Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early co-design projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.

As JavaScript has become virtually omnipresent as the language for programming large and complex web applications in the last several years, we have seen an increase in interest in finding data races in client-side JavaScript. While JavaScript execution is single-threaded, there is still enough potential for data races, created largely by the nondeterminism of the scheduler. Recently, several academic efforts have explored both static and runtime analysis approaches in an effort to find data races. However, despite this, we have not seen these analysis techniques deployed in practice and we have only seen scarce evidence that developers find and fix bugs related to data races in JavaScript. In this paper we argue for a different formulation of what it means to have a data race in a JavaScript application and distinguish between benign and harmful races, affecting persistent browser or server state. We further argue that while benign races-the subject of the majority of prior workdo exist, harmful races are exceedingly rare in practice (19 harmful vs. 621 benign). Our results shed a new light on the issues of data race prevalence and importance. To find races, we also propose a novel lightweight runtime symbolic exploration algorithm for finding races in traces of runtime execution. Our algorithm eschews schedule exploration in favor of smaller runtime overheads and thus can be used by beta testers or in crowd-sourced testing. In our experiments on 26 sites, we demonstrate that benign races are considerably more common than harmful ones.

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We report the implementation of the real-time equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green's function method [J. Chem. Phys. 2020, 152, 174113] within the Tensor Algebra for Many-body Methods (TAMM) infrastructure. TAMM is a massively parallel heterogeneous tensor library designed for utilizing forthcoming exascale computing resources. The two-body electron repulsion matrix elements are Cholesky-decomposed, and we imposed spin-explicit forms of the various operators when evaluating the tensor contractions. Unlike our previous real algebra Tensor Contraction Engine (TCE) implementation, the TAMM implementation supports fully complex algebra. The RT-EOM-CC singles (S) and doubles (D) time-dependent amplitudes are propagated using a first-order Adams−Moulton method. This new implementation shows excellent scalability tested up to 500 GPUs using the Zn-porphyrin molecule with 655 basis functions, with parallel efficiencies above 90% up to 400 GPUs. The TAMM RT-EOM-CCSD was used to study core photoemission spectra in the formaldehyde and ethyl trifluoroacetate (ESCA) molecules. Simulations of the latter involve as many as 71 occupied and 649 virtual orbitals. The relative quasiparticle ionization energies and overall spectral functions agree well with available experimental results.

Tensor contraction operations in computational chemistry consume significant fractions of computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. The TAMM framework decouples the specification of the computation and the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM whereas high-performance computing developers can focus on various optimizations on the underlying constructs such as efficient data distribution, optimized scheduling algorithms, efficient use of intra-node resources (e.g., GPUs). The modular structure of TAMM allows it to be extended to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to sustainable development of tensor contraction-based methods in computational chemistry applications. We present case studies that highlight the ease of use as well as the performance and productivity gains compared to other implementations.