1. Parallel vs Sequential Algorithms

2. Design of efficient algorithms
A parallel computer is of little use unless efficient parallel algorithms are available.
The issue in designing parallel algorithms are very different from those in designing their sequential counterparts.
A significant amount of work is being done to develop efficient parallel algorithms for a variety of parallel architectures.

3. Processor Trends Moore’s Law Parallelization within processors
performance doubles every 18 months
Parallelization within processors
pipelining
multiple pipelines

4. Why Parallel Computing
Practical:
Moore’s Law cannot hold forever
Problems must be solved immediately
Cost-effectiveness
Scalability
Theoretical:
challenging problems

5. Efficient and optimal parallel algorithms
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

6. The main open question
The basic parallel complexity class is NC.
NC is a class of problems computable in poly-logarithmic time (log c n, for a constant c) using a polynomial number of processors.
P is a class of problems computable sequentially in a polynomial time
The main open question in parallel computations is
NC = P ?

7. PRAM A very reasonable question: Why do we need a PRAM model? . . . .1
PRAM - Parallel Random Access Machine
Shared-memory multiprocessor
unlimited number of processors, each
has unlimited local memory
knows its ID
able to access the shared
memory in constant time
unlimited shared memory P1 2 3 P2 . . . Pi . . Pn m
A very reasonable question: Why do we need a PRAM model?
to make it easy to reason about algorithms
to achieve complexity bounds
to analyze the maximum parallelism

8. PRAM MODEL 1 P1 2 3 P2 ? .
Common Memory . . Pi . . Pn m
PRAM n RAM processors connected to a common memory of m cells
ASSUMPTION: at each time unit each Pi can read a memory cell, make an internal
computation and write another memory cell.
CONSEQUENCE: any pair of processor Pi Pj can communicate in constant time!
Pi writes the message in cell x at time t
Pi reads the message in cell x at time t+1

9. Summary of assumptions for PRAM
Inputs/Outputs are placed in the shared memory (designated address)
Memory cell stores an arbitrarily large integer
Each instruction takes unit time
Instructions are synchronized across the processors
PRAM Instruction Set
accumulator architecture
memory cell R0 accumulates results
multiply/divide instructions take only constant operands
prevents generating exponentially large numbers in polynomial time

10. PRAM Complexity Measures
for each individual processor
time: number of instructions executed
space: number of memory cells accessed
PRAM machine
time: time taken by the longest running processor
hardware: maximum number of active processors

11. Two Technical Issues for PRAM
How processors are activated
How shared memory is accessed

12. Processor Activation
P0 places the number of processors (p) in the designated shared-memory cell
each active Pi, where i < p, starts executing
O(1) time to activate
all processors halt when P0 halts
Active processors explicitly activate additional processors via FORK instructions
tree-like activation
O(log p) time to activate p
... 1
i processor will activate a processor 2i and a processor 2i+1

13. PRAM Too many interconnections gives problems with synchronization
However it is the best conceptual model for designing efficient parallel algorithms
due to simplicity and possibility of simulating efficiently PRAM algorithms on more realistic parallel architectures
Basic parallel statement
for all x in X do in parallel
instruction (x)
For each x PRAM will assign a processor which will execute instruction(x)

14. Shared-Memory Access
Concurrent (C) means, many processors can do the operation simultaneously in the same memory
Exclusive (E) not concurent
EREW (Exclusive Read Exclusive Write)
CREW (Concurrent Read Exclusive Write)
Many processors can read simultaneously the same location, but only one can attempt to write to a given location
ERCW (Exclusive Read Concurrent Write)
CRCW (Concurrent Read Concurrent Write)
Many processors can write/read at/from the same memory location

15. Concurrent Write (CW) What value gets written finally?
Priority CW – processors have priority based on which write value is decided
Common CW – multiple processors can simultaneously write only if values are the same
Arbitrary/Random CW – any one of the values are randomly chosen

16. Example CRCW-PRAM Initially table A contains values 0 and 1
output contains value 0
The program computes the “Boolean OR” of
A[1], A[2], A[3], A[4], A[5]

17. Example CREW-PRAM
Assume initially table A contains [0,0,0,0,0,1] and we have the parallel program

18. Pascal triangle
PRAM CREW

19. Membership problem p processors PRAM with n numbers (p ≤ n)
Does x exist within the n numbers?
P0 contains x and finally P0 has to know
Algorithm
step1: Inform everyone what x is
step2: Every processor checks [n/p] numbers and sets a flag
step3: Check if any of the flags are set to 1

20. One more time about PRAM model
N synchronized processors
Shared memory
EREW, ERCW,
CREW, CRCW
Constant time
access to the memory
standard multiplication/addition
Communication
(implemented via access to shared memory)

21. Two problems for PRAM Problem 1. Min of n numbers
Problem 2. Computing a position of the first one in the sequence of 0’s and 1’s.
How fast we can compute with many processor and how to reduce the number of processors?

22. ? Min of n numbers Input: Given an array A with n numbers
Output: the minimal number in an array A
Cost = 1 n ?
Sequential vs Parallel
Optimal
Par.Cost = O(n)
Sequential algorithm …
At least n comparisons should be performed!!!
COST = (num. of processors)  (time)

23. Mission: Impossible … computing in a constant time
Archimedes: Give me a lever long enough and a place to stand and I will move the earth
NOWDAYS….
Give me a parallel machine with enough processors and I will find the smallest number in any giant set in a constant time!

24. Parallel solution 1 Min of n numbers
Comparisons between numbers can be done independently
The second part is to find the result using concurrent write mode
For n numbers ----> we have ~ n2 pairs
[a1,a2,a3,a4]
(a1,a2)
(a2, a3)
(a3, a4)
(a2, a4)
(a1, a3)
(a1, a4) 1
(ai ,aj)
If ai > aj then ai cannot be the minimal number n i j 1
M[1..n]

25. for each 1 i  n do in parallel M[i]:=0
The following program computes MIN of n numbers stored in the array C[1..n] in O(1) time with n2 processors.
Algorithm A1
for each 1 i  n do in parallel
M[i]:=0
for each 1 i,j  n do in parallel
if ij C[i]  C[j] then M[j]:=1
if M[i]=0 then output:=i

26. From n2 processors to n1+1/2A1 A1
Step 1: Partition into disjoint blocks of size Step 2: Apply A1 to each block Step 3: Apply A1 to the results from the step 2

27. From n1+1/2 processors to n1+1/4A2 A2
Step 1: Partition into disjoint blocks of size Step 2: Apply A2 to each block Step 3: Apply A2 to the results from the step 2

28. 2. Partition the input array C of size n into disjoint
n2 -> n1+1/2 -> n1+1/4 -> n1+1/8 -> n1+1/16 ->… -> n1+1/k ~ n1
Assume that we have an algorithm Ak working in O(1) time with processors
Algorithm Ak+1
1.Let =1/2
2. Partition the input array C of size n into disjoint
blocks of size n each
3. Apply in parallel algorithm Ak to each of these blocks
4. Apply algorithm Ak to the array C’ consisting of n/ n
minima in the blocks.

29. Complexity
We can compute minimum of n numbers using CRCW PRAM model in O(log log n) with n processors by applying a strategy of partitioning the input
ParCost = n  log log n

30. Mission: Impossible (Part 2) Computing a position of the first one in the sequence of 0’s and 1’s in a constant time.

31. Problem 2. Computing a position of the first one in the sequence of 0’s and 1’s.
Algorithm A
(2 parallel steps and n2 processors)
for each 1 i<j  n do in parallel
if C[i] =1 and C[j]=1 then C[j]:=0
for each 1 i  n do in parallel
if C[i] =1 then FIRST-ONE-POSITION:=i 1 1 1
FIRST-ONE-POSITION(C)=4 for the input array
C=[0,0,0,1,0,0,0,1,1,1,0,0,0,1]
After the first parallel step C will contain a single element 1

32. Reducing number of processors
Algorithm B –
it reports if there is any one in the table.
There-is-one:=0
for each 1 i  n do in parallel
if C[i] =1 then There-is-one:=1 1 1 1

33. Now we can merge two algorithms A and B
Partition table C into segments of size
In each segment apply the algorithm B
Find position of the first one in these sequence by applying algorithm A
Apply algorithm A to this single segment and compute the final value B A A

34. Complexity
We apply an algorithm A twice and each time to the array of length
which need only ( )2 = n processors
The time is O(1) and number of processors is n.

35. Tractable and intractable problems for parallel computers

36. P (complexity)
In computational complexity theory, P is the complexity class containing decision problems which can be solved by a deterministic Turing machine using a polynomial amount of computation time, or polynomial time.
P is known to contain many natural problems, including linear programming, calculating the greatest common divisor, and finding a maximum matching.
In 2002, it was shown that the problem of determining if a number is prime is in P.

37. P-complete class
In complexity theory, the complexity class P-complete is a set of decision problems and is useful in the analysis of which problems can be efficiently solved on parallel computers.
A decision problem is in P-complete if it is complete for P, meaning that it is in P, and that every problem in P can be reduced to it in polylogarithmic time on a parallel computer with a polynomial number of processors.
In other words, a problem A is in P-complete if, for each problem B in P, there are constants c and k such that B can be reduced to A in time O((log n)c) using O(nk) parallel processors.

38. Motivation
The class P, typically taken to consist of all the "tractable" problems for a sequential computer, contains the class NC, which consists of those problems which can be efficiently solved on a parallel computer. This is because parallel computers can be simulated on a sequential machine.
It is not known whether NC=P. In other words, it is not known whether there are any tractable problems that are inherently sequential.
Just as it is widely suspected that P does not equal NP, so it is widely suspected that NC does not equal P.

39. P-complete problems The most basic P-complete problem is this:
Given a Turing machine, an input for that machine, and a number T (written in unary), does that machine halt on that input within the first T steps?
It is clear that this problem is P-complete: if we can parallelize a general simulation of a sequential computer, then we will be able to parallelize any program that runs on that computer.
If this problem is in NC, then so is every other problem in P.

40. This problem illustrates a common trick in the theory of P-completeness. We aren't really interested in whether a problem can be solved quickly on a parallel machine.
We're just interested in whether a parallel machine solves it much more quickly than a sequential machine. Therefore, we have to reword the problem so that the sequential version is in P. That is why this problem required T to be written in unary.
If a number T is written as a binary number (a string of n ones and zeros, where n=log(T)), then the obvious sequential algorithm can take time 2n. On the other hand, if T is written as a unary number (a string of n ones, where n=T), then it only takes time n. By writing T in unary rather than binary, we have reduced the obvious sequential algorithm from exponential time to linear time. That puts the sequential problem in P. Then, it will be in NC if and only if it is parallelizable.

41. P-complete problems
Many other problems have been proved to be P-complete, and therefore are widely believed to be inherently sequential. These include the following problems, either as given, or in a decision-problem form:
In order to prove that a given problem is P-complete, one typically tries to reduce a known P-complete problem to the given one, using an efficient parallel algorithm.

42. Examples of P-complete problems
Circuit Value Problem (CVP) - Given a circuit, the inputs to the circuit, and one gate in the circuit, calculate the output of that gate
Game of Life - Given an initial configuration of Conway's Game of Life, a particular cell, and a time T (in unary), is that cell alive after T steps?
Depth First Search Ordering - Given a graph with fixed ordered adjacency lists, and nodes u and v, is vertex u visited before vertex v in a depth-first search?

43. Problems not known to be P-complete
Some problems are not known to be either NP-complete or P. These problems (e.g. factoring) are suspected to be difficult.
Similarly there are problems that are not known to be either P-complete or NC, but are thought to be difficult to parallelize.
Examples include the decision problem forms of finding the greatest common divisor of two binary numbers, and determining what answer the extended Euclidean algorithm would return when given two binary numbers.

44. Conclusion
Just as the class P can be thought of as the tractable problems, so NC can be thought of as the problems that can be efficiently solved on a parallel computer.
NC is a subset of P because parallel computers can be simulated by sequential ones.
It is unknown whether NC = P, but most researchers suspect this to be false, meaning that there are some tractable problems which are probably "inherently sequential" and cannot significantly be sped up by using parallelism
The class P-Complete can be thought of as "probably not parallelizable" or "probably inherently sequential".