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The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.

As the visualization research community reorients its software to address up-coming challenges, it must successfully deal with diverse processor architectures, distributed systems, various data sources, massive parallelism, multiple input and output devices, and interactivity.

One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

Modern science is often conducted on large scale, distributed, heterogeneous and high-performance computing infrastructures. Increasingly, the scale and complexity of both the applications and the underlying execution platforms have been growing. Scientific workflows have emerged as a flexible representation to declaratively express complex applications with data andcontrol dependences. However, it is extremely challengingfor scientists to execute their science workflows in a reliable and scalable way due to a lack of understanding of expected and realistic behavior of complex scientific workflows on large scale and distributed HPC systems. This is exacerbated by failures and anomalies in largescale systems and applications, which makes detecting, analyzing and acting on anomaly events challenging. In this work, we present a prototype of an end-to-end system for modeling and diagnosing the runtime performance of complex scientific workflows. We interfaced the Pegasus workflow management system, Aspen performance modeling, monitoring and anomaly detection into an integrated framework that not only improves the understanding of complex scientific applications on large scale complex infrastructure, but also detects anomalies and supports adaptivity. We present a black box modeling tool, a comprehensive online monitoring system, and anomaly detection algorithms that employ the models and monitoring data to detect anomaly events. We present an evaluation of the system with a Spallation Neutron Source workflow as a driving use case.

The Keeneland project's goal is to develop and deploy an innovative, GPU-based high-performance computing system for the NSF computational science community.

This article considers trends in heterogeneous system design, particularly for GPUs. Using the Keeneland Initial Delivery System, the authors examine the performance implications of increased parallelism and specialized hardware on parallel scientific applications. They examine how nonuniform data-transfer performance across the node-level topology can impact performance. Finally, they help users of GPU-based systems avoid performance problems related to this nonuniformity.

Current architectural trends on supercomputers have dramatic increases in the number of cores and available computational power per die, but this power is increasingly difficult for programmers to harness effectively. High-level language constructs can simplify programming many-core devices, but this ease comes with a potential loss of processing power, particularly for cross-platform constructs. Recently, scientific visualization packages have embraced language constructs centering around data parallelism, with familiar operators such as map, reduce, gather, and scatter. Complete adoption of data parallelism will require that central visualization algorithms be revisited, and expressed in this new paradigm while preserving both functionality and performance. This investment has a large potential payoff: portable performance in software bases that can span over the many architectures that scientific visualization applications run on. With this work, we present a method for ray tracing consisting of entirely of data parallel primitives. Given the extreme computational power on nodes now prevalent on supercomputers, we believe that ray tracing can supplant rasterization as the work-horse graphics solution for scientific visualization. Our ray tracing method is relatively efficient, and we describe its performance with a series of tests, and also compare to leading-edge ray tracers that are optimized for specific platforms. We find that our data parallel approach leads to results that are acceptable for many scientific visualization use cases, with the key benefit of providing a single code base that can run on many architectures.