Editorials in Proceedings:
S. Hunold, A. Legrand, L. Nussbaum:
"Introduction to REPPAR Workshop";
in: "Proceedings of the IEEE 31st International Parallel and Distributed Processing Symposium (IPDPS 2017) Workshops",
Conducting sound and reproducible experiments in parallel computing is not easy, as hardware and software architectures of current parallel computers are most often very complex. This high complexity makes it difficult-and often impossible-for computer scientists to model such systems mathematically. For that reason, scientists rely on experiments to study new parallel algorithms, different software solutions (e.g., operating systems), or novel hardware architectures. The situation in parallel computing is made even more difficult than it would be otherwise, as parallel systems are in a constant state of flux, e.g., the total core count is rapidly growing and many programming paradigms for parallel machines have emerged and are
actively being used in a hybrid fashion, e.g., MPI, OpenMP, or PGAS.
For these reasons, the workshop is concerned with experimental practices in parallel computing research. We solicit research papers and experience reports on a number of relevant topics, particularly: methods for analyzing and visualizing experimental data, best practice recommendations, results of attempts to replicate previously published experiments, and tools for experimental computational sciences. Some examples of the latter include workflow management systems, experimental testbeds, and systems for archiving and querying large data files.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.