Let's let the benchmark run a little longer and check again.
现在让基准运行时间更长一些，来再次进行检查。
We challenged contestants to get the most transactions per minute during a 10-minute benchmark run.
我们要求参赛者在10分钟基准测试运行时间内获得最多的每分钟事务数。
The purpose was not to run a typical database benchmark with huge memory and storage server requirements, but rather to test CPU and process scalability.
目的不是运行一个典型的、具有大量内存和存储服务器需求的数据库基准，而是测试CPU和进程的可伸缩性。
Once the code is in a steady state then the benchmark must run it several times and compute a statistical analysis of the results.
代码达到稳定状态之后，基准必须对这段代码运行多次，然后才能对结果做出有效的统计分析。
Although the details of how to run the benchmark is out of the scope of our discussion, it is still worth going through some of the key aspects.
虽然关于如何运行基准的细节超出了我们的讨论范围，但仍有必要介绍一遍一些关键的方面。
A Throughput run consisting of five streams of queries is required for the benchmark audit.
基准测试审计需要由五个查询流构成的吞吐量运行。
The benchmark unchanged took about 30 minutes to run and I challenged participants to make it run faster.
未修改过的基准代码的运行时间大约为30分钟，要求参赛者改进它，让它运行得更快。
For the purposes of this article, I will contend that the benchmark takes too long to run and that we need it to execute in under an hour.
基于本文的目的，我想说，基准的运行时间过长，我想在一个小时内执行完毕。
To run the LINPACK benchmark on your Linux cluster, you need to get the parallel version of LINPACK and configure it for the cluster.
要在Linux集群上运行LINPACK基准测试，我们需要获得一个并行版本的LINPACK，并对这个集群配置LINPACK。
Specifically, this DB2 UDB benchmark was run on a 16-way IBM eServer p5 570 server and achieved 809,144 tpmC - surpassing competitive results on systems with up to four times more licensed CPU's.
特别地，这个DB2UDB基准在一个16 路IBMeServerp5 570服务器上运行，取得了 809,144tpmC- 这超出了那些拥有多达4倍以上的经过许可授权的CPU的系统上的结果。
After each more complicated benchmark, run a check for consistent, repeatable, accurate results before continuing.
在完成每个更复杂的基准测试阶段之后，检查结果是否是一致的、可重复的、精确的，之后才能执行下一个测试。
In general, a good benchmark for seeing how useful gprof will be in helping you optimize your application is to run it under the time command.
通常，有一个很好的基准测试可以用来查看gprof对于帮助对应用程序进行优化是多么有用，方法是在time命令下面执行它。
Six years later, I run the code in Listing 1 on this modern configuration (which I use for every benchmark result in this article unless I note otherwise).
年后，我在下面的现代配置上运行了清单1中的代码(除非另外说明，本文中的所有基准测试结果都采用这种配置)。
But rather than jumping to that conclusion I decided to validate the results by having my colleagues run the benchmark on their machines.
但是在得出最终的结论之前，我决定先对结果进行检验：我请我的同事们在他们的机器上运行了这个测试。
But often, the result is a do-nothing benchmark, which the compiler can optimize away partially or completely without you realizing it, making the test run faster than expected.
但是，这样做得到的往往是一个不做任何事的基准，在您不知情的情况下，编译器可能将此操作部分地或者完全地优化掉，使得测试运行起来比预期更快。
We run a number of benchmarks internally, so yes, there are some benchmark results for 6.4, but we have had no time to publish them yet.
在内部我们运行了大量的基准，当然，有一些结果是针对6.4的，但是我们没时间发布他们。
After the tests are run and the information is analyzed, the benchmark server can be converted back to a production server or can become a testing server with another focus.
运行测试并分析信息之后，瓶颈服务器可以转换为生产服务器，或者可以成为焦点不同的测试服务器。
Performance is usually judged relative to rivals or to an industry benchmark, encouraging banks to mimic each other’s risk-taking, even if in the long run it benefits no one.
对于公司表现的判断往往是基于竞争对手的表现或者行业标准，这种判断鼓励银行去模仿彼此的冒险行为，即使从长远来看，这对任何公司都没有好处。
Immediately following the baseline test we run the benchmark under the same conditions.
基线测试后，我们立即在同样条件下运行基准测试。
You could run benchmark with 1GB log files and 2GB and see if there is any performance benefit.
可以运行基准测试来检查1GB 大小的日志相对2GB有什么好处。
PC performance and stability issue in multithreaded simple benchmark. How to make each thread run on separate core?
电脑的性能和稳定性的问题，在多线程的简单的基准。如何在不同的核心使每个线程的运行？
This paper presents a way for a large accessory to measure run-out tolerance after changing primary benchmark.
介绍了一种大型零件在原基准改变后测量端面跳动公差的方法。
This paper presents a way for a large accessory to measure run-out tolerance after changing primary benchmark.
介绍了一种大型零件在原基准改变后测量端面跳动公差的方法。
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