This paper presents the implementation and performance of the highly accurate CCSD(T) quantum chemistry method on the Intel®Xeon Phi coprocessor within the context of the NWChem computational chemistry package. The widespread use of highly correlated methods in electronic structure calculations is contingent upon the interplay between advances in theory and the possibility of utilizing the ever-growing computer power of emerging heterogeneous architectures. We discuss the design decisions of our implementation as well as the optimizations applied to the compute kernels and data transfers between host and coprocessor. We show the feasibility of adopting the Intel®Many Integrated Core Architecture and the Intel Xeon Phi coprocessor for developing efficient computational chemistry modeling tools. Remarkable scalability is demonstrated by benchmarks. Our solution scales up to a total of 62560 cores with the concurrent utilization of Intel®Xeon®processors and Intel Xeon Phi coprocessors.

The development of reliable tools for excited-state simulations is very important for understanding complex processes in the broad class of light harvesting systems and optoelectronic devices. Over the last years we have been developing equation of motion coupled cluster (EOMCC) methods capable of tackling these problems. In this paper we discuss the parallel performance of EOMCC codes which provide accurate description of excited-state correlation effects. Two aspects are discussed in detail: (1) a new algorithm for the iterative EOMCC methods based on improved parallel task scheduling algorithms, and (2) parallel algorithms for the non-iterative methods describing the effect of triply excited configurations. We demonstrate that the most computationally intensive non-iterative part can take advantage of 210,000 cores of the Cray XT5 system at the Oak Ridge Leadership Computing Facility (OLCF), achieving over 80% parallel efficiency. In particular, we demonstrate the importance of the computationally demanding non-iterative many-body methods in matching experimental level of accuracy for several porphyrin-based systems.

Water is ubiquitous on our planet and plays an essential role in several key chemical and biological processes. Accurate models for water are crucial in understanding, controlling and predicting the physical and chemical properties of complex aqueous systems. Over the last few years we have been developing a molecular-level based approach for a macroscopic model for water that is based on the explicit description of the underlying intermolecular interactions between molecules in water clusters. In the absence of detailed experimental data for small water clusters, highly-accurate theoretical results are required to validate and parameterize model potentials. As an example of the benchmarks needed for the development of accurate models for the interaction between water molecules, for the most stable structure of (H2O)20 we ran a coupled-cluster calculation on the ORNL's Jaguar petaflop computer that used over 100 TB of memory for a sustained performance of 487 TFLOP/s (double precision) on 96,000 processors, lasting for 2 hours. By this summer we will have studied multiple structures of both (H2O)20 and (H2O)24 and completed basis set and other convergence studies and anticipate the sustained performance rising close to 1 PFLOP/s.

This paper proposes using shared memory for caching latency sensitive distributed data structures on symmetric multiprocessor nodes of clusters connected with commodity networks. Shared memory mirroring is a hybrid approach that replicates data across cluster nodes and distributes data within each node. The user is responsible for managing consistency of the data cached within the mirrored data structures. The method is shown to be very effective in improving the performance of a real scientific application on clusters equipped with Ethernet, Myrinet, or quadric networks.

Introduction In the past five years, the Myrinet network has received much attention in the literature on high-performance communication. Multiple projects have focused on developing efficient messaging middleware (e.g., AM[1], PM[2], BIP[3], and HPVM FM [4]) by exploiting the programmable network interface card (NIC) of Myrinet. The most common use of these interfaces has been support of MPI and other internal research projects. The HPVM project used its FM system to implement MPI and other programming interfaces including two one-sided: the Cray SHMEM and Global Arrays. In the last few years, because of the good scalability and rather moderate cost, Myrinet has become the primary network for building medium and large-scale clusters based on commodity processing nodes (e.g., Intel or Alpha Linux systems). To our best knowledge, with the exception of the NCSA Windows NT SuperCluster that operates in the HPVM environment, the majority of medium and large Myrinet-based clusters used in