The growing scale of high-performance computing systems increasingly enables scientists to develop more complex applications as in situ workflows composed of coupled simulation and analysis codes. It is therefore important that workflow programming systems and runtime middleware support the composition and execution of these complex applications intuitively and efficiently. The scientific computing community has put significant effort into purpose-built coupled simulation codes that have been optimized for specialized use cases. However, the development effort involving the coupling of established codes has been largely ad hoc. The Benesh programming system was recently proposed to support the development of coupled simulation workflows from existing code bases. Benesh allows a shared data model to be defined across established codes, so that they can be interfaced in a flexible, coupled workflow. In this paper, we develop Benesh into a workflow development framework. Using Benesh, we develop workflow data model and data exchange definitions for coupled, in situ workflows. We evaluate the cost of development using Benesh in terms of development time and overhead, showing that Benesh offers development advantages without undue impact upon workflow performance.

Summary Reliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

The Parallel Unstructured Mesh Infrastructure (PUMI) is designed to support the representation of, and operations on, unstructured meshes as needed for the execution of mesh-based simulations on massively parallel computers. In PUMI, the mesh representation is complete in the sense of being able to provide any adjacency of mesh entities of multiple topologies in O(1) time, and fully distributed to support relationships of mesh entities across multiple memory spaces in a manner consistent with supporting massively parallel simulation workflows. PUMI's mesh maintains links to the high-level model definition in terms of a model topology as produced by CAD systems, and is specifically designed to efficiently support evolving meshes as required for mesh generation and adaptation. To support the needs of parallel unstructured mesh simulations, PUMI also supports a specific set of services such as the migration of mesh entities between parts while maintaining the mesh adjacencies, maintaining read-only mesh entity copies from neighboring parts (ghosting), repartitioning parts as the mesh evolves, and dynamic mesh load balancing. Here we present the overall design, software structures, example programs, and performance results. The effectiveness of PUMI is demonstrated by its applications to massively parallel adaptive simulation workflows.

Much of the effort required to create a new simulation code goes into developing infrastructure for mesh data manipulation, adaptive refinement, design optimization, and so forth. This infrastructure is an obvious target for code reuse, except that implementations of these functionalities are typically tied to specific data structures. In this article, we describe a software component---an abstract data model and programming interface---designed to provide low-level mesh query and manipulation support for meshing and solution algorithms. The component’s data model provides a data abstraction, completely hiding all details of how mesh data is stored, while its interface defines how applications can interact with that data. Because the component has been carefully designed to be general purpose and efficient, it provides a practical platform for implementing high-level mesh operations independently of the underlying mesh data structures. After describing the data model and interface, we provide several usage examples, each of which has been used successfully with multiple implementations of the interface functionality. The overhead due to accessing mesh data through the interface rather than directly accessing the underlying mesh data is shown to be acceptably small.

This paper introduces a general-purpose communication package built on top of MPI which is aimed at improving inter-processor communications for parallel computations characterized by large numbers of messages. The current library provides a utility for such applications based on two key attributes that are: (i) explicit consideration of the neighborhood communication pattern to avoid many-to-many calls and to reduce the number of collective calls and (ii) use of non-blocking MPI functions along with message packing to reduce actual communications in number and time. The introduction of the neighborhood communications leads to substantial reductions in the data exchange cost.

Implicit methods for partial differential equations using unstructured meshes allow for an efficient solution strategy for many real-world problems (e.g., simulation-based virtual surgical planning). Scalable solvers employing these methods not only enable solution of extremely-large practical problems but also lead to dramatic compression in time-to-solution. We present a parallelization paradigm and associated procedures that enable our implicit, unstructured flow-solver to achieve strong scalability.

One of the major obstacles in CAD is the lack of available software systems that can effectively manage the massive amounts and diverse types of data necessary to describe designs. In this paper, we focus on this problem by investigating the use of data abstraction, a software engineering principle, as a means of managing the volume and complexity of design data.