PIUMA: Programmable Integrated Unified Memory Architecture
High performance large scale graph analytics is essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on graph workloads. To enable efficient and scalable graph analysis, Intel developed the Programmabl...
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Cornell University Library, arXiv.org
13.10.2020
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Abstract | High performance large scale graph analytics is essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on graph workloads. To enable efficient and scalable graph analysis, Intel developed the Programmable Integrated Unified Memory Architecture (PIUMA). PIUMA consists of many multi-threaded cores, fine-grained memory and network accesses, a globally shared address space and powerful offload engines. This paper presents the PIUMA architecture, and provides initial performance estimations, projecting that a PIUMA node will outperform a conventional compute node by one to two orders of magnitude. Furthermore, PIUMA continues to scale across multiple nodes, which is a challenge in conventional multinode setups. |
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AbstractList | High performance large scale graph analytics is essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on graph workloads. To enable efficient and scalable graph analysis, Intel developed the Programmable Integrated Unified Memory Architecture (PIUMA). PIUMA consists of many multi-threaded cores, fine-grained memory and network accesses, a globally shared address space and powerful offload engines. This paper presents the PIUMA architecture, and provides initial performance estimations, projecting that a PIUMA node will outperform a conventional compute node by one to two orders of magnitude. Furthermore, PIUMA continues to scale across multiple nodes, which is a challenge in conventional multinode setups. |
Author | Aananthakrishnan, Sriram Sikora, Mariusz Szkoda, Sebastian Howard, Jason Hur, Ibrahim Fryman, Joshua B More, Ankit Petrini, Fabrizio Hoppe, Hans-Christian Ganev, Ivan Cave, Vincent Montigny, Laurent Vandriessche, Yves Klowden, Daniel S Kodiyath, MidhunChandra Landowski, Marek M Jain, Samkit Ahmed, Nesreen K Pawlowski, Robert Ossowski, Przemyslaw Smith, Shaden Wrosz, Izajasz P Heirman, Wim Cintra, Marcelo Kristof Du Bois Tayal, Sanjaya Demir, Yigit Eyerman, Stijn Pepperling, Nick Balasubramanian Seshasayee Jesmin Jahan Tithi |
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