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DTSTART;TZID=America/Los_Angeles:20230602T130000
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SUMMARY:Saeed Kargar: Hamming Tree: The case for Energy-Aware Indexing for NVMs
DESCRIPTION:Zoom Link: https://uci.zoom.us/j/8045933305\n\nAbstract\nNVM technologies play a crucial role in data storage solutions as well as in battery-powered mobile and IoT devices. However\, the challenges of wear-out and energy efficiency need to be addressed for the widespread adoption of NVM. In this presentation\, I will discuss our research endeavors aimed at enhancing various aspects of NVMs and seamlessly integrating these technologies into the memory hierarchy.I will particularly focus on our latest work\, “Hamming Tree\,” which recently got accepted at SIGMOD 2023. The Hamming Tree introduces a novel software-level memory-aware solution designed to intelligently select the memory segment for write operations\, thereby minimizing bit flipping. By reducing bit flips\, we can significantly improve energy consumption and enhance the write endurance of NVMs.To demonstrate the effectiveness of the Hamming Tree approach\, we conducted real evaluations on an Optane memory device. The results revealed substantial improvements in both energy consumption and write endurance for NVMs. These findings underscore the practical benefits that can be achieved by implementing the Hamming Tree technique in NVM technologies.\n\nBio\nSaeed is a sixth-year PhD student at UCSC\, under the supervision of Professor Faisal Nawab. His main research area focuses on storage systems\, Non-Volatile Memory (NVM) technology\, and machine learning for systems.
URL:https://isg.ics.uci.edu/event/saeed-kargar-hamming-tree-the-case-for-energy-aware-indexing-for-nvms/
LOCATION:DBH 4011
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DTSTART;TZID=America/Los_Angeles:20230609T130000
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UID:1582-1686315600-1686319200@isg.ics.uci.edu
SUMMARY:Hari Kishore Chaparala: When (Apache) AsterixDB Hit An (Apache) Iceberg
DESCRIPTION:Abstract\nApache Iceberg is an open-source table format with rich data management capabilities\, including schema evolution\, time travel\, and efficient data pruning. It offers a reliable foundation for storing and organizing data in a data lake environment. Iceberg specification allows multiple query engines to safely operate on the same data simultaneously. In this talk\, we see how we have introduced Apache AsterixDB to the family of query engines that support Iceberg tabe format specification. Apache AsterixDB is an open-source scalable Big Data Management System (BDMS) targeted to efficiently handle large amounts of semi-structured data. AsterixDB uses Hyracks\, a partitioned-parallel platform to perform data-intensive computations and analytics. With AsterixDB’s highly parallel execution capabilities and rich analytics support through SQL++ and flexible data model\, querying on external datasets in data lake environments becomes seamless. By integrating AsterixDB with Iceberg\, we can leverage Iceberg’s data management features and AsterixDB’s querying features for efficient data lake management and advanced analytics.\n\nBio\nHari Kishore is a second-year Master of Science student in Computer Science. His main research interests are in distributed systems.
URL:https://isg.ics.uci.edu/event/when-apache-asterixdb-hit-an-apache-iceberg/
LOCATION:DBH 4011
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