Remote Sensing Powered Containers for Big Data and AI/ML Analysis: Accelerating Science, Standardizing Operations

Caraballo-Vega, J.A.; Smith, Noah S. Oller; Carroll, M.; Carriere, L.; Jasen, John E.; Le, Minh Tri; Li, Jian; Peck, Kenneth E.; Strong, Savannah L.; Tamkin, G.; Thompson, Matthew A.; Thompson, John H.

doi:10.1109/igarss46834.2022.9883436

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Through this mechanism, most instructions, including memory access and vectorized floating-point operations, are completed within a single thread cycle (equivalent to 6 clock cycles). Each thread represents an entirely independent program, without constraints on group execution or lockstep program execution across threads, thereby ensuring high SRAM bandwidth [10,30].…”

Section: Graphcore Ipu and Platform Architecturementioning

confidence: 99%

Performance Evaluation of Parallel Graphs Algorithms Utilizing Graphcore IPU

Gepner,

Kocot,

Paprzycki

et al. 2024

Electronics

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Recent years have been characterized by increasing interest in graph computations. This trend can be related to the large number of potential application areas. Moreover, increasing computational capabilities of modern computers allowed turning theory of graph algorithms into explorations of best methods for their actual realization. These factors, in turn, brought about ideas like creation of a hardware component dedicated to graph computation; i.e., the Graphcore Intelligent Processor Unit (IPU). Interestingly, Graphcore systems are a hardware implementation of the Bulk Synchronous Parallel paradigm, which seemed to be a mostly theoretical concept from the end of last century. In this context, the question that has to be addressed experimentally is as follows: how good are Graphcore systems in comparison with standard systems that can be used to run graph algorithms, i.e., CPUs and GPUs. To provide a partial response to this broad question, in this contribution, PageRank, Single Source Shortest Path and Breadth-First Search algorithms are used to compare the performance of IPU-deployed algorithms to other parallel architectures. Obtained results clearly show that the Graphcore IPU outperforms other devices for the studied heterogeneous algorithms and, currently, provides best-in-class execution time results for a range of graph sizes and densities.

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Section: Graphcore Ipu and Platform Architecturementioning

confidence: 99%

Performance Evaluation of Parallel Graphs Algorithms Utilizing Graphcore IPU

Gepner,

Kocot,

Paprzycki

et al. 2024

Electronics

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“…• Given the large volumes of geoscience data that might be utilized with AI/ML algorithms, "containers" represent a way to make code portable across environments (Caraballo-Vega, et al, 2022).…”

Section: Module 3 Use Cases/illustrative Examplesmentioning

confidence: 99%

Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental Sciences

Stall

Cervone

Coward

et al. 2023

Preprint

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Through broad and inclusive partnerships, AGU aims to advance discovery and solution science that accelerate knowledge and create solutions that are ethical, unbiased and respectful of communities and their values. Our programs include serving as a scholarly publisher, convening virtual and in-person events and providing career support. We live our values in everything we do, including through our net zero energy renovated building in Washington, D.C. and our Ethics and Equity Center, which fosters a diverse and inclusive geoscience community to ensure responsible conduct.

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