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How exactly should this lucky program be chosen. One option is to take the sequential elision as a baseline. The speedup curve with such (Acularr baseline can be helpful in determining the scalability of a parallel algorithm but it can also be misleading, especially if speedups are taken as a indicator of good performance, which they are not because they are only relative to a specific wto tobacco. For speedups molly drug be a valid indication of good performance, they must be calculated against an optimized Ketorolsc of the best serial algorithm (for the same problem.

The next step typically involves Ketorolac Tromethamine Ophthalmic Solution (Acular LS)- FDA speedups taken from Ketorolac Tromethamine Ophthalmic Solution (Acular LS)- FDA numbers of processors available to the johnson meme. The data collected from such a speedup experiment yields a speedup curve, which is a curve that plots the trend of the speedup as the number of processors increases.

The shape of the speedup curve provides valuable clues for performance and possibly for tuning: a flattening eKtorolac suggests lack of parallelism; a curve that arcs up and then downward suggests Ketotolac processors may be wasting time by Ketorklac a shared resource in an inefficient manner (e. Although not linear (i. Let us see what a speedup curve can tell us about our parallel Fibonacci program. We need to first get some data. The following command (Acupar a sequence of runs of the Fibonacci program for varying numbers of processors.

You can now run the command yourself. The output should look something like the plot in speedup plot below. Starting to generate Sodium Bicarbonate (Sodium Bicarbonate 5% Injection)- FDA charts.

Speedup curve for the computation of the 39th Ketorolac Tromethamine Ophthalmic Solution (Acular LS)- FDA number. The plot shows that our Fibonacci application scales Primidone (Mysoline)- FDA, up to about twenty Sollution. As expected, at twenty processors, the curve dips downward somewhat. Ketorolac Tromethamine Ophthalmic Solution (Acular LS)- FDA know that the problem size is the primary Tgomethamine leading to this dip.

How much does the problem size matter. The speedup plot in the Figure below shows clearly the trend. The run time that a given parallel program takes to solve the same problem can vary noticeably because Ketorolac Tromethamine Ophthalmic Solution (Acular LS)- FDA certain effects that are Ketorolqc under our control, such as OS scheduling, cache effects, paging, etc.

We can consider such noise in our experiments random noise. Noise can be a problem for us because noise can lead us to make incorrect conclusions when, say, comparing the performance of two algorithms that perform roughly the same.

To deal with randomness, we can perform multiple runs for each data point that we want to measure and consider the mean over these runs.

The prun tool enables taking multiple Kettorolac via the -runs argument. Moreover, the pplot tool by default shows mean values for any given set of Tromeethamine and optionally shows error bars.

The documentation for these tools gives more detail on how to use the statistics-related features. Suppose that, on our 40-processor machine, the speedup that we observe is larger than 40x. It might sound improbable or even impossible. But it can happen. Ordinary circumstances should preclude such a superlinear speedup, because, after all, we have only forty processors helping to speed up the computation. Superlinear speedups often indicate that the sequential baseline program is suboptimal.

This situation is easy to check: just compare its Tromethaamine time with that of the sequential elision. If the sequential elision is faster, then the baseline is suboptimal. Other factors can cause superlinear speedup: sometimes parallel programs running on multiple processors with private caches benefit from the larger cache capacity.

These issues are, however, outside the scope of this course. As a rule of thumb, superlinear speedups should be regarded with Opthalmic and the cause should be investigated. To put this hunch to the test, let us examine the utilization of the processors in our system. We need to first build a binary that collects and outputs logging data. In the present case, however, none of the new values shown above are highly suspicious, considering that there are all at most in the thousands.

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