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A resurgence in neuromorphic architectures enabling remote sensing computation

Proceedings - 2019 IEEE Space Computing Conference, SCC 2019

Vineyard, Craig M.; Severa, William M.; Kagie, Matthew J.; Scholand, Andrew J.; Hays, Park E.

Technological advances have enabled exponential growth in both sensor data collection, as well as computational processing. However, as a limiting factor, the transmission bandwidth in between a space-based sensor and a ground station processing center has not seen the same growth. A resolution to this bandwidth limitation is to move the processing to the sensor, but doing so faces size, weight, and power operational constraints. Different physical constraints on processor manufacturing are spurring a resurgence in neuromorphic approaches amenable to the space-based operational environment. Here we describe historical trends in computer architecture and the implications for neuromorphic computing, as well as give an overview of how remote sensing applications may be impacted by this emerging direction for computing.

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TYPE Conference Poster YEAR 2019
DOIOSTIScopus

Investigating Fairness in Disaggregated Non-Volatile Memories

Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI

Kommareddy, Vamsee R.; Hughes, Clayton; Hammond, Simon; Awad, Amro

Many applications have growing demands for memory, particularly in the HPC space, making the memory system a potential bottleneck of next-generation computing systems. Sharing the memory system across processor sockets and nodes becomes a compelling argument given that memory technology is scaling at a slower rate than processor technology. Moreover, as many applications rely on shared data, e.g., graph applications and database workloads, having a large number of nodes accessing shared memory allows for efficient use of resources and avoids duplicating huge files, which can be infeasible for large graphs or scientific data. As new memory technologies come on the market, the flexibility of upgrading memory and system updates become major a concern, disaggregated memory systems where memory is shared across different computing nodes, e.g., System-on-Chip (SoC), is expected to become the most common design/architecture on memory-centric systems, e.g., The Machine project from HP Labs. However, due to the nature of such systems, different users and applications compete for the available memory bandwidth, which can lead to severe contention due to memory traffic from different SoCs. In this paper, we discuss the contention problem in disaggregated memory systems and suggest mechanisms to ensure memory fairness and enforce QoS. Our simulation results show that employing our proposed QoS techniques can speed up memory response time by up to 55%.

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TYPE Conference Poster YEAR 2019
DOIOSTIScopus

A vision for managing extreme-scale data hoards

Proceedings - International Conference on Distributed Computing Systems

Logan, Jeremy; Mehta, Kshitij; Heber, Gerd; Klasky, Scott; Kurc, Tahsin; Podhorszki, Norbert; Widener, Patrick; Wolf, Matthew

Scientific data collections grow ever larger, both in terms of the size of individual data items and of the number and complexity of items. To use and manage them, it is important to directly address issues of robust and actionable provenance. We identify three key drivers as our focus: managing the size and complexity of metadata, lack of a priori information to match usage intents between publishers and consumers of data, and support for campaigns over collections of data driven by multi-disciplinary, collaborating teams. We introduce the Hoarde abstraction as an attempt to formalize a way of looking at collections of data to make them more tractable for later use. Hoarde leverages middleware and systems infrastructures for scientific and technical data management. Through the lens of a select group of challenging data usage scenarios, we discuss some of the aspects of implementation, usage, and forward portability of this new view on data management.

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TYPE Conference Poster YEAR 2019
DOIOSTIScopus

Compatible meshfree discretization of surface PDEs

Computational Particle Mechanics

Trask, Nathaniel A.; Kuberry, Paul

Meshfree discretization of surface partial differential equations is appealing, due to their ability to naturally adapt to deforming motion of the underlying manifold. In this work, we consider an existing scheme proposed by Liang et al. reinterpreted in the context of generalized moving least squares (GMLS), showing that existing numerical analysis from the GMLS literature applies to their scheme. With this interpretation, their approach may then be unified with recent work developing compatible meshfree discretizations for the div-grad problem in Rd. Informally, this is analogous to an extension of collocated finite differences to staggered finite difference methods, but in the manifold setting and with unstructured nodal data. In this way, we obtain a compatible meshfree discretization of elliptic problems on manifolds which is naturally stable for problems with material interfaces, without the need to introduce numerical dissipation or local enrichment near the interface. As a result, we provide convergence studies illustrating the high-order convergence and stability of the approach for manufactured solutions and for an adaptation of the classical five-strip benchmark to a cylindrical manifold.

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TYPE Journal Article YEAR 2019
DOIOSTI
Results 2101–2200 of 9,998
Results 2101–2200 of 9,998