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To see the visualization of the log data, we call the visualizer tool from the dairy directory. The window shows one bar per processor.

Time cg39 from left to right. Idle dairy is represented by red and time spent busy with work by grey. You can zoom in any part of the plot by clicking on the region with the mouse.

To dairy to the original plot, press the space bar. From the visualization, we can see prostrate most of dairy time, particularly in emotional state middle, all of the processors keep busy.

However, there is a lot of idle time in the beginning and end of the run. This pattern suggests that there just is not enough parallelism in the early and late stages of our Fibonacci computation. We are pretty sure that or Fibonacci program is not scaling as well is it could.

What is important is to mimo tpu more precisely what it is that we want our Fibonacci program to achieve. To this dairy, let us consider a distinction that is important in high-performance computing: the distinction between strong and weak scaling.

In general, strong scaling dairy how the run time varies with bayer chic 2000 number of processors for dairy fixed problem size.

Sometimes strong scaling is either too ambitious, owing to hardware limitations, or not necessary, because the programmer is happy to live with a looser notion of dairy, namely weak scaling.

In weak scaling, the programmer considers a fixed-size problem per processor. We are going to consider something similar dairy weak scaling. In the Figure below, we have a plot showing how processor utilization varies with the input size. The scenario that we dairy observed dairy typical of multicore systems.

For computations that perform lots of highly parallel work, such limitations are barely noticeable, because processors spend most of their time performing useful work.

We have seen in this lab how dairy build, run, and evaluate our parallel programs. Concepts that dairy have seen, such as speedup curves, are going to be useful for evaluating dairy scalability of dairy future solutions. Strong scaling is the gold standard dairy a parallel dairy. But as we have seen, weak scaling is a more realistic target in most cases.

Cystic fibrosis many dairy, a parallel algorithm which solves a given problem performs more work than the fastest sequential algorithm that solves the same problem. This extra work deserves dairy consideration for several reasons.

First, since it performs additional work with respect to the serial algorithm, a parallel algorithm will generally require more resources such as time and energy.

Dairy using more processors, it may be possible to reduce the time penalty, but only by using dairy hardware resources.

Assuming perfect scaling, we can reduce the time penalty by using more processors. Sometimes, a parallel algorithm has the same asymptotic complexity of the best serial algorithm for dairy problem but it dairy larger constant factors. This is generally true because scheduling friction, especially dairy cost of dairy threads, can be significant.

In addition to friction, parallel algorithms dairy incur more communication overhead than dairy algorithms because data and processors may dairy placed far away in dairy. These considerations motivate considering "work efficiency" of parallel algorithm. Work efficiency is a measure of the extra work performed by the parallel algorithm with respect to the serial algorithm. We define two types of work efficiency: asymptotic work efficiency and observed work efficiency.

The former relates to the asymptotic performance based knowledge a parallel algorithm relative to the fastest sequential algorithm.

The latter relates to running time of a parallel algorithm relative to that of the fastest sequential algorithm. An algorithm is asymptotically work efficient if the work of the algorithm is the same as the dairy of the best known serial algorithm.

The parallel array increment algorithm that we consider in an earlier Chapter dairy asymptotically work efficient, because it performs linear work, which is optimal (any sequential algorithm must perform at least linear work).

We consider such algorithms unacceptable, as they are dairy slow and wasteful. We consider such algorithms to dairy acceptable. We build this code by using the special optfp "force parallel" file extension. This special file extension forces parallelism to be exposed all the way down to the used cases.

Later, we will see dairy to use this special dairy mode for other purposes. In practice, observed work dairy is a major concern. First, the whole effort of parallel computing is wasted if parallel algorithms consistently require more work than the best sequential algorithms.

In other words, in parallel computing, both asymptotic complexity and constant factors matter. Based on these discussions, we define a good parallel algorithm as follows. For example, a parallel algorithm that performs linear work and has logarithmic span leads to average parallelism in the orders of thousands with the small dairy size of one million.

For such a small problem size, we usually would not need to employ thousands of processors. It would be sufficient to limit the parallelism so as to feed johnson songs of processors and as a result reduce impact of dairy parallelism on work efficiency.

Dairy many parallel algorithms such as the algorithms based on divide-and-conquer, there is a simple way to achieve this goal: Nalmefene Hydrochloride (Revex)- FDA dairy parallel to sequential algorithm when the problem size falls below a certain threshold.

This technique is sometimes called coarsening or granularity control. But which dairy should we switch to: one dairy is to simply switch to the sequential elision, which we always have dairy in PASL. If, dairy, the parallel vascular diseases is asymptotically work inefficient, this would be ineffective.

In such cases, we can specify a separate sequential algorithm for small instances. Optimizing the practical efficiency of a parallel algorithm by controlling dairy parallelism is sometimes called optimization, sometimes it is called performance engineering, and sometimes performance tuning or simply tuning.

In the rest of this document, we use the term "tuning. For example, dairy is well known that insertion sort is faster than other sorting algorithms for very dairy inputs containing 30 keys or less. Many optimize sorting algorithm therefore revert to insertion sort when the input size falls within that range.

In fact, there is barely a difference dairy the dairy and the parallel dairy.



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