Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

No abstract

No abstract

mWe describe the design philosophy, structure, and supporting tool kits of the NWChem computational chemistry package. The primary purpose of this effort was to develop efficient parallel algorithms for a broad range of methods commonly used in computational chemistry. To facilitate this, we developed a shared nonuniform access memory model which simplifies parallel programming while at the same time providing for portability across both distributed-and shared-memory machines. In addition to this specific focus on parallelization, a substantial effort has been made to simplify the general problem of large-scale software development, which is common to many research groups. We find that this simplification can be achieved through judicious use of ideas from the computer science field of software engineering in the design and implementation modeling software applications that provide 10-100 times more computing capability than has been available with conventional supercomputers. While increases in raw computing power alone will greatly expand the range of problems that can be treated by theoretical chemistry methods, a significant investment in new algorithms is needed

data elements per node. We also introduce a hybrid algorithm that is not asymptotically optimal, but in Several algorithms are discussed for implementing practice outperforms the others for wide ranges of n global combine (summation) oll distributed memory and p. In addition, we show that a different algocomputers using a two-dimensional mesh interconnect rithm, optimized for a hypercube, is often the fastest with wormhole routing. These include algorithms that method for meshes containing p = 2d nodes, if care is are asymptotically optimal for short vectors (O(log(p)) taken to order the communications to minimize net-/or p processing nodes) and /or long vectors (O(n) work contention. The state of the art for performing /or n data elements per node), as well as hybrid ai-the global combine on hypercubes is described in [5]. gorithms that are superior for intermediate n. Per-Earlier work on 2-D mesh combining is reported in [2]. formance models are developed that include the effects The global combine operation can be stated as folof link conflicts and other characteristics of the un-lows: Given p processing nodes, each of which owns a derlying communication system. The models are val-vector of data, xi, of length n, a global combine forms p-1 idated using experimental data from the Intel Touchy = (9i= 0 (xi), where $ is a commutative and associastone DELTA computer. Each of the combine algo-tive operator defined on the elements of the vectors. rithms is shown to be superior under some circum-In this paper we choose to have a copy of the resulting stances, y end up on every node.