  Optimal use of computing resources requires extensive coding, tuning and
benchmarking. To boost developer productivity in these time consuming tasks, we
introduce the Experimental Linear Algebra Performance Studies framework
(ELAPS), a multi-platform open source environment for fast yet powerful
performance experimentation with dense linear algebra kernels, algorithms, and
libraries. ELAPS allows users to construct experiments to investigate how
performance and efficiency vary depending on factors such as caching,
algorithmic parameters, problem size, and parallelism. Experiments are designed
either through Python scripts or a specialized GUI, and run on the whole
spectrum of architectures, ranging from laptops to clusters, accelerators, and
supercomputers. The resulting experiment reports provide various metrics and
statistics that can be analyzed both numerically and visually. We demonstrate
the use of ELAPS in four concrete application scenarios and in as many
computing environments, illustrating its practical value in supporting critical
performance decisions.
