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Luke Dalessandro

Researcher at Indiana University

Publications -  27
Citations -  1273

Luke Dalessandro is an academic researcher from Indiana University. The author has contributed to research in topics: Transactional memory & Software transactional memory. The author has an hindex of 13, co-authored 25 publications receiving 1216 citations. Previous affiliations of Luke Dalessandro include University of Rochester.

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Proceedings ArticleDOI

NOrec: streamlining STM by abolishing ownership records

TL;DR: An ownership-record-free software transactional memory (STM) system that combines extremely low overhead with unusually clean semantics is presented, and the experience suggests that NOrec may be an ideal candidate for such a software system.
Proceedings ArticleDOI

Privatization techniques for software transactional memory

TL;DR: It is argued that privatization comprises a pair of symmetric subproblems: private operations may fail to see updates made by transactions that have committed but not yet completed; conversely, transactions that are doomed but have not yet aborted may see Updates made by private code, causing them to perform erroneous, externally visible operations.
Proceedings ArticleDOI

A comprehensive strategy for contention management in software transactional memory

TL;DR: Experimental evaluation demonstrates that the overhead of the mechanisms is low, particularly when conflicts are rare, and that the strategy as a whole provides good throughput and fairness, including livelock and starvation freedom, even for challenging workloads.
Proceedings ArticleDOI

Hybrid NOrec: a case study in the effectiveness of best effort hardware transactional memory

TL;DR: A family of hybrid TMs built using the recent NOrec STM algorithm is introduced that, unlike existing hybrid approaches, provide both low overhead on hardware transactions and concurrent execution of hardware and software transactions.
Book ChapterDOI

Transactional mutex locks

TL;DR: Using optimized spinlocks and the TL2 STM algorithm as baselines, TML provides the low latency of locks at low thread levels, and the scalability of STM for read-dominated workloads, suggesting that TML is a good reference implementation to use when evaluating STM algorithms.