{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/22649"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/22649","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Panda: Fast access to persistent arrays using high-level interfaces and server directed input/output","abstract":"Multidimensional arrays are a fundamental data type in scientific computing and are used extensively across a broad range of applications. Often these arrays are persistent, i.e., they outlive the invocation of the program that created them. Portability and performance with respect to input and output (i/o) pose significant challenges to applications accessing large persistent arrays, especially in distributed-memory environments. A significant number of scientific applications perform conceptually simple array i/o operations, such as reading or writing a subarray, an entire array, or a list of arrays. However, the algorithms to perform these operations efficiently on a given platform may be complex and non-portable, and may require costly customizations to operating system software.","abstract_html":"Multidimensional arrays are a fundamental data type in scientific computing and are used extensively across a broad range of applications. Often these arrays are persistent, i.e., they outlive the invocation of the program that created them. Portability and performance with respect to input and output (i/o) pose significant challenges to applications accessing large persistent arrays, especially in distributed-memory environments. A significant number of scientific applications perform conceptually simple array i/o operations, such as reading or writing a subarray, an entire array, or a list of arrays. However, the algorithms to perform these operations efficiently on a given platform may be complex and non-portable, and may require costly customizations to operating system software.","abstract_has_math":false,"creators":["Seamons, Kent Eldon"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Winslett, Marianne"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:46:45Z","date_published":"2011-05-07T13:46:45Z","updated_at":"2026-07-22T22:25:20Z","subjects":["Engineering, System Science","Computer Science"],"languages":["eng"],"rights":["Copyright 1996 Seamons, Kent Eldon"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591088489","AAI9702660","(UMI)AAI9702660"],"render_values":[{"text":"9780591088489","href":null,"code":true},{"text":"AAI9702660","href":null,"code":true},{"text":"(UMI)AAI9702660","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/22649","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Winslett, Marianne"]},{"key":"dc:creator","label":"Author","values":["Seamons, Kent Eldon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:46:45Z","10000-01-01","1996"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Engineering, System Science","Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1996 Seamons, Kent Eldon"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591088489","AAI9702660","(UMI)AAI9702660","http://hdl.handle.net/2142/22649"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Multidimensional arrays are a fundamental data type in scientific computing and are used extensively across a broad range of applications. Often these arrays are persistent, i.e., they outlive the invocation of the program that created them. Portability and performance with respect to input and output (i/o) pose significant challenges to applications accessing large persistent arrays, especially in distributed-memory environments. A significant number of scientific applications perform conceptually simple array i/o operations, such as reading or writing a subarray, an entire array, or a list of arrays. However, the algorithms to perform these operations efficiently on a given platform may be complex and non-portable, and may require costly customizations to operating system software.","This thesis presents a high-level interface for array i/o and three implementation architectures, embodied in the Panda (Persistence AND Arrays) array i/o library. The high-level interface contributes to application portability, by encapsulating unnecessary details and being easy to use. Performance results using Panda demonstrate that an i/o system can provide application programs with a high-level, portable, easy-to-use interface for array i/o without sacrificing performance or requiring custom system software; in fact, combining all these benefits may only be possible through a high-level interface due to the great freedom and flexibility a high-level interface provides for the underlying implementation.","The Panda server-directed i/o architecture is a prime example of an efficient implementation of collective array i/o for closely synchronized applications in distributed-memory single-program multiple-data (SPMD) environments. A high-level interface is instrumental to the good performance of server-directed i/o, since it provides a global view of an upcoming collective i/o operation that Panda uses to plan sequential reads and writes. Performance results show that with server-directed i/o, Panda achieves throughputs close to the maximum AIX file system throughput on the i/o nodes of the IBM SP2 when reading and writing large multidimensional arrays.","Made available in DSpace on 2011-05-07T13:46:45Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9702660.pdf: 6666569 bytes, checksum: 702dca4faf3df90f011079a97212823e (MD5) Previous issue date: 1996","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:59:04Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:27:49-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Panda: Fast access to persistent arrays using high-level interfaces and server directed input/output"]}]}],"canonical_facts":{"dc:contributor":["Winslett, Marianne"],"dc:creator":["Seamons, Kent Eldon"],"dc:date":["2011-05-07T13:46:45Z","10000-01-01","1996"],"dc:description":["Multidimensional arrays are a fundamental data type in scientific computing and are used extensively across a broad range of applications. Often these arrays are persistent, i.e., they outlive the invocation of the program that created them. Portability and performance with respect to input and output (i/o) pose significant challenges to applications accessing large persistent arrays, especially in distributed-memory environments. A significant number of scientific applications perform conceptually simple array i/o operations, such as reading or writing a subarray, an entire array, or a list of arrays. However, the algorithms to perform these operations efficiently on a given platform may be complex and non-portable, and may require costly customizations to operating system software.","This thesis presents a high-level interface for array i/o and three implementation architectures, embodied in the Panda (Persistence AND Arrays) array i/o library. The high-level interface contributes to application portability, by encapsulating unnecessary details and being easy to use. Performance results using Panda demonstrate that an i/o system can provide application programs with a high-level, portable, easy-to-use interface for array i/o without sacrificing performance or requiring custom system software; in fact, combining all these benefits may only be possible through a high-level interface due to the great freedom and flexibility a high-level interface provides for the underlying implementation.","The Panda server-directed i/o architecture is a prime example of an efficient implementation of collective array i/o for closely synchronized applications in distributed-memory single-program multiple-data (SPMD) environments. A high-level interface is instrumental to the good performance of server-directed i/o, since it provides a global view of an upcoming collective i/o operation that Panda uses to plan sequential reads and writes. Performance results show that with server-directed i/o, Panda achieves throughputs close to the maximum AIX file system throughput on the i/o nodes of the IBM SP2 when reading and writing large multidimensional arrays.","Made available in DSpace on 2011-05-07T13:46:45Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9702660.pdf: 6666569 bytes, checksum: 702dca4faf3df90f011079a97212823e (MD5) Previous issue date: 1996","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:59:04Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:27:49-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["9780591088489","AAI9702660","(UMI)AAI9702660","http://hdl.handle.net/2142/22649"],"dc:language":["eng"],"dc:rights":["Copyright 1996 Seamons, Kent Eldon"],"dc:subject":["Engineering, System Science","Computer Science"],"dc:title":["Panda: Fast access to persistent arrays using high-level interfaces and server directed input/output"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:20Z"}