Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed billions discrete entities. Such span a diverse array domains, including molecular dynamics, cosmology, computational fluid and geology. The scale the in those increases substantially thanks ever-increasing power high-performance computing (HPC) platforms. However, actual gains from such are often undercut by obstacles data management related storage, transfer,...

To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such can generate large amounts of scientific data, which often result in expensive costs data associated with movement storage. Lossy compression techniques have become attractive because they significantly reduce size maintain high fidelity for post-analysis. In this paper, we propose to use GPU-based lossy cosmological simulations. Our...

This paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing heuristics and thread decompositions. As compared to prior work for other parallel architectures, we find that the large number cores relatively high global memory bandwidth a GPU lead different strategies implementation. Specifically, simple uniform block partitioning is very effective GPUs our same or fewer colors than approaches distributed-memory cluster architecture. Our...

As the computation power of supercomputers increases, so does simulation size, which in turn produces orders-of-magnitude more data. Because generated data often exceed simulation's disk quota, many simulations would stand to benefit from data-reduction techniques reduce storage requirements. Such include autoencoders, compression algorithms, and sampling. Lossy can significantly but such come at expense losing information that could result incorrect post hoc analysis results. To help...

Abstract In this paper, a method for interactive direct volume rendering is proposed computing depth of field effects, which previously were shown to aid observers in and size perception synthetically generated images. The presented technique extends those benefits visualizations 3D scalar fields from CT/MRI scanners or numerical simulations. It based on incremental filtering as such does not depend any pre‐computation, thus allowing explorations volumetric data sets via on‐the‐fly editing...

In this paper we present a user study on the use of Depth Field for depth perception in Direct Volume Rendering. Rendering with Phong shading and perspective projection is used as baseline. then added to see its impact correct ordinal depth. Accuracy response time are metrics evaluate usefulness Field. The onsite has two parts: static dynamic. Eye tracking monitor gaze subjects. From our results that though does not act proper cue all conditions, it can be reinforce which feature front...

Modern supercomputers have thousands of nodes, each with CPUs and/or GPUs capable several teraflops. However, the network connecting these nodes is relatively slow, on order gigabits per second. For time-critical workloads such as interactive visualization, bottleneck no longer computation but communication. In this paper, we present an image compositing algorithm that works both CPU-only and GPU-accelerated focuses communication avoidance overlapping at expense evenly balancing workload....

Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has in workflows to reduce storage requirements minimize the impact throughput limitations while saving large snapshots high-fidelity data for post-hoc analysis. In this paper, we propose adaptively provide compression configurations compute partitions with newly designed post-analysis aware rate-quality modeling....

As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage transmission experiences exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an effective solution to address these two challenges. Concurrently, error-bounded lossy compression is recognized one most efficient approaches tackle latter issue. Despite their respective advantages, few attempts have been made investigate how AMR can...

This paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing heuristics and thread decompositions. As compared to prior work for other parallel architectures, we find that the large number cores relatively high global memory bandwidth a GPU lead different strategies implementation. Specifically, simple uniform block partitioning is very effective GPUs our same or fewer colors than approaches distributed-memory cluster architecture. Our...

This paper details a method for interactive direct volume rendering that computes ambient occlusion effects visualizations combine both volumetric and geometric primitives, specifically tube-shaped objects representing streamlines, magnetic field lines or DTI fiber tracts. The algorithm extends the recently presented directional shading model to allow of those shapes in combination with context providing 3D volume, considering mutual between structures represented by geometry. Stream tube...

Today's scientific simulations require a significant reduction of data volume because extremely large amounts they produce and the limited I/O bandwidth storage space. Error-bounded lossy compression has been considered one most effective solutions to above problem. However, little work done improve error-bounded for Adaptive Mesh Refinement (AMR) simulation data. Unlike previous that only leverages 1D compression, in this work, we propose leverage high-dimensional (e.g., 3D) each refinement...

Today's scientific simulations generate exceptionally large volumes of data, challenging the capacities available I/O bandwidth and storage space. This necessitates a substantial reduction in data volume, for which error-bounded lossy compression has emerged as highly effective strategy. A crucial metric assessing efficacy is visualization. Despite extensive research on impact visualization, there notable gap literature concerning effects visualization Adaptive Mesh Refinement (AMR) data....

In this paper, a method for interactive direct volume rendering is proposed that computes ambient occlusion effects visualizations combine both volumetric and geometric primitives, specifically tube shaped objects representing streamlines, magnetic field lines or DTI fiber tracts. The algorithm extends the recently Directional Occlusion Shading model to allow of those shapes in combination with context providing 3D volume, considering mutual between structures represented by geometry.

Figure 1.Datasets rendered by Galaxy using 64-ray cross-node ambient occlusion shadow sampling: (left) volumetric asteroid impact simulation; (center) geometric limestone karst core sample scan; (right) n-body Cosmic Web dark matter simulation. The long-range ambient-occlusion effects in Asteroid and cannot be performed conventional sort-last distributed ray tracers, where rays must stop at local data boundaries.We present Galaxy, a fully asynchronous parallel rendering engine geared towards...

Modern supercomputers have very powerful multi-core CPUs. The programming model on these supercomputer is switching from pure MPI to for inter-node communication, and shared memory threads intra-node communication. Consequently the bottleneck in most systems no longer computation but communication between nodes. In this paper, we present a new compositing algorithm hybrid parallelism that focuses avoidance overlapping with at expense of evenly balancing workload. has three stages: direct...

In situ analysis is now commonly used in many simulations. Prior to a simulation being run, the user specifies what should be run and results of that are saved disk while running. However, it sometimes hard know before has started, often saving full datasets for later. this paper, we present framework, Seer, allows users change, real time, run. Moreover Seer also each their own analysis, ”personalized workspace”, independent other. Finally can easily integrated into simulations plays well...

Algorithms for sort-last parallel volume rendering on large distributed memory machines usually divide a dataset equally across all nodes rendering. Depending the features that user wants to see in dataset, will rarely finish at same time. Existing compositing algorithms do not often take this into consideration, which can lead significant delays when are wait other still In paper, we present an image algorithm uses spatial and temporal awareness dynamically schedule exchange of regions...

Today's scientific simulations require significant data volume reduction because of the enormous amounts produced and limited I/O bandwidth storage space. Error-bounded lossy compression has been considered one most effective solutions to above problem. However, little work done improve error-bounded for Adaptive Mesh Refinement (AMR) simulation data. Unlike previous that only leverages 1D compression, in this work, we propose an approach (TAC) leverage high-dimensional SZ each refinement...

Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality data both uniform AMR simulations....

Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed billions discrete entities. Such span a diverse array domains, including molecular dynamics, cosmology, computational fluid and geology. The scale the in those increases substantially thanks ever-increasing power high-performance computing (HPC) platforms. However, actual gains from such are often undercut by obstacles data management related storage, transfer,...

Cosmology simulations are among some of the largest currently run on supercomputers, generating terabytes to petabytes data for each run. Consequently, scien-tists seeking reduce amount storage needed while preserving enough quality analysis and visualization data. One most commonly used techniques cosmology is volume rendering. Here, we investigate how different types lossy error-bound compression algorithms affect volume-rendered images generated from reconstructed datasets. We also...