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The coupled cluster singles and doubles (CCSD) algorithm in the NWChem software package has been optimized to alleviate the communication bottleneck. This optimization provided a 2-fold to 5-fold speedup in the CCSD iteration time depending on the problem size and available memory, and improved the CCSD scaling to 20 000 nodes of the NCSA Blue Waters supercomputer. On 20 000 XE6 nodes of Blue Waters, a complete conventional CCSD(T) calculation of a system encountering 1042 basis functions and 103 occupied correlated orbitals obtained a performance of 0.32 petaflop/s and took 5 h and 24 min to complete. The reported time and performance included all stages of the calculation from initialization to termination for iterative single and double excitations as well as perturbative triples correction. In perturbative triples alone, the computation sustained a rate of 1.18 petaflop/s. The CCSD and (T) phases took approximately (3)/4 and (1)/4 of the total time to solution, respectively, showing that CCSD is the most time-consuming part at the large scale. The MP2, CCSD, and CCSD(T) computations in 6-311++G** basis set performed on guanine-cytosine deoxydinucleotide monophosphate probed the conformational energy difference between the A- and B-conformations of single stranded DNA. Good agreement between MP2 and coupled cluster methods has been obtained, suggesting the utility of MP2 for conformational analysis in these systems. The study revealed a significant discrepancy between the quantum mechanical and classical force field predictions, suggesting a need to improve the dihedral parameters.

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Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.