94 research outputs found
Proto-Plasm: parallel language for adaptive and scalable modelling of biosystems
This paper discusses the design goals and the first developments of
Proto-Plasm, a novel computational environment to produce libraries
of executable, combinable and customizable computer models of natural and
synthetic biosystems, aiming to provide a supporting framework for predictive
understanding of structure and behaviour through multiscale geometric modelling
and multiphysics simulations. Admittedly, the Proto-Plasm platform is
still in its infancy. Its computational framework—language, model library,
integrated development environment and parallel engine—intends to provide
patient-specific computational modelling and simulation of organs and biosystem,
exploiting novel functionalities resulting from the symbolic combination of
parametrized models of parts at various scales. Proto-Plasm may define
the model equations, but it is currently focused on the symbolic description of
model geometry and on the parallel support of simulations. Conversely, CellML
and SBML could be viewed as defining the behavioural functions (the model
equations) to be used within a Proto-Plasm program. Here we exemplify
the basic functionalities of Proto-Plasm, by constructing a schematic
heart model. We also discuss multiscale issues with reference to the geometric
and physical modelling of neuromuscular junctions
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Scene representation networks (SRNs) have been recently proposed for
compression and visualization of scientific data. However, state-of-the-art
SRNs do not adapt the allocation of available network parameters to the complex
features found in scientific data, leading to a loss in reconstruction quality.
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN)
and propose a domain decomposition training and inference technique for
accelerated parallel training on multi-GPU systems. We also release an
open-source neural volume rendering application that allows plug-and-play
rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses
multiple spatially adaptive feature grids that learn where to be placed within
the domain to dynamically allocate more neural network resources where error is
high in the volume, improving state-of-the-art reconstruction accuracy of SRNs
for scientific data without requiring expensive octree refining, pruning, and
traversal like previous adaptive models. In our domain decomposition approach
for representing large-scale data, we train an set of APMGSRNs in parallel on
separate bricks of the volume to reduce training time while avoiding overhead
necessary for an out-of-core solution for volumes too large to fit in GPU
memory. After training, the lightweight SRNs are used for realtime neural
volume rendering in our open-source renderer, where arbitrary view angles and
transfer functions can be explored. A copy of this paper, all code, all models
used in our experiments, and all supplemental materials and videos are
available at https://github.com/skywolf829/APMGSRN.Comment: Accepted to IEEE VIS 202
A Survey of GPU-Based Large-Scale Volume Visualization
This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera-, and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e., “output-sensitive” algorithms and system designs. This leads to recent outputsensitive approaches that are “ray-guided,” “visualization-driven,” or “display-aware.” In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we discuss in this survey.Engineering and Applied Science
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