2026-09-21T18:58:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/22649 2023-07-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Winslett, Marianne Seamons, Kent Eldon 2011-05-07T13:46:45Z 2011-05-07T13:46:45Z 10000-01-01 1996 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 9780591088489 AAI9702660 (UMI)AAI9702660 http://hdl.handle.net/2142/22649 eng Copyright 1996 Seamons, Kent Eldon Engineering, System Science Computer Science Panda: Fast access to persistent arrays using high-level interfaces and server directed input/output text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.