Extending Relativistic Programming to Multiple Writers
For software to take advantage of modern multicore processors, it must be safely concurrent and it must scale. Many techniques that allow safe concurrency do so at the expense of scalability. Coarse grain locking allows multiple threads to access common data safely, but not at the same time. Non-Blo...
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ndltd-pdx.edu-oai-pdxscholar.library.pdx.edu-open_access_etds-11132019-10-20T04:23:57Z Extending Relativistic Programming to Multiple Writers Howard, Philip William For software to take advantage of modern multicore processors, it must be safely concurrent and it must scale. Many techniques that allow safe concurrency do so at the expense of scalability. Coarse grain locking allows multiple threads to access common data safely, but not at the same time. Non-Blocking Synchronization and Transactional Memory techniques optimistically allow concurrency, but only for disjoint accesses and only at a high performance cost. Relativistic programming is a technique that allows low overhead readers and joint access parallelism between readers and writers. Most of the work on relativistic programming has assumed a single writer at a time (or, in partitionable data structures, a single writer per partition), and single writer solutions cannot scale on the write side. This dissertation extends prior work on relativistic programming in the following ways: 1) It analyses the ordering requirements of lock-based and relativistic programs in order to clarify the differences in their correctness and performance characteristics, and to define precisely the behavior required of the relativistic programming primitives. 2) It shows how relativistic programming can be used to construct efficient, scalable algorithms for complex data structures whose update operations involve multiple writes to multiple nodes. 3) It shows how disjoint access parallelism can be supported for relativistic writers, using Software Transactional Memory, while still allowing low-overhead, linearly-scalable, relativistic reads. 2012-01-01T08:00:00Z text application/pdf https://pdxscholar.library.pdx.edu/open_access_etds/114 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1113&context=open_access_etds Dissertations and Theses PDXScholar Concurrency Relativistic programming Data structures Synchronization Multicore Multiprocessors -- Programming Systems programming (Computer science) Parallel programming (Computer science) |
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Concurrency Relativistic programming Data structures Synchronization Multicore Multiprocessors -- Programming Systems programming (Computer science) Parallel programming (Computer science) |
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Concurrency Relativistic programming Data structures Synchronization Multicore Multiprocessors -- Programming Systems programming (Computer science) Parallel programming (Computer science) Howard, Philip William Extending Relativistic Programming to Multiple Writers |
description |
For software to take advantage of modern multicore processors, it must be safely concurrent and it must scale. Many techniques that allow safe concurrency do so at the expense of scalability. Coarse grain locking allows multiple threads to access common data safely, but not at the same time. Non-Blocking Synchronization and Transactional Memory techniques optimistically allow concurrency, but only for disjoint accesses and only at a high performance cost. Relativistic programming is a technique that allows low overhead readers and joint access parallelism between readers and writers. Most of the work on relativistic programming has assumed a single writer at a time (or, in partitionable data structures, a single writer per partition), and single writer solutions cannot scale on the write side. This dissertation extends prior work on relativistic programming in the following ways: 1) It analyses the ordering requirements of lock-based and relativistic programs in order to clarify the differences in their correctness and performance characteristics, and to define precisely the behavior required of the relativistic programming primitives. 2) It shows how relativistic programming can be used to construct efficient, scalable algorithms for complex data structures whose update operations involve multiple writes to multiple nodes. 3) It shows how disjoint access parallelism can be supported for relativistic writers, using Software Transactional Memory, while still allowing low-overhead, linearly-scalable, relativistic reads. |
author |
Howard, Philip William |
author_facet |
Howard, Philip William |
author_sort |
Howard, Philip William |
title |
Extending Relativistic Programming to Multiple Writers |
title_short |
Extending Relativistic Programming to Multiple Writers |
title_full |
Extending Relativistic Programming to Multiple Writers |
title_fullStr |
Extending Relativistic Programming to Multiple Writers |
title_full_unstemmed |
Extending Relativistic Programming to Multiple Writers |
title_sort |
extending relativistic programming to multiple writers |
publisher |
PDXScholar |
publishDate |
2012 |
url |
https://pdxscholar.library.pdx.edu/open_access_etds/114 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1113&context=open_access_etds |
work_keys_str_mv |
AT howardphilipwilliam extendingrelativisticprogrammingtomultiplewriters |
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1719270844972138496 |