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We present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms,...
Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating ‘Big Data’ from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the ‘Big Data’ hardware and software...
In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal...
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