The problem of efficiently feeding processing elements and finding ways to reduce data movement is pervasive in computing. Efficient modeling of both temporal and spatial locality of memory references is invaluable in identifying superfluous data movement in a given application.
To this end, we present a new way to infer both spatial and temporal locality using reuse distance analysis. This is accomplished by performing reuse distance analysis at different data block granularities: specifically, 64B, 4KiB, and 2MiB sizes. This process of simultaneously observing reuse distance with multiple granularities is called multi-spectral reuse distance. This approach allows for a qualitative analysis of spatial locality, through observing the shifting of mass in an application's reuse signature at different granularities. Furthermore, the shift of mass is empirically measured by calculating the Earth Mover's Distance between reuse signatures of an application.
From the characterization, it is possible to determine how spatially dense the memory references of an application are based on the degree to which the mass has shifted (or not shifted) and how close (or far) the Earth Mover's Distance is to zero as the data block granularity is increased. It is also possible to determine an appropriate page size from this information, and whether or not a given page is being fully utilized. From the applications profiled, it is observed that not all applications will benefit from having a larger page size. Additionally, larger data block granularities subsuming smaller ones suggest that larger pages will allow for more spatial locality exploitation, but examining the memory footprint will show whether those larger pages are fully utilized or not.
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Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities. To construct this geometry we define a time-inhomogemeous diffusion process that effectively condenses data points together to uncover nested groupings at larger and larger granularities. This inhomogeneous process creates a deep cascade of intrinsic low pass filters on the data affinity graph that are applied in sequence to gradually eliminate local variability while adjusting the learned data geometry to increasingly coarser resolutions. We provide visualizations to exhibit our method as a “continuously-hierarchical” clustering with directions of eliminated variation highlighted at each step. The utility of our algorithm is demonstrated via neuronal data condensation, where the constructed multiresolution data geometry uncovers the organization, grouping, and connectivity between neurons.
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- Award ID(s):
- 1845856
- NSF-PAR ID:
- 10165725
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 2624 to 2633
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.
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We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed tree-structured encoders can be used to classify shapes and outperform existing point-based architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData47090.2019.9006013
