ACM Transactions on Programming Languages and Systems (TOPLAS)
Proceedings of the fourteenth annual ACM symposium on Principles of distributed computing
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The Potential for Using Thread-Level Data Speculation to Facilitate Automatic Parallelization
HPCA '98 Proceedings of the 4th International Symposium on High-Performance Computer Architecture
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Customizable parallel execution of scientific stream queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Predictive log-synchronization
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Implicit parallelism with ordered transactions
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Optimistic parallelism requires abstractions
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Streamflex: high-throughput stream programming in java
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Transactional boosting: a methodology for highly-concurrent transactional objects
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Dynamic performance tuning of word-based software transactional memory
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
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Event stream applications consist of an acyclic graph of components that are traversed by streams of events. Examples of operations in such components are filtering, aggregation, enrichment, and transformation of events and, commonly, applications include a mix of common-use library functions and user-defined functions. When the operation only depends on the current input events, the component can be trivially parallelized by replication. However, if the component keeps state that is used for the computation of the results, the trivial parallelization approach does not work. Parallel versions for common components have being designed, but complex or user-defined components are normally limited by single thread performance. In this work, we use optimistic parallelization approaches to harness the potential of multi-core processors to scale the performance of stateful operators in event stream applications. In addition, we investigate indulgent ways to allow the user to provide application knowledge that can improve the amount of useful speculative work. The current prototype shows considerable gain in throughput even though some speculative executions must be disregarded.