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Imagine a symphony orchestra where each musician plays without regard to the conductor or the other musicians. At the first tap of the conductor’s baton, each musician goes through all of his or her sheet music. Some finish far ahead of others, leave the stage, and go home. The cacophony wouldn’t resemble music (come to think of it, it would resemble experimental jazz) because it would be totally uncoordinated. Of course this isn’t how music is played. A computer program, like a musical piece, is woven on a fabric that unfolds in time (though perhaps woven more loosely). Certain things must happen before or along with others, and there is a rate to the whole process.

With computer programs, whenever event A must occur before event B can, we say that B is dependent on A. We call the relationship between them a dependency. Sometimes dependencies exist because of calculations or memory operations; we call these data dependencies . Other times, we are waiting for a branch or do-loop exit to take place; this is called a control dependency . Each is present in every program to varying degrees. The goal is to eliminate as many dependencies as possible. Rearranging a program so that two chunks of the computation are less dependent exposes parallelism , or opportunities to do several things at once.

Control dependencies

Just as variable assignments can depend on other assignments, a variable’s value can also depend on the flow of control within the program. For instance, an assignment within an if-statement can occur only if the conditional evaluates to true. The same can be said of an assignment within a loop. If the loop is never entered, no statements inside the loop are executed.

When calculations occur as a consequence of the flow of control, we say there is a control dependency , as in the code below and shown graphically in [link] . The assignment located inside the block-if may or may not be executed, depending on the outcome of the test X .NE. 0. In other words, the value of Y depends on the flow of control in the code around it. Again, this may sound to you like a concern for compiler designers, not programmers, and that’s mostly true. But there are times when you might want to move control-dependent instructions around to get expensive calculations out of the way (provided your compiler isn’t smart enough to do it for you). For example, say that [link] represents a little section of your program. Flow of control enters at the top and goes through two branch decisions. Furthermore, say that there is a square root operation at the entry point, and that the flow of control almost always goes from the top, down to the leg containing the statement A=0.0 . This means that the results of the calculation A=SQRT(B) are almost always discarded because A gets a new value of 0.0 each time through. A square root operation is always “expensive” because it takes a lot of time to execute. The trouble is that you can’t just get rid of it; occasionally it’s needed. However, you could move it out of the way and continue to observe the control dependencies by making two copies of the square root operation along the less traveled branches, as shown in [link] . This way the SQRT would execute only along those paths where it was actually needed.

Questions & Answers

what is a good calculator for all algebra; would a Casio fx 260 work with all algebra equations? please name the cheapest, thanks.
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a perfect square v²+2v+_
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algebra 2 Inequalities:If equation 2 = 0 it is an open set?
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or infinite solutions?
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Kristine 2*2*2=8
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Differences Between Laspeyres and Paasche Indices
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J, combine like terms 7x-4y
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f(x)= 2|x+5| find f(-6)
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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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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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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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