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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| Format: | Others |
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PDXScholar
2012
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| Online Access: | https://pdxscholar.library.pdx.edu/open_access_etds/114 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1113&context=open_access_etds |
| Summary: | 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. |
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