Performance

In a nutshel performance analysis is the measuring, modeling, and tuning of software's time, space, efficiency, and accuracy.

Performance is a critical component of customer satisfaction with network-based applications. Unfortunately, accurately evaluating the performance of collaborative software that operates in extremely heterogeneous environments is difficult with traditional techniques such as modeling workloads or testing in controlled environments.

[I]t is difficult to make decisions about performance from just looking at the design. Rather, you have to actually run the code and measure performance.

An experimental approach employs the Google App Engine (GAE) for high-performance parallel computing. A generic master-slave framework enables fast prototyping and integration of parallel algorithms that are transparently scheduled and executed on the Google cloud infrastructure. Compared to Amazon Elastic Compute Cloud (EC2), GAE offers lower resource-provisioning overhead and is cheaper for jobs shorter than one hour.

Studies of computational scientists developing software for high-performance computing systems indicate that these scientists face unique software engineering issues. Previous failed attempts to transfer SE technologies to this domain haven't always taken these issues into account. To support scientific-software development, the SE community can disseminate appropriate practices and processes, develop educational materials specifically for computational scientists, and investigate the large-scale reuse of development frameworks.

[T]he availability of the components before the system is built provides early data on their performance properties.

Performance tools are a way of making systematic the work needed to carry out performance studies so that several studies can be carried out more easily and be compared in a consistent way.

A successful project effectively manages four cornerstones - schedule, cost, scope, and quality - to achieve its goals. Every project activity influences these four cornerstones. Stochastic optimization modeling factors in the uncertainties associated with project activities and provides insight into the expected project outputs as probability distributions rather than as deterministic approximations.

The availability of testing infrastructure is a major factor in product development project costs. Software virtualization is a powerful mechanism for simulating a test setup on a few desktops that would otherwise require 'real' equipment.