Compared to posthoc rendering, insitu rendering often generates larger numbers of images, as a result rendering performance and scalability are critical in the insitu setting. In this work we present improvements to VisIt's rendering and compositing infrastructure that deliver increased performance and scalability in both posthoc and insitu settings. We added the capability for alpha blend compositing and use it with ordered compositing when datasets have disjoint block domain decomposition to optimize the rendering of transparent geometry. We also made improvements that increase overall efficiency by reducing communication and data movement and have addressed a number of performance issues. We structured our code to take advantage of SIMD parallelization and use threads to overlap communication and compositing. We tested our improvements on a 20 core workstation using 8 cores to render geometry generated from a $256^3$ cosmology dataset and on a Cray XC31 using 512 cores to render geometry generated from a $2000^2 \times 800$ plasma dataset. Our results show that ordered compositing provides a speed up of up to $4 \times$ over the current sort first strategy. The other improvements resulted in modest speed up with one notable exception where we achieve up to $40 \times$ speed up of rendering and compositing of opaque geometry when both opaque and transparent geometry are rendered together. We also investigated the use of depth peeling, but found that the implementation provided by VTK is substantially slower,both with and without GPU acceleration, than a local camera order sort.

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

This work focuses on enabling the use of Python-based methods for the purpose of performing in situ analysis and visualization. This approach facilitates access to and use of a rapidly growing collection of Python-based, third-party libraries for analysis and visualization, as well as lowering the barrier to entry for user-written Python analysis codes. Beginning with a simulation code that is instrumented to use the SENSEI in situ interface, we present how to couple it with a Python-based data consumer, which may be run in situ, and in parallel at the same concurrency as the simulation. We present two examples that demonstrate the new capability. One is an analysis of the reaction rate in a proxy simulation of a chemical reaction on a 2D substrate, while the other is a coupling of an AMR simulation to Yt, a parallel visualization and analysis library written in Python. In the examples, both the simulation and Python in situ method run in parallel on a large-scale HPC platform.

North Atlantic tropical cyclones (TCs) have considerable interannual variability, with La Niña and the positive phase of the Atlantic Meridional Mode (AMM) tending to drive active hurricane seasons, and El Niño and the negative AMM often driving inactive seasons. Here, we analyze how active and inactive Atlantic hurricane seasons may change in the future using the high resolution Energy Exascale Earth System Model (E3SM). We performed atmosphere-only simulations forced by sea-surface temperature patterns characteristic of La Niña and the positive AMM jointly, and El Niño and the negative AMM jointly, in historical and future climates. Projected Atlantic TCs become more frequent in the future by approximately 34% during El Niño and negative AMM and by 66% during La Niña and positive AMM, with a significant increase in the portion of intense TCs. Warmer SSTs increase TC potential intensity, with reduced wind shear and increased mid-tropospheric humidity further supporting TC activity.

For in transit processing, one of the fundamental challenges is the efficient movement of data from producers to consumers. Exploiting the flexibility offered by the SENSEI generic in situ framework, we have developed a number of different in transit data transport mechanisms. In this work, we focus on the transport mechanism that leverages the HDF5 parallel I/O library, and investigate the performance characteristics of this transport mechanism. For in transit use cases at scale on HPC platforms, one might expect that an in transit data transport mechanism that uses faster layers of the storage hierarchy, such as DRAM memory, would always outperform a transport that uses slower layers of the storage hierarchy, such as an NVRAM-based persistent storage presented as a distributed file system. However, our test results show that the performance of the transport using NVRAM is competitive with the transport that uses socket-based data movement across varying levels of producer and consumer concurrency.}, booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization

Abstract. The United Nations Framework Convention on Climate Change (UNFCCC) invited the scientific community to explore the impacts of a world where anthropogenic global warming is stabilized at only 1.5 °C above preindustrial average temperatures. We present a projection of future tropical cyclone statistics for both 1.5 °C and 2.0 °C stabilized warming scenarios by direct numerical simulation using a high resolution global climate model. As in similar projections at higher warming levels, we find that even at these low warming levels the most intense tropical cyclones becomes more frequent and more intense, while simultaneously the frequency of weaker tropical storms is decreased. We also conclude that in the 1.5 °C stabilization, the effect of aerosol forcing changes complicates the interpretation of greenhouse gas forcing changes.

Atmospheric rivers (ARs) are intensive poleward moisture transport events that are essential to the global hydrological cycle and are often linked to extreme weather events. We categorize the winter North Pacific ARs into two “flavors”: wind-dominated (windy ARs) and moisture-dominated (wet ARs) using 40 years of hourly data from fifth generation of the European Centre for Medium-Range Weather Forecasts Interim Reanalysis. We compare the differences between windy ARs and wet ARs including the lifecycle characteristics (such as genesis locations and changes of meteorological elements through the lifecycle), overall AR frequency, landfall impacts, and variability. The windy ARs are more likely to occur in the midlatitudes, while wet ARs are more active in the subtropics. Windy ARs are associated with intensive surface pressure lows, where the strong pressure gradient can support the strong wind within ARs. Due to larger size and longer lifetime, wet ARs are more likely to produce more precipitation over a lifecycle. By scaling the landfalling ARs, we show that wet ARs dominate the high-category ARs (Category 4 and 5) with higher spatial frequency and more precipitation, and windy ARs have higher contributions in the lower AR categories especially over British Columbia. Windy ARs are modulated by El Niño Southern Oscillation (ENSO) teleconnections via the anomalous geopotential height and extended subtropical jet. Wet ARs are affected by the anomalous sea surface temperature over the midlatitudes related to ENSO. Sensitivity analysis with an alternate AR detection algorithm shows consistent results on AR flavors but with disagreement on the amplitude.

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness.Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x fasterthan similar tools on a typical image file.

The deadly and destructive nature of tropical cyclones (TCs) makes understanding their response to future climate change of the utmost importance. TC genesis hinges on multiple factors, including an initial disturbance. African easterly waves (AEWs) have been shown to serve as such disturbances for TCs developing in the North Atlantic. It is therefore crucial to understand the relationship between AEWs and TCs and how this relationship may be affected by climate change. In this study, we examine the AEW-TC relationship in historical and future climates using three models from the HighResMIP PRIMAVERA simulations. The AEWs and TCs were tracked in the model data using objective tracking algorithms, and AEW and TC tracks were then matched together if they were close to each other in space and time. The strength of the AEWs was measured using the eddy kinetic energy and the curvature vorticity of the waves. TC strength and intensity were measured using potential intensity and lifetime maximum 10 m windspeed. We found that future changes in the frequency of AEWs are not a good indicator of future TC activity. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates.