{"676820":{"#nid":"676820","#data":{"type":"event","title":"PhD Proposal by Ranjan Sarpangala Venkatesh","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003EOptimizing HPC I\/O Performance Over New Memory and Storage Hierarchies: A Data-Driven Approach\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: September 20th, 2024\u003Cbr\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 2:00 PM - 4:00 PM EDT\u003Cbr\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Klaus Advanced Computing Building, Conference Room 3126\u003Cbr\u003E\u003Cstrong\u003EVirtual meeting\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/98262397839?pwd=fc5E5vZzIEwgH2nMHN5oa9ci1z8Q3t.1\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/98262397839?pwd=fc5E5vZzIEwgH2nMHN5oa9ci1z8Q3t.1\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ERanjan Sarpangala Venkatesh\u003C\/strong\u003E\u003Cbr\u003ESchool of Computer Science\u003Cbr\u003ECollege of Computing\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003Cbr\u003EDr. Ada Gavrilovska (advisor) - School of Computer Science, Georgia Institute of Technology\u003Cbr\u003EDr. Greg Eisenhauer - School of Computer Science, Georgia Institute of Technology\u003Cbr\u003EDr. Santosh Pande - School of Computer Science, Georgia Institute of Technology\u003Cbr\u003EDr. Richard Vuduc - School of Computational Science and Engineering, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMulti-component HPC workflows face growing bottlenecks in data and metadata I\/O due to rapid data growth. The efficiency of data movement in these workflows relies on I\/O performance throughout the memory and storage hierarchy. While new memory technologies and I\/O stacks offer opportunities for improvement, their distinct APIs complicate optimization, making empirical approaches necessary to balance trade-offs across components. This thesis supports data-driven methods to improve I\/O performance in next-generation HPC systems.\u003C\/p\u003E\u003Cp\u003EThe first part of this work evaluated various workflow configurations on systems with heterogeneous memory, showing that careful scheduling and data allocation can enhance end-to-end performance by up to 1.6x. By analyzing workflow characteristics, key elements impacting performance variability were identified, resulting in a framework for future workflow schedulers to optimize in situ workflows.\u003C\/p\u003E\u003Cp\u003EThe second part focused on metadata I\/O, a growing issue in large-scale workflows. Using the WarpX application and ADIOS (Adaptable I\/O System) middleware, which is widely used for data management in scientific applications, it was shown that metadata I\/O could account for up to 25% of total I\/O time at scale. To address this, the design space of the DAOS (Distributed Asynchronous Object Storage) system was explored, specifically focusing on DAOS Key-Value and Array objects for transferring ADIOS metadata. A newly developed DAOS-based engine for ADIOS metadata I\/O improved performance by 2.3x compared to the DAOS POSIX interface, effectively reducing metadata scaling bottlenecks. For the WarpX application, this reduced metadata I\/O time by more than 4x, lowering overhead from 20% to just 5% of total I\/O time.\u003C\/p\u003E\u003Cp\u003EAs part of the proposed work, MetaBench is introduced. This suite of benchmarks will evaluate DAOS Key-Value and Array interfaces for ADIOS metadata transfer, optimized for a given HPC setup. MetaBench will analyze trade-offs, including metadata size and the number of ranks, to identify the optimal DAOS configuration. It will evaluate real applications and data patterns, providing a practical template for managing metadata transfer across HPC middleware, including ADIOS, HDF5, and PnetCDF.\u003C\/p\u003E\u003Cp\u003EThese insights will contribute to the development of tools and support the HPC community by integrating DAOS engines into widely used middleware.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOptimizing HPC I\/O Performance Over New Memory and Storage Hierarchies: A Data-Driven Approach\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Optimizing HPC I\/O Performance Over New Memory and Storage Hierarchies: A Data-Driven Approach"}],"uid":"27707","created_gmt":"2024-09-16 18:23:20","changed_gmt":"2024-09-16 18:24:10","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-20T14:00:00-04:00","event_time_end":"2024-09-20T16:00:00-04:00","event_time_end_last":"2024-09-20T16:00:00-04:00","gmt_time_start":"2024-09-20 18:00:00","gmt_time_end":"2024-09-20 20:00:00","gmt_time_end_last":"2024-09-20 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Klaus Advanced Computing Building, Conference Room 3126","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}