This special issue on Innovative research and development toward the exascale era explores new foundational and translational research toward enabling exascale computing for emerging scientific and societal challenges. Exascale computing is defined as the capability to perform 1018 operations per second. Productively harnessing such a scale of processing, storage, and networking capabilities for diverse domains— including high-performance computing (HPC) simulations, artificial intelligence (AI), and extreme data-driven computing— relies on not only revitalizing existing parallel and distributed computing technologies but also innovating new solutions. Papers in this special issue explore diverse topics in research encompassing parallel, distributed, and heterogeneous systems for exascale, including advances in applications, programming environments, runtimes, libraries, innovative algorithms, domain-specific frameworks, systems architecture, performance analysis, data processing, and networking technologies.

We present DMNetwork, a high-level package included in the PETSc library for the simulation of multiphysics phenomena over large-scale networked systems. The library aims at applications that have networked structures such as those in electrical, gas, and water distribution systems. DMNetwork provides data and topology management, parallelization for multiphysics systems over a network, and hierarchical and composable solvers to exploit the problem structure. DMNetwork eases the simulation development cycle by providing the necessary infrastructure through simple abstractions to define and query the network components. This article presents the design of DMNetwork, illustrates its user interface, and demonstrates its ability to solve multiphysics systems, such as an electric circuit, a network of power grid and water subnetworks, and transient hydraulic systems over large networks with more than 2 billion variables on extreme-scale computers using up to 30,000 processors.

The Portable, Extensible Toolkit for Scientific computing (PETSc), which focuses on the scalable solution of problems based on partial differential equations, now incorporates new components that allow full compos ability of solvers for multiphysics and multilevel methods. Through strong encapsulation, we achieve arbitrary, dynamic composition of hierarchical methods for coupled problems and allow customization of all components in composite solvers. For example, we support block decompositions with nested multigrid as well as multigrid on the fully coupled system with block-decomposed smoothers. This paper provides an overview of PETSc's new multiphysics capabilities, which have been used in parallel applications including lithosphere dynamics, subduction and mantle convection, ice sheet dynamics, subsurface reactive flow, fusion, mesoscale materials modeling, and power networks.

Coupling separately developed codes offers an attractive method for increasing the accuracy and fidelity of the computational models. Examples include the earth sciences and fusion integrated modeling. This paper describes the Framework Application for Core-Edge Transport Simulations (FACETS).

Automating the process of parallel performance experimentation, analysis, and problem diagnosis can enhance environments for performance-directed application development, compilation, and execution. This is especially true when parametric studies, modeling, and optimization strategies require large amounts of data to be collected and processed for knowledge synthesis and reuse. This paper describes the integration of the PerfExplorer performance data mining framework with the OpenUH compiler infrastructure. OpenUH provides auto-instrumentation of source code for performance experimentation and PerfExplorer provides automated and reusable analysis of the performance data through a scripting interface. More importantly, PerfExplorer inference rules have been developed to recognize and diagnose performance characteristics important for optimization strategies and modeling. Three case studies are presented which show our success with automation in OpenMP and MPI code tuning, parametric characterization, Pand power modeling. The paper discusses how the integration supports performance knowledge engineering across applications and feedback-based compiler optimization in general.

Challenges in the scalable solution of large-scale optimization problems include the development of innovative algorithms and efficient tools for parallel data manipulation. This article discusses two complementary toolkits from the collection of Advanced CompuTational Software (ACTS), namely, Global Arrays (GA) for parallel data management and the Toolkit for Advanced Optimization (TAO), which have been integrated to support large-scale scientific applications of unconstrained and bound constrained minimization problems. Most likely to benefit are minimization problems arising in classical molecular dynamics, free energy simulations, and other applications where the coupling among variables requires dense data structures. TAO uses abstractions for vectors and matrices so that its optimization algorithms can easily interface to distributed data management and linear algebra capabilities implemented in the GA library. The GA/TAO interfaces are available both in the traditional library mode and as components compliant with the Common Component Architecture (CCA). We highlight the design of each toolkit, describe the interfaces between them, and demonstrate their use.

Abstract The Common Component Architecture (CCA) is a component model for high‐performance computing, developed by a grass‐roots effort of computational scientists. Although the CCA is usable with CORBA‐like distributed‐object components, its main purpose is to set forth a component model for high‐performance, parallel computing. Traditional component models are not well suited for performance and massive parallelism. We outline the design pattern for the CCA component model, discuss our strategy for language interoperability, describe the development tools we provide, and walk through an illustrative example using these tools. Performance and scalability, which are distinguishing features of CCA components, affect choices throughout design and implementation. Copyright © 2005 John Wiley & Sons, Ltd.

We analyze the performance and scalabilty of algorithms for the solution of large optimization problems on high-performance parallel architectures. Our case study uses the GPCG (gradient projection, conjugate gradient) algorithm for solving bound-constrained convex quadratic problems. Our implementation of the GPCG algorithm within the Toolkit for Advanced Optimization (TAO) is available for a wide range of high-performance architectures and has been tested on problems with over 2.5 million variables. We analyze the performance as a function of the number of variables, the number of free variables, and the preconditioner. In addition, we discuss how the software design facilitates algorithmic comparisons.

Describes work in progress to develop a standard for interoperability among high-performance scientific components. This research stems from the growing recognition that the scientific community needs to better manage the complexity of multidisciplinary simulations and better address scalable performance issues on parallel and distributed architectures. The driving force for this is the need for fast connections among components that perform numerically intensive work and for parallel collective interactions among components that use multiple processes or threads. This paper focuses on the areas we believe are most crucial in this context, namely an interface definition language that supports scientific abstractions for specifying component interfaces and a port connection model for specifying component interactions.