{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129995"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129995","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Managing scalable direct storage accesses for GPUs","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Zhou, Yirui (Eric)"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Jian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-25","date_published":"2025-07-25","updated_at":"2026-07-22T22:25:06Z","subjects":["File System","Gpu"],"languages":["en","eng"],"rights":["Copyright 2025 Yirui (Eric) Zhou"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129995","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Jian"]},{"key":"dc:creator","label":"Author","values":["Zhou, Yirui (Eric)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-25","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["File System","Gpu"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Yirui (Eric) Zhou"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129995"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Yirui (Eric) Zhou, accepted the attached license on 2025-07-25 at 09:26.","The student, Yirui (Eric) Zhou, submitted this Thesis for approval on 2025-07-25 at 09:35.","This Thesis was approved for publication on 2025-07-25 at 09:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22737 on 2025-10-20 at 20:15:49","As we shift from CPU-centric computing to GPU-accelerated computing for supporting intelligent data processing at scale, the storage bottleneck has been exacerbated. To bypass the host CPU and alleviate unnecessary data movements, modern GPUs enable direct storage access to SSDs (i.e., GPUDirect Storage). However, current GPUDirect Storage solutions still rely on the host file system to manage the storage device, direct storage accesses are still bottlenecked by the host. This thesis proposes a GPU-orchestrated file system (GoFS) for scaling the direct storage accesses for GPU programs, by fully offloading the storage management to the GPU. As GoFS provides POSIX API and manages core filesystem structures in GPU memory, it can execute both control path and data path without host CPU involvement. To enable highly concurrent direct storage accesses, GoFS rethinks the design and implementation of core filesystem structures with various optimization techniques, such as scalable data indexing, fine-grained per-SM (streaming multiprocessor) block management, and zero-copy I/O accesses, by carefully exploring the GPU-accelerated computing paradigm. GoFS preserves the essential filesystem properties such as crash consistency, and it is compatible with existing host-based file systems like F2FS. GoFS does not require changes to the on-disk filesystem organization, therefore, the host and GPU can manage the SSD in a coordinated fashion, and maintain the data consistency in a primary/secondary mode. GoFS develops based on F2FS using 7.9K lines of codes with CUDA programming. We examine its efficiency on an A100 GPU. The experiments with various GPU-based applications show that GoFS outperforms state-of-the-art storage access solutions for GPUs by 1.61× on average."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Managing scalable direct storage accesses for GPUs"]}]}],"canonical_facts":{"dc:contributor":["Huang, Jian"],"dc:creator":["Zhou, Yirui (Eric)"],"dc:date":["2025-07-25","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Yirui (Eric) Zhou, accepted the attached license on 2025-07-25 at 09:26.","The student, Yirui (Eric) Zhou, submitted this Thesis for approval on 2025-07-25 at 09:35.","This Thesis was approved for publication on 2025-07-25 at 09:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22737 on 2025-10-20 at 20:15:49","As we shift from CPU-centric computing to GPU-accelerated computing for supporting intelligent data processing at scale, the storage bottleneck has been exacerbated. To bypass the host CPU and alleviate unnecessary data movements, modern GPUs enable direct storage access to SSDs (i.e., GPUDirect Storage). However, current GPUDirect Storage solutions still rely on the host file system to manage the storage device, direct storage accesses are still bottlenecked by the host. This thesis proposes a GPU-orchestrated file system (GoFS) for scaling the direct storage accesses for GPU programs, by fully offloading the storage management to the GPU. As GoFS provides POSIX API and manages core filesystem structures in GPU memory, it can execute both control path and data path without host CPU involvement. To enable highly concurrent direct storage accesses, GoFS rethinks the design and implementation of core filesystem structures with various optimization techniques, such as scalable data indexing, fine-grained per-SM (streaming multiprocessor) block management, and zero-copy I/O accesses, by carefully exploring the GPU-accelerated computing paradigm. GoFS preserves the essential filesystem properties such as crash consistency, and it is compatible with existing host-based file systems like F2FS. GoFS does not require changes to the on-disk filesystem organization, therefore, the host and GPU can manage the SSD in a coordinated fashion, and maintain the data consistency in a primary/secondary mode. GoFS develops based on F2FS using 7.9K lines of codes with CUDA programming. We examine its efficiency on an A100 GPU. The experiments with various GPU-based applications show that GoFS outperforms state-of-the-art storage access solutions for GPUs by 1.61× on average."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129995"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Yirui (Eric) Zhou"],"dc:subject":["File System","Gpu"],"dc:title":["Managing scalable direct storage accesses for GPUs"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}