUES
The essence of parallel programming lies in being able to partition the task gracefully.
DIVIDE AND CONQUER
Steps
1. Divide an instance of a problem into one or more smaller instances.
2. Conquer each of the smaller instances.Unless a smaller instance is sufficiently small,use recursion to do this.
3. If necessary, combine the solutions to thesmaller instances to obtain the solution to theoriginal instance.
![Page 47: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/47.jpg)
DYNAMIC PROGRAMMING:
STEPS:-
1. Establish a recursive property that givesthe Solution to an instance of theproblem.
2.Solve an instance of the problem in abottom-up fashion by solving smallerinstances first.
![Page 48: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/48.jpg)
EXAMPLE ILLUSTRATING
THE DIFFERENCE
BINOMIAL COEFFICIENT
The binomial coefficient is given by:
[n] = { [n-1] + [n-1] }
[k] { [k-1]+ [k] } 0<k<n
![Page 49: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/49.jpg)
Algorithm ----Binomial Coefficient
using Divide and Conquer
• function bin(n, k : integer):integer;
• begin
• if k=0 or n=k then
• bin:=1
• else
• bin:=bin(n-1,k-1)+bin(n-1,k)
• end
• end;
![Page 50: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/50.jpg)
Algorithm --Dynamic Programming
• function bin2(n,k :integer):integer;
• var
• I,j:index;
• B:array[0..n,0..k] of integer;
• Begin
• For i:= 0 to n do
• For j:=0 to minimum(i,k) do
• If j=0 or j=I then
• B[I,j]:=i
• Else
• B[I,j]:= b[i-1,j-1] + b[i-1,j]
• End
• End;
![Page 51: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/51.jpg)
THE GREEDY APPROACH
STEPS:
A selection procedure chooses the nextitem to add to the set. The selection isperformed according to the greedy criterionthat satisfies some locally optimalconsideration at the time.
A feasibility check determines if the newset is feasible by checking whether it ispossible to complete this set in such a wayas to give a solution to the instance.
![Page 52: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/52.jpg)
THE GREEDY APPROACH
• A solution check determines whether the new set constitutes a solution to the instance.
• A greedy algorithm works in phases: At each phase:
– You take the best you can get right now, without regard for future consequences
– You hope that by choosing a local optimumat each step, you will end up at a globaloptimum
![Page 53: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/53.jpg)
BACKTRACKING
• Backtracking is used to solve problems
in which a sequence of objects is
chosen from a specified set so that the
sequence satisfies some criteria
• EXAMPLE: n-Queens Problem
![Page 54: Parallel Algorithms - Design,Performance &Analysis Issues Algorithms.pdf · •A parallel algorithms is optimal iff this product is of the same order as the best known sequential](https://reader034.vdocuments.us/reader034/viewer/2022042804/5f58f5d6b5d5046c7832c993/html5/thumbnails/54.jpg)
BACKTRACKING
STEPS:-
– Tests to see if a solution has been found,
and if so, returns it; otherwise
– For each choice that can be made at this
point,
• Make that choice
• Recur
• If the recursion returns a solution, return it
– If no choices remain, return failure