LDMS Streams Integration: For Run Time Diagnosis of HPC Application I/O Performance.
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This paper proposes functional programming frameworks for the design of highly reliable multiprocessor systems. In contrast to imperative programming environments, a functional environment offers elegant, relatively simple, and efficient solutions to concurrent error detection and recovery problems in multiprocessors. Specific fault tolerance mechanisms for upset exposure, fault containment, secure task assignment, and recovery are developed for a class of applicative multiprocessor architectures. Verification of abstract behavioral characteristics of applicative tasks is used for exposing faults during the execution of tasks. The fault containment mechanism is based on isolation of stack and heap segments of tasks. A protocol for secure task assignment is defined between system components. The architecture permits incremental, distributed, and asynchronous backups of system state. Finally, recovery is accomplished, even in the worst cases, by reexecution of a small number of tasks.
Functional programming frameworks for the design of highly reliable multiprocessor systems are proposed. In contrast to imperative programming environments, a functional environment offers elegant, relatively simple, and efficient solutions to concurrent error detection and recovery problems in multiprocessors. Specific fault tolerance mechanisms for upset exposure, fault containment, secure task assignment, and recovery are developed for a class of applicative multiprocessor architectures. Verification of abstract behavioral characteristics of applicative tasks is used for exposing faults during the execution of tasks. The fault containment mechanism is based on isolation of stack and heap segments of tasks. A protocol for secure task assignment is defined between system components. The architecture permits incremental, distributed, and asynchronous backups of system state. Finally, recovery is accomplished, even in the worst cases, by re-execution of a small number of tasks.
Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract