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The power of parallelization
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The power of parallelization
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Whether it s optimizing the speed at which testers can operate or increasing the performance of a production application, the common theme of cloud computing here is parallelization. By now, you probably know that in the world of computing performance, parallelization is king. Thanks to the work done by companies such as Intel and AMD, which now ship almost exclusively multicore chips, most consumers enjoy the same benefits scientists have had for years with supercomputers. Everything from photo manipulation to super-realistic 3D video games to software-compilation tasks has been dramatically improved through the use of parallel computing techniques. And yet when it comes to software development and testing, we re still remarkably single-threaded in the way we operate. Whether it s automated unit tests, which almost always run in sequence, or the agile-less inefficiencies that organizations unknowingly employ, such as forcing their test team to wait idle for the next release candidate to be pushed to the testing environment, the software industry has been surprisingly slow to use parallelization to improve its own internal processes. Perhaps it s because we re so maniacally dedicated to making our users have better experiences that we selflessly put off improving our own processes. Clearly the software industry understands and can take advantage of parallel computing. Ask any PlayStation or XBox game developer who routinely takes advantage of the multiple cores in those platforms. In a way, that s what s happening. But usually, the motivation is more about cost savings and corner cutting than about being customer focused. Convincing management that you should spend more time building new features is always easier than arguing for time to speed up your unit tests. Consider figure 7.4, which shows how you can augment limited internal resources (personnel and hardware alike) with cloud-based resources, all for the purpose of testing against your test environment. CPUs have been gaining more cores in recent years; many desktop-class systems now routinely ship with eight processor cores. There s an opportunity in the cloud to make a generational leap in the number of resources you can utilize in parallel. You no longer have to look within your own organization to try to achieve a 5X or 10X increase in throughput; rather, you can look to the cloud and achieve a 100X or greater throughput increase. This isn t limited to raw CPU resources, either, although that s a huge area that benefits testing. You ll find new growth in cloud-based services that employ real people to do specialized tasks on demand, paying as you go, following many of the same principles of cloud computing. Let s explore the various types of testing and see how they can be improved by using cloud-based resources in parallel.
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Almost every software project has some number of unit tests. The quantity varies greatly from team to team and project to project, but tools such as JUnit (and their xUnit equivalents in other programming languages) have become ubiquitous in software development projects.
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Cloud APIs & Services
Testing Environment
Manual Testing
Figure 7.4 How cloud resources can augment internal resources (personnel and hardware) for testing. Instead of a small staff being responsible for all manual testing, a crowd-sourcing scenario is backed up by a potentially vast number of virtual servers in an elastic compute farm.
Most unit tests require little in terms of complex setup and teardown logic. They re often designed to test algorithms and blocks of code that are fairly static. Because there is little to set up, they often run quickly and demand few resources. As such, many projects can run hundreds of unit tests in a few minutes. Unless you re using the parallelization features of your unit-testing framework (many of which were only added in the last year or two), you re likely running all those tests in sequence. By turning on parallelization in your unit tests, you can use up all those idle CPU cores and potentially speed up your unit test execution by 5X or 10X.
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