November 20, 2016 – Salt Lake City, UT, USA

Experimental reproducibility is a cornerstone of the scientific method. As computing has grown into a powerful tool for scientific inquiry, computational reproducibility has been one of the core assumptions underlying scientific computing.  With "traditional" single-core CPUs, documenting a numerical result was relatively straightforward. However, hardware developments over the past several decades have made it almost impossible to ensure computational reproducibility or to even fully document a computation without incurring a severe loss of performance. This loss of reproducibility started with CPUs that used out-of-order execution to improve performance.  It has accelerated with recent architectural trends towards platforms with increasingly large numbers of processing elements, namely multicore CPUs and compute accelerators (GPUs, Intel Xeon Phi, FPGAs). Learn more at http://sc16.supercomputing.org/conference-components/workshops-sunmonfri/