{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117685"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117685","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DEMOC: decompression memcpy operations for CODAG","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Gacek, Andrew"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hwu, Wen-mei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Gpu","Codag","Accelerator","Compression","Decompression"],"languages":["en","eng"],"rights":["Copyright 2022 Andrew Gacek"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117685","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hwu, Wen-mei"]},{"key":"dc:creator","label":"Author","values":["Gacek, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gpu","Codag","Accelerator","Compression","Decompression"]}]},{"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 2022 Andrew Gacek"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117685"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Andrew Gacek, accepted the attached license on 2022-12-06 at 14:44.","The student, Andrew Gacek, submitted this Thesis for approval on 2022-12-06 at 14:53.","This Thesis was approved for publication on 2022-12-08 at 08:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18755 on 2023-04-12 at 08:14:05","Big-data applications utilizing increasingly large datasets require fast decompression schemes to process these growing stores of data. State-of-the-art big-data applications are accelerated on GPUs to leverage high compute throughput. GPU decompression frameworks such as RAPIDs and CODAG enable developers to create general purpose data pipelines to process compressed data. These frameworks provide high throughput for GPU decompression but do not leverage specific properties of the dataset when writing the output. To address this shortcoming, we introduce DEMOC, a set of writing functions for the CODAG decompression framework designed to allow developers to easily tune the decompression scheme's writing behavior. DEMOC provides seven memcpy functions tested against a variety of unit tests and within the CODAG framework using the Deflate decompression scheme. On highly compressed datasets, DEMOC functions can increase throughput by up to 1.4x, while an N-Byte Hybrid memcpy function obtains six percent increased throughput across all the test datasets and decreases the amount of memory requests by 25 percent. Other DEMOC memcpy functions are shown to achieve up to 2x throughput on large memcpy calls over the traditional 1-Byte function using the DEMOC standalone decompression writing unit test environment. To provide insight on the different memcpy functions' performance across different decompression schemes, this work goes into further detail on the resource requirements of each function and characteristics of each dataset."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["DEMOC: decompression memcpy operations for CODAG"]}]}],"canonical_facts":{"dc:contributor":["Hwu, Wen-mei"],"dc:creator":["Gacek, Andrew"],"dc:date":["2022-12","2022-12-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Andrew Gacek, accepted the attached license on 2022-12-06 at 14:44.","The student, Andrew Gacek, submitted this Thesis for approval on 2022-12-06 at 14:53.","This Thesis was approved for publication on 2022-12-08 at 08:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18755 on 2023-04-12 at 08:14:05","Big-data applications utilizing increasingly large datasets require fast decompression schemes to process these growing stores of data. State-of-the-art big-data applications are accelerated on GPUs to leverage high compute throughput. GPU decompression frameworks such as RAPIDs and CODAG enable developers to create general purpose data pipelines to process compressed data. These frameworks provide high throughput for GPU decompression but do not leverage specific properties of the dataset when writing the output. To address this shortcoming, we introduce DEMOC, a set of writing functions for the CODAG decompression framework designed to allow developers to easily tune the decompression scheme's writing behavior. DEMOC provides seven memcpy functions tested against a variety of unit tests and within the CODAG framework using the Deflate decompression scheme. On highly compressed datasets, DEMOC functions can increase throughput by up to 1.4x, while an N-Byte Hybrid memcpy function obtains six percent increased throughput across all the test datasets and decreases the amount of memory requests by 25 percent. Other DEMOC memcpy functions are shown to achieve up to 2x throughput on large memcpy calls over the traditional 1-Byte function using the DEMOC standalone decompression writing unit test environment. To provide insight on the different memcpy functions' performance across different decompression schemes, this work goes into further detail on the resource requirements of each function and characteristics of each dataset."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117685"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Andrew Gacek"],"dc:subject":["Gpu","Codag","Accelerator","Compression","Decompression"],"dc:title":["DEMOC: decompression memcpy operations for CODAG"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}