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DTSTART;TZID=America/Los_Angeles:20241017T150000
DTEND;TZID=America/Los_Angeles:20241017T160000
DTSTAMP:20241014T182231Z
CREATED:20241011T160628Z
LAST-MODIFIED:20241014T182231Z
UID:16367-1729177200-1729180800@www.cecs.uci.edu
SUMMARY:Storage-Centric Computing for Genomics and Metagenomics
DESCRIPTION:Title: Storage-Centric Computing for Genomics and Metagenomics \nSpeaker: Nika Mansouri Ghiasi \nDate and Time: Thursday\, October 17th\, 3:00pm \nLocation: DBH 4011 \nAbstract: \nGenomics and metagenomics applications have enabled significant advancements in many critical areas. The exponential growth of genomic data poses unprecedented challenges in genomics and metagenomic applications. These applications suffer from significant data movement overheads from the storage system. To fundamentally address these overheads\, we make a case for storage-centric computing. \nFirst\, we propose GenStore\, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. We address the challenges of in-storage processing\, supporting reads with 1) different read lengths and error rates\, and 2) different degrees of genetic variation. Through rigorous analysis of read mapping processes\, we design low-cost hardware accelerators and data/computation flows inside a NAND flash-based SSD. Our evaluation using a wide range of real genomic datasets shows that GenStore significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05× (1.52-3.32×) for read sets with high similarity to the reference genome and 1.45-33.63× (2.70-19.2×) for read sets with low similarity to the reference genome. \nSecond\, we propose MegIS\, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design\, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning\, 2) data/computation flow coordination\, 3) storage technology-aware algorithmic optimizations\, 4) data mapping\, and 5) lightweight in-storage accelerators. MegIS’s design is flexible\, capable of supporting different types of metagenomic input datasets\, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7×–37.2× and 6.9×–100.2×\, respectively\, while matching the accuracy of the accuracy-optimized tool. MegIS achieves 1.5×–5.1× speedup compared to the state-of-the-art metagenomic hardware-accelerated (using processing-in-memory) tool\, while achieving significantly higher accuracy. \nBiography: \nNika Mansouri Ghiasi is a Ph.D. candidate in the SAFARI Research Group at ETH Zürich\, working with Professor Onur Mutlu. Her current research interests are in computer architecture\, emerging technologies\, bioinformatics\, and their interactions. \nHosted By: Prof. Alex Veidenbaum \nMore Information
URL:https://www.cecs.uci.edu/event/storage-centric-computing-for-genomics-and-metagenomics/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/10/OIP-e1728930126635.jpg
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DTSTART;TZID=America/Los_Angeles:20241018T140000
DTEND;TZID=America/Los_Angeles:20241018T150000
DTSTAMP:20250404T180934Z
CREATED:20241014T181841Z
LAST-MODIFIED:20250404T180934Z
UID:16376-1729260000-1729263600@www.cecs.uci.edu
SUMMARY:In Vitro Biological Neural Networks
DESCRIPTION:Title: In Vitro Biological Neural Networks \nSpeaker: Prof. Lawrence Rauchwerger \nDate and Time: Friday\, October 18th\, 2:00pm \nLocation: EH 2430 \nAbstract: \nA computing stack powered by living neurons could unlock self-organizing and dynamically rewiring systems with extreme connectivity and parallel processing power\, all while running on sugar with unprecedented energy efficiency. In this presentation we outline a framework that leverages in-silico simulation to establish an engineering testbed with the ultimate goal of learning to harness neural in-vitro systems for computational purposes. We describe an optimization approach to uncover reproducible neural activity present in a system that can be leveraged to carry out basic information processing tasks. We demonstrate the feasibility of this approach by optimizing a simulated neural system to perform digit classification\, offering a proof-of-concept for a potential pathway to leveraging neural computation in vitro. \nBiography: \nLawrence Rauchwerger is a professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. He holds a doctoral degree in computer science from the University of Illinois Urbana-Champaign and an M.S. in electrical engineering from Stanford University. Rauchwerger is known for his approach to auto-parallelization\, thread-level speculation and parallel library (STAPL). His research interests are in compilers for parallel computing\, parallel and distributed C++ libraries\, adaptive runtime optimizations\, and architectures for parallel computing. Lately he has become active in neurocomputing. He received an IBM Faculty Award\, an Intel Faculty Award\, and the NSF Faculty Early Career Development\n(CAREER) Award. He is an IEEE Fellow and a AAAS Fellow. \nHosted By: Prof. Alex Veidenbaum \nMore Information
URL:https://www.cecs.uci.edu/event/in-vitro-biological-neural-networks/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2024/10/Lawrence-Rauchwerger-e1728930349485.png
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