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Lecture 4 Analytical Modeling
of Parallel Programs
Parallel Computing
Fall 2008
1
Performance Metrics for
Parallel Systems


Number of processing elements p
Execution Time


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Parallel runtime: the time that elapses from the moment a
parallel computation starts to the moment the last processing
element finishes execution.
Ts: serial runtime
Tp: parallel runtime
Total Parallel Overhead T0


Total time collectively spent by all the processing elements –
running time required by the fastest known sequential
algorithm for solving the same problem on a single processing
element.
T0=pTp-Ts
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Performance Metrics for
Parallel Systems

Speedup S:
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

The ratio of the serial runtime of the best sequential algorithm for
solving a problem to the time taken by the parallel algorithm to
solve the same problem on p processing elements.
S=Ts(best)/Tp
Example: adding n numbers: Tp=Θ(logn), Ts= Θ(n), S= Θ(n/logn)
Theoretically, speedup can never exceed the number of processing
elements p(S<=p).


Proof: Assume a speedup is greater than p, then each processing
element can spend less than time Ts/p solving the problem. In this case,
a single processing element could emulate the p processing elements
and solve the problem in fewer than Ts units of time. This is a
contradiction because speedup, by definition, is computed with respect
to the best sequential algorithm.
Superlinear speedup: In practice, a speedup greater than p is
sometimes observed, this usually happens when the work
performed by a serial algorithm is greater than its parallel
formulation or due to hardware features that put the serial
implementation at a disadvantage.
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Example for Superlinear speedup

Superlinear speedup:

Example1: Superlinear effects from caches: With the problem
instance size of A and 64KB cache, the cache hit rate is 80%.
Assume latency to cache of 2ns and latency of DRAM of 100ns,
then memory access time is 2*0.8+100*0.2=21.6ns. If the
computation is memory bound and performs one FLOP/memory
access, this corresponds to a processing rate of 46.3 MFLOPS. With
the problem instance size of A/2 and 64KB cache, the cache hit
rate is higher, i.e., 90%, 8% the remaining data comes from local
DRAM and the other 2% comes from the remote DRAM with
latency of 400ns, then memory access time is
2*0.9+100*0.08+400*0.02=17.8. The corresponding execution
rate at each processor is 56.18MFLOPS, and for two processors the
total processing rate is 112.36MFLOPS. Then the speedup will be
112.36/46.3=2.43!
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Example for Superlinear speedup

Superlinear speedup:

Example2: Superlinear effects due to exploratory decomposition:
explore leaf nodes of an unstructured tree. Each leaf has a label
associated with it and the objective is to find a node with a
specified label, say ‘S’. The solution node is the rightmost leaf in
the tree. A serial formulation of this problem based on depth-first
tree traversal explores the entire tree, i.e. all 14 nodes, time is 14
units time. Now a parallel formulation in which the left subtree is
explored by processing element 0 and the right subtree is explored
by processing element 1. The total work done by the parallel
algorithm is only 9 nodes and corresponding parallel time is 5 units
time. Then the speedup is 14/5=2.8.
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Performance Metrics for
Parallel Systems(cont.)

Efficiency E


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

Cost(also called Work or processor-time product) W

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
Ratio of speedup to the number of processing element.
E=S/p
A measure of the fraction of time for which a processing element is usefully
employed.
Examples: adding n numbers on n processing elements: Tp=Θ(logn), Ts=
Θ(n), S= Θ(n/logn), E= Θ(1/logn)
Product of parallel runtime and the number of processing elements used.
W=Tp*p
Examples: adding n numbers on n processing elements: W= Θ(nlogn).
Cost-optimal: if the cost of solving a problem on a parallel computer has the
same asymptotic growth(in Θ terms) as a function of the input size as the
fastest-known sequential algorithm on a single processing element.
Problem Size W2


The number of basic computation steps in the best sequential algorithm to
solve the problem on a single processing element.
W2=Ts of the fastest known algorithm to solve the problem on a sequential
computer.
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Parallel vs Sequential Computing:
Amdahl’s

Theorem 0.1 (Amdahl’s Law) Let f, 0 ≤ f ≤ 1, be
the fraction of a computation that is inherently
sequential. Then the maximum obtainable speedup S
on p processors is S ≤1/(f + (1 − f)/p)

Proof. Let T be the sequential running time for the named
computation. fT is the time spent on the inherently sequential
part of the program. On p processors the remaining
computation, if fully parallelizable, would achieve a running
time of at most (1−f)T/p. This way the running time of the
parallel program on p processors is the sum of the execution
time of the sequential and parallel components that is, fT + (1
− f)T/p. The maximum allowable speedup is therefore S ≤
T/(fT + (1 − f)T/p) and the result is proven.
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Amdahl’s Law


Amdahl used this observation to advocate the building of even
more powerful sequential machines as one cannot gain much by
using parallel machines. For example if f = 10%, then S ≤ 10 as
p → ∞. The underlying assumption in Amdahl’s Law is that the
sequential component of a program is a constant fraction of the
whole program. In many instances as problem size increases
the fraction of computation that is inherently sequential
decreases with time. In many cases even a speedup of 10 is
quite significant by itself.
In addition Amdahl’s law is based on the concept that parallel
computing always tries to minimize parallel time. In some cases
a parallel computer is used to increase the problem size that can
be solved in a fixed amount of time. For example in weather
prediction this would increase the accuracy of say a three-day
forecast or would allow a more accurate five-day forecast.
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Parallel vs Sequential Computing:
Gustaffson’s Law

Theorem 0.2 (Gustafson’s Law) Let the execution time of a
parallel algorithm consist of a sequential segment fT and a
parallel segment (1 − f)T and the sequential segment is
constant. The scaled speedup of the algorithm is then. S =(fT +
(1 − f)Tp)/(fT + (1 − f)T) = f + (1 − f)p

For f = 0.05, we get S = 19.05, whereas Amdahl’s law gives an S ≤
10.26.
1 proc
p proc
fT
fT
(1-f)Tp
(1-f)T
T(f+(1-f)p) T

Amdahl’s Law assumes that problem size is fixed when it deals with
scalability. Gustafson’s Law assumes that running time is fixed.
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Brent’s Scheduling Principle
(Emulations)



Suppose we have an unlimited parallelism efficient parallel algorithm,
i.e. an algorithm that runs on zillions of processors. In practice zillions
of processors may not available. Suppose we have only p processors. A
question that arises is what can we do to “run” the efficient zillion
processor algorithm on our limited machine.
One answer is emulation: simulate the zillion processor algorithm on
the p processor machine.
Theorem 0.3 (Brent’s Principle) Let the execution time of a parallel
algorithm requires m operations and runs in parallel time t. Then
running this algorithm on a limited processor machine with only p
processors would require time m/p + t.

Proof: Let mi be the number of computational operations at the i-th step,
i.e.  mi  m .If we assign the p processors on the i-th step to work on these
mi operations they can conclude in time mi / p  mi / p 1 . Thus the total
running time on p processors would be
t
 m / p    m / p  1  t   m / p  t  m / p
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End
Thank you!
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