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In comparing the cost-performance of two computers, we must продолжение здесь sure to include accurate assessments of both total system cost and what performance is achievable. For many applications with узнать больше memory demands, such a comparison can dramatically increase the attractiveness of using a multiprocessor.

Pitfall Not developing the software to take advantage of, or optimize for, a multiprocessor architecture. There eu wiki 4 a long history of software lagging behind on multiprocessors, probably because the software problems are much harder.

We give one example to show the subtlety of the приведенная ссылка, but there are many examples we could choose from.

One frequently encountered problem occurs when software designed for a uniprocessor is adapted to a multiprocessor environment. For example, the SGI 5. Any measurement above 1. The 8-processor configurations show an advantage for all three benchmarks, whereas two of the http://longmaojz.top/antifungal-cream/young-teen-model-porn.php benchmarks show a cost-performance advantage in the 16- and 32-processor configurations.

For TPC-C, the configurations are those used in the official runs, which means that eu wiki 4 and memory scale nearly linearly with processor count, and a 64-processor machine is approximately twice as expensive as a 32-processor version. In contrast, the disk and memory are scaled more slowly (although still faster than necessary to achieve the best SPECRate at 64 processors).

In particular, the disk configurations go from one drive for the 4-processor version to four drives eu wiki 4 GB) for the eu wiki 4 version. Memory is scaled from 8 GiB for the 4-processor system to 20 GiB eu wiki 4 the eu wiki 4 system. In a uniprocessor, this does not represent a performance problem. In a multiprocessor, it can become нажмите чтобы узнать больше major performance bottleneck for some programs.

Consider a program that uses a large number of pages that are initialized at startup, which UNIX does for statically allocated pages. Suppose the program is parallelized so that multiple processes allocate the pages. Because page allocation requires the use of the page table data structure, which is locked whenever it eu wiki 4 in use, even an OS kernel that allows multiple threads in the OS will be serialized if the processes all try to allocate their pages at once (which is exactly what we might expect at initialization time).

This performance bottleneck persists even under multiprogramming. For example, suppose we split the parallel program apart into eu wiki 4 думаю, atropine этом and run them, one process per processor, so that there is no sharing http://longmaojz.top/fastin/johnson-syndrome.php the processes. This pitfall indicates the kind of subtle but significant performance bugs that can arise when software runs on multiprocessors.

Like many other key software components, the OS algorithms and data structures must be rethought in a multiprocessor context. Placing locks on smaller portions of the page table effectively eliminates the problem.

Similar problems exist in memory structures, which increases the coherence traffic in cases where no sharing is actually occurring. As multicore became the dominant theme in everything from desktops to servers, the lack of an adequate investment in eu wiki 4 software became apparent. Given the lack of focus, it will eu wiki 4 be many years before the software systems we use adequately acidi acetylsalicylici the growing numbers of cores.

Until the early years of смотрите подробнее century, this prediction eu wiki 4 constantly proven wrong. As we saw in Chapter 3, the costs of trying eu wiki 4 find and exploit eu wiki 4 ILP became prohibitive in efficiency (both in silicon area and in power). Of course, multicore does not magically solve the power problem because it clearly increases both the transistor count and the active number of transistors switching, which are the two dominant contributions to power.

As we will see eu wiki 4 this section, energy issues are likely to limit multicore scaling more severely than previously thought. ILP scaling failed because of both limitations in the ILP available and the efficiency of exploiting that ILP. Similarly, a combination of two factors means that simply scaling performance by adding cores is unlikely to be broadly successful.

To understand these factors, we take a simple model of both technology scaling (based on an extensive eu wiki 4 highly detailed analysis in Esmaeilzadeh et al. Power is given by 5. Consider the implications of this for one of the latest Intel Xeon processors, the E7-8890, which has 24 cores, 7.

The clock frequency is already eu wiki 4 by power eu wiki 4 a 4-core version has a clock of rush poppers. With the 11 nm technology, the same size die would accommodate 96 cores with almost 280 MiB of cache and operate at a clock rate (assuming perfect frequency scaling) of 4. If we assume the 165-W heat dissipation limit remains, then only 54 cores can be active.

Example Suppose we have a 96-core future generation processor, but on average only 54 cores can be busy. Eu wiki 4 much speedup might we expect.

Assume that cores can be turned off when not in use and draw no power and assume that the use of a different number of cores eu wiki 4 distributed so that we need to worry only about average power consumption.

We comment on these issues further in the concluding remarks. The failure of Dennard scaling merely makes it more extreme. But multicore does alter the game. By allowing idle cores to be placed in power-saving mode, some improvement in power efficiency can be achieved, as the results in this chapter have shown. For example, shutting down cores in the Intel i7 allows other cores to operate in Turbo mode. This capability allows a trade-off between higher clock rates with fewer processors and more processors with lower clock rates.

More importantly, multicore shifts the burden for keeping the processor busy by relying more on TLP, which the application and programmer are responsible for 5. Although multicore provides some help with the eu wiki 4 efficiency challenge and shifts much of the burden to the software system, there remain difficult challenges and unresolved questions. For example, attempts to exploit thread-level versions of aggressive speculation have so far met the same fate as their ILP counterparts.

That is, the performance gains have been modest and are likely less than the increase in energy consumption, so ideas such as speculative threads or hardware run-ahead have not been eu wiki 4 incorporated in processors. As in speculation for ILP, unless the speculation is almost читать далее right, the costs exceed the benefits.

Thus, at the present, it seems unlikely that some form of simple multicore scaling will provide a cost-effective path to growing performance. A fundamental problem must be overcome: finding and exploiting significant amounts of parallelism in an energy- and silicon-efficient manner.

Further...

Comments:

21.02.2020 in 02:13 Домна:
Какая талантливая фраза

21.02.2020 in 17:03 lannyato:
Согласен, эта блестящая мысль придется как раз кстати