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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
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData47090.2019.9006013
