User Benchmarks Download

The tests were conducted in a three-tiered client/server environment comprising the following components: Unisys HS/6 application servers, each with six Intel 200-MHz Pentium Pro processors, 1024 Kbytes of Level-2 cache, two Gbytes of memory, and 72 Gbytes of disk A Unisys QS/2 database server running Oracle7.3.3 equipped with four 400-MHz Pentium II Xeon processors, 32 Kbytes of Level-1 and 1024 Kbytes of Level-2 cache, four Gbytes of memory, and 378 Gbytes of disk A client PC for submitting transactions and a set of Mercury Interactive LoadRunner load drivers for simulating concurrent users

I've used time command to measure real time. Sometimes i got more cpu time (hadoop counter) than actual real time or vice versa.I know that real time measures actual clock time elapsed and it can be greater or lesser than user+sys.

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Control loop performance assessment has been extended to many situations, and many approaches have been developed as discussed in the earlier chapters, e.g., performance assessment of: 1) SISO feedback control systems (Desborough and Harris 1992; Stanfelj, Marlin, and MacGregor 1993; Kozub and Garcia 1993; Lynch and Dumont 1993; Tyler and Morari 1996); 2) feedback control of nonminimum phase SISO systems (Tyler and Morari 1995); 3) MIMO feedback control systems (Huang, Shah, and Kwok 1995; Huang, Shah, and Kwok 1996; Harris, Boudreau, and MacGregor 1995; Harris, Boudreau, and MacGregor 1996). The portion of a process output that is feedback controller invariant determines the minimum variance achievable theoretically and characterizes the most fundamental performance limitation of a system owing to the existence of time-delays or infinite zeros. Practically, however, there are many other limitations on the achievable control loop performance. The existence of nonminimum phase or poorly damped zeros, sampling rate, amplitude and/or rate constraints on control action, robustness constraints etc. are examples of such limitations; therefore, a feedback controller that indicates performance reasonably close to minimum variance control does not require further tuning (if the variance is the most important issue) while a feedback controller that indicates poor performance relative to minimum variance control is not necessarily a poor controller. Further analysis of performance limitations and comparison with more realistic benchmarks are usually required. Performance assessment with minimum variance control as a benchmark requires minimum effort (routine operating data plus a priori knowledge of time-delays), and serves as the most convenient first-level performance assessment test, therefore, (if the variance is the main point of interest). Only those loops that indicate poor first-level performance need to be re-evaluated by higher-level performance assessment tests. A higher-level performance test usually requires more a priori knowledge than just a knowledge of time-delays. This chapter addresses practical issues that are of interest for such a higher-level performance assessment test.

In order to improve our airline travel booking app's notification experience, I'm conducting a benchmarking study where I hope to look at the various notifications at different points of the user journey (e.g. online check-in open, miles have been added, change of flight time).

When User Record Auditing is not enabled, the Modification History table displays each time the User Record page is edited for a user. This includes the name and username of the user who modified the page and the date and time at which it was edited. However, the Modification History table does not display details for each field that was modified. This makes it difficult to audit which fields have been modified and the field's previous value.

When User Record Auditing is enabled, the Modification History table displays a detailed record of each successfully completed and scheduled modification to the User Record page for a user. In addition, administrators can search modifications by field name and filter modifications by field type.

Evaluation process of a switch-based interaction technique (SIT) requires an interdisciplinary team effort and takes a considerable amount of time. Collecting subjective evaluation data from the users is a very common approach, but the subjective evaluation data alone might be manipulated and unreliable for comparing performances in many cases. Thus, therapists generally cannot succeed in determining the optimum SIT setup (i.e., determining the most appropriate combination of setup variables such as the switch type or switch site) at first attempts since it is hard to evaluate the measurable performance by collecting subjective data instead of objective data. Inevitably, each unsuccessful attempt to reach the optimum SIT setup results in a loss of serious time and effort. On the contrary, a benchmark application is also required to make performance evaluation of SITs by using a number of standard tests and empirical attributes. It is obvious that a quicker and more accurate SIT evaluation process provides a better cost and schedule management considering the increasing number of SIT users in the world. Therefore, we propose a novel benchmark for performance evaluation called SITbench that provides a quicker and more accurate switch evaluation process by collecting and saving the objective data automatically. We conducted a user study with eight participants and demonstrated that the objective data collected via the SITbench helped to determine the optimum SIT setup accurately. Result of a questionnaire applied to evaluate the SITbench itself was also satisfactory. SITbench is expected to help researchers and therapists to make a better evaluation according to any change done in SIT setup variables (switch type, activation method, etc.) with the aim of reaching the optimum SIT setup, which leads to a better cost and schedule management. As the first benchmark application compatible with all SITs, which can emulate keyboard characters or mouse clicks, it can be utilized by assistive technology professionals to make comparisons and evaluations automatically via standardized tests.

The JSON format outputs human readable json split into two top level attributes. The context attribute contains information about the run in general, including information about the CPU and the date. The benchmarks attribute contains a list of every benchmark run. Example json output looks like:

Benchmarks are executed by running the produced binaries. Benchmarks binaries, by default, accept options that may be specified either through their command line interface or by setting environment variables before execution. For every --option_flag= CLI switch, a corresponding environment variable OPTION_FLAG= exist and is used as default if set (CLI switches always prevails). A complete list of CLI options is available running benchmarks with the --help switch.

Sometimes it's useful to add extra context to the content printed before the results. By default this section includes information about the CPU on which the benchmarks are running. If you do want to add more context, you can use the benchmark_context command line flag:

Sometimes a family of benchmarks can be implemented with just one routine that takes an extra argument to specify which one of the family of benchmarks to run. For example, the following code defines a family of benchmarks for measuring the speed of memcpy() calls of different lengths:

Some benchmarks may require specific argument values that cannot be expressed with Ranges. In this case, ArgsProduct offers the ability to generate a benchmark input for each combination in the product of the supplied vectors.

Asymptotic complexity might be calculated for a family of benchmarks. The following code will calculate the coefficient for the high-order term in the running time and the normalized root-mean square error of string comparison.

In multithreaded benchmarks, each counter is set on the calling thread only. When the benchmark finishes, the counters from each thread will be summed; the resulting sum is the value which will be shown for the benchmark.

When using the console reporter, by default, user counters are printed at the end after the table, the same way as bytes_processed and items_processed. This is best for cases in which there are few counters, or where there are only a couple of lines per benchmark. Here's an example of the default output:

By default each benchmark is run once and that single result is reported. However benchmarks are often noisy and a single result may not be representative of the overall behavior. For this reason it's possible to repeatedly rerun the benchmark.

The RegisterBenchmark(name, func, args...) function provides an alternative way to create and register benchmarks. RegisterBenchmark(name, func, args...) creates, registers, and returns a pointer to a new benchmark with the specified name that invokes func(st, args...) where st is a benchmark::State object.

The times for some benchmarks depend on the order in which items are run. These differences are due to the cost of memory allocation and garbagecollection. To avoid these discrepancies, the bmbm method is provided. Forexample, to compare ways to sort an array of floats:

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The RePEc plagiarism page Renewal of water-related infrastructure and user\'s contribution: a few benchmarksEpiphane Assouan, Tina Rambonilaza and Bndicte RulleauCahiers du GREThA (2007-2019) from Groupe de Recherche en Economie Thorique et Applique (GREThA)Abstract:This paper studies the contribution required from the users of collective drinking water networks to finance asset management of infrastructures. We characterize the first-best optimum and we compare it to the social optimum in the presence of preferences heterogeneity, in order to take into account the uses of alternative techniques for certain household needs. These alternatives uses generate negative externalities for the good functioning of the water networks. The first-best optimum thus requires a transfer from the exclusive users of the collective network to the users of the alternatives. Furthermore, Nash equilibrium reveals that the existence of this transfer requires other motivations than the only usage values. Finally, the case of water infrastructure asset management emphasizes how an essential part of inequality that can be associated with it can be attributed to preferences heterogeneity.Keywords: water services; willigness to pay; pur public good; game theory (search for similar items in EconPapers)

JEL-codes: C72 H41 H54 Q25 (search for similar items in EconPapers)

Date: 2018

New Economics Papers: this item is included in nep-cdm and nep-gth

References: View references in EconPapers View complete reference list from CitEc

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