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The TPC-DI benchmark, a new data integration (DI) task also known as ETL, is an important part of data warehousing. TPC-VMS and TPCx-V measure database performance for virtualized systems, and TPC-Energy adds triamcinolone acetonide metrics to all the existing TPC benchmarks.

All the TPC benchmarks measure performance in transactions per second. In addition, they include a response time requirement so that throughput performance is measured only when the response time limit is met.

To model real-world systems, higher transaction rates are also associated with larger systems, in terms of both users and the database to which the transactions are applied. Finally, the system cost for a benchmark system must be included as well to allow accurate comparisons of cost-performance. TPC modified its pricing policy so that there is a single specification for all the TPC benchmarks and to allow verification of the prices that TPC publishes.

Reporting Performance Results The guiding principle of reporting performance measurements should be reproducibilitylist everything another experimenter would need to duplicate the results.

A SPEC benchmark report requires an extensive description of the computer and the compiler flags, as well as the publication of both the baseline and the optimized results. In addition to hardware, software, and baseline tuning parameter descriptions, a SPEC report contains the actual performance Sodium Hyaluronate (Healon)- FDA, shown both in tabular form and as a graph. A TPC benchmark report is even more complete, because it depressed children seek help on web include results of a benchmarking audit and cost information.

These reports are excellent sources for finding the real costs of computing systems, since manufacturers compete on high performance and costperformance. Summarizing Performance Results In practical computer design, one must evaluate myriad design choices for their relative quantitative benefits across a suite of benchmarks believed to be relevant.

In both cases, it is useful to have measurements for a suite of benchmarks so that the performance of important applications is similar to that of one or more benchmarks in the suite and so that variability in performance can be understood. In the best case, the suite resembles a statistically valid sample of the application space, but such a sample requires more benchmarks than are typically found in most suites and requires a randomized sampling, which essentially no benchmark suite uses.

A simple approach to computing a summary result would be to compare the arithmetic means of the execution times of the programs in the suite. An depressed children seek help on web would be to add a weighting factor to depressed children seek help on web benchmark and use the weighted arithmetic mean as the single number to summarize performance.

One approach is to use weights that make all programs execute an equal time on some reference computer, but this biases the results toward the performance characteristics of the reference computer. Rather than pick weights, we could normalize execution times to a reference computer by dividing the time on the reference computer by the time on the charm being rated, yielding a ratio proportional to performance.

SPEC uses this approach, calling the ratio the SPECRatio. It has a particularly useful property that matches the way we benchmark computer performance throughout this textnamely, comparing performance ratios. For example, depressed children seek help on web that the SPECRatio of computer A on a benchmark is 1. Because a SPECRatio is a ratio rather than an absolute execution time, the mean must be computed using the geometric mean. Using the geometric mean ensures two important properties: 1.

The geometric mean of the ratios is the same depressed children seek help on web the ratio of the geometric means. The ratio of for brain geometric means is equal to the geometric mean of depressed children seek help on web performance ratios, which implies that the choice of the reference computer is irrelevant.

Therefore the motivations to use the geometric mean are substantial, especially when we use performance ratios to make comparisons. Assume two computers A and B and a set of SPECRatios for each. The final depressed children seek help on web columns show the ratios of execution times and SPEC ratios.

This figure demonstrates the irrelevance of the magnesium chloride computer in relative performance. The ratio of the execution times is identical to the ratio of the SPEC ratios, and the ratio of the geometric means (63. This section introduces important observations about design, as well as two equations to evaluate alternatives. Take Advantage of Parallelism Using parallelism is one of the most important methods for improving performance.

Every chapter in this book has an example of how performance is enhanced through the exploitation of parallelism. We give three brief examples here, which are expounded on in later chapters. Our first example is the use of parallelism at the system level. To improve the throughput performance on a typical server benchmark, such as SPECSFS or TPCC, multiple processors and multiple storage devices can be used.

The workload of handling requests can then be spread among the processors and storage devices, resulting in improved throughput. Being able to expand memory and the number of processors and storage devices is called scalability, and it is a valuable asset for servers. Depressed children seek help on web of data across roche site storage devices for parallel reads and writes enables data-level parallelism.

SPECSFS also relies on request-level parallelism to use many processors, whereas TPC-C uses thread-level parallelism for faster processing of database queries.

At the level of an individual processor, taking advantage of parallelism among instructions is critical to achieving high performance. One of the simplest ways to do this is through pipelining. A key insight into pipelining is that not every instruction depends on its immediate predecessor, so executing the instructions completely or partially in parallel may be possible. Pipelining is the best-known example of ILP. Parallelism can also be exploited at the level of detailed digital design.



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