Shared memory multi-core systems benet from transactional memory implementations
due to the inherent avoidance of deadlocks and progress guarantees. In
this research, we examine how the system performance is aected by transaction
fairness in scheduling and by the precision in consistency. We rst explore
the fairness aspect using a Lazy Snapshot (multi-version) Algorithm. The fairness
of transactions scheduling aims to balance the load between read-only and update
transactions. We implement a fairness mechanism based on machine learning
techniques that improve fairness decisions according to the transaction execution
history. Experimental analysis shows that the throughput of the Lazy Snapshot
Algorithm is improved with machine learning support.
We also explore the impacts on performance of consistency relaxation. In transactional
memory, correctness is typically proven with opacity which is a precise
consistency property that requires a legal serialization of an execution such that
transactions do not overlap (atomicity) and read instructions always return the
most recent value (legality). In real systems there are situations where system delays
do not allow precise consistency, such as in large scale applications, due to
network or other time delays. Thus, we introduce here the notion of approximate
consistency in transactional memory. We dene K-opacity as a relaxed consistency
property where transactions' read operations may return one of K most recent
written values. In multi-version transactional memory, this allows to save a new
object version once every K object updates, which has two benets: (i) it reduces
space requirements by a factor of K, and (ii) it reduces the number of aborts, since
there is smaller chance for con
icts. In fact, we apply the concept of K-opacity on
regular read and write, count and queue objects, which are common objects used in typical concurrent programs. We provide formal correctness proofs and we also
demonstrate the performance benets of our approach with experimental analysis.
We compare the performance of precise consistent execution (1-opaque) with
dierent consistency values of K using micro benchmarks. The results show that
increased relaxation of opacity gives higher throughput and decreases the aborts
rate signicantly.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-06292016-021029 |
| Date | 11 July 2016 |
| Creators | Assiri, Basem Ibrahim |
| Contributors | Busch, Konstantin, Karki, Bijaya, Mukhopadhyay, Supratik, Abu-Farsakh, Murad |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-06292016-021029/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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