machinelearning (ML) has emerged as a tool for understanding data at scale. However, this new methodology comes at a cost because ML requires the use of even more hpc resources to generate ML algorithms. In addition ...
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ISBN:
(纸本)9781728160184
machinelearning (ML) has emerged as a tool for understanding data at scale. However, this new methodology comes at a cost because ML requires the use of even more hpc resources to generate ML algorithms. In addition to the compute resources required to develop ML algorithms, ML does not sidestep one of the biggest challenges on leading-edge hpc systems: the increasing gap between compute performance and I/O bandwidth. this has led to a strong push towards in situ, processing the data as it is generated, strategies to mitigate the I/O bottleneck. Unfortunately, there are no in situ frameworks dedicated to coupling scientific visualization and ML at scale to develop ML algorithms for scientific visualization. To address the ML and in situ visualization gap, we introduce PAVE. PAVE is an in situ framework which addresses the data management needs between visualisation and machinelearning tasks. We demonstrate our framework with a case study that accelerates physically-based light rendering, path-tracing, through the use of a conditional Generative Adversarial neural Network (cGAN). PAVE couples the training over path-traced images resulting in a generative model able to produce scene renderings with accurate light transport and global illumination of a quality comparable to offline approaches in a more efficient manner.
Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However estimating the amount of Oil In Place (OIP) relies on compu...
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Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such data reduction techniques are required to reduce this set down to a much smaller yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. the result of this work is an approach which enables us to describe the entire state space using only 0.5% of the models, along with a series of lessons learnt. the techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the hpc community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.
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