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Sometimes you want more detail than the overall timing of the application. But you don’t have time to modify the code to insert several hundred etime calls into your code. Profiles are also very useful when you have been handed a strange 20,000-line application program and told to figure out how it works and then improve its performance.

Most compilers provide a facility to automatically insert timing calls into your code at the entry and exit of each routine at compile time. While your program runs, the entry and exit times are recorded and then dumped into a file. A separate utility summarizes the execution patterns and produces a report that shows the percentage of the time spent in each of your routines and the library routines.

The profile gives you a sense of the shape of the execution profile. That is, you can see that 10% of the time is spent in subroutine A, 5% in subroutine B, etc. Naturally, if you add all of the routines together they should account for 100% of the overall time spent. From these percentages you can construct a picture — a profile — of how execution is distributed when the program runs. Though not representative of any particular profiling tool, the histograms in [link] and [link] depict these percentages, sorted from left to right, with each vertical column representing a different routine. They help illustrate different profile shapes.

Sharp profile — dominated by routine 1

This figure is a histogram, with routines on the horizontal axis and % time on the vertical axis. For routines of value 1, the histogram shows a % time value of 65. For routines of value 2, the histogram shows a % time value of 20. For routines of value 3, the histogram shows a % time value of 10. For routines of value 4, the histogram shows a % time value of 5. For routines of value 5, the histogram shows a % time value of approximately 2. Above the histograms is a dashed curve that follows the decreasing trend of the histogram as the number of routines increases.

A sharp profile says that most of the time is spent in one or two procedures, and if you want to improve the program’s performance you should focus your efforts on tuning those procedures. A minor optimization in a heavily executed line of code can sometimes have a great effect on the overall runtime, given the right opportunity. A flat profile , The term “flat profile” is a little overloaded. We are using it to describe a profile that shows an even distribution of time throughout the program. You will also see the label flat profile used to draw distinction from a call graph profile, as described below. on the other hand, tells you that the runtime is spread across many routines, and effort spent optimizing any one or two will have little benefit in speeding up the program. Of course, there are also programs whose execution profile falls somewhere in the middle.

Flat profile — no routine predominates

This figure is a histogram, with routines on the horizontal axis and % time on the vertical axis. For routines of value 1, the histogram shows a % time value of approximately 22. For routines of value 2, the histogram shows a % time value of 20. For routines of value 3, the histogram shows a % time value of approximately 18. For routines of value 4, the histogram shows a % time value of approximately 22. For routines of value 5, the histogram shows a % time value of approximately 18. Above the histograms is a dashed curve that follows the decreasing trend of the histogram as the number of routines increases.

We cannot predict with absolute certainty what you are likely to find when you profile your programs, but there are some general trends. For instance, engineering and scientific codes built around matrix solutions often exhibit very sharp profiles. The runtime is dominated by the work performed in a handful of routines. To tune the code, you need to focus your efforts on those routines to make them more efficient. It may involve restructuring loops to expose parallelism, providing hints to the compiler, or rearranging memory references. In any case, the challenge is tangible; you can see the problems you have to fix.

There are limits to how much tuning one or two routines will improve your runtime, of course. An often quoted rule of thumb is Amdahl’s Law , derived from remarks made in 1967 by one of the designers of the IBM 360 series, and founder of Amdahl Computer, Gene Amdahl. Strictly speaking, his remarks were about the performance potential of parallel computers, but people have adapted Amdahl’s Law to describe other things too. For our purposes, it goes like this: Say you have a program with two parts, one that can be optimized so that it goes infinitely fast and another that can’t be optimized at all. Even if the optimizable portion makes up 50% of the initial runtime, at best you will be able to cut the total runtime in half. That is, your runtime will eventually be dominated by the portion that can’t be optimized. This puts an upper limit on your expectations when tuning.

Questions & Answers

how do they get the third part x = (32)5/4
kinnecy Reply
can someone help me with some logarithmic and exponential equations.
Jeffrey Reply
sure. what is your question?
ninjadapaul
20/(×-6^2)
Salomon
okay, so you have 6 raised to the power of 2. what is that part of your answer
ninjadapaul
I don't understand what the A with approx sign and the boxed x mean
ninjadapaul
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
Salomon
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Salomon
I got X =-6
Salomon
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
ninjadapaul
oops. ignore that.
ninjadapaul
so you not have an equal sign anywhere in the original equation?
ninjadapaul
Commplementary angles
Idrissa Reply
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Sherica
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Uday
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a perfect square v²+2v+_
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or infinite solutions?
Kim
The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
Al
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Embra Reply
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ramon Reply
Kristine 2*2*2=8
Bridget Reply
Differences Between Laspeyres and Paasche Indices
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No. 7x -4y is simplified from 4x + (3y + 3x) -7y
Mary Reply
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Joan Reply
J, combine like terms 7x-4y
Bridget Reply
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linda Reply
Need to simplify the expresin. 3/7 (x+y)-1/7 (x-1)=
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. After 3 months on a diet, Lisa had lost 12% of her original weight. She lost 21 pounds. What was Lisa's original weight?
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China
Cied
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abeetha Reply
I start with an easy one. carbon nanotubes woven into a long filament like a string
Porter
many many of nanotubes
Porter
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Yasmin
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Cesar
I'm interested in nanotube
Uday
what is nanomaterials​ and their applications of sensors.
Ramkumar Reply
what is nano technology
Sravani Reply
what is system testing?
AMJAD
preparation of nanomaterial
Victor Reply
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
good afternoon madam
AMJAD
what is system testing
AMJAD
what is the application of nanotechnology?
Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
Azam
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Prasenjit
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
Azam
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
Prasenjit
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
Damian
silver nanoparticles could handle the job?
Damian
not now but maybe in future only AgNP maybe any other nanomaterials
Azam
Hello
Uday
I'm interested in Nanotube
Uday
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
Prasenjit
can nanotechnology change the direction of the face of the world
Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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the Beer law works very well for dilute solutions but fails for very high concentrations. why?
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Smarajit Reply
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Source:  OpenStax, High performance computing. OpenStax CNX. Aug 25, 2010 Download for free at http://cnx.org/content/col11136/1.5
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