Transactional Memory Scheduling Using Machine Learning Techniques

Current shared memory multi-core systems require powerful software and hardware techniques to support the performance parallel computation and consistency simultaneously. The use of transactional memory results in significant improvement of performance by avoiding thread synchronization and locks ov...

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Veröffentlicht in:2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) S. 718 - 725
Hauptverfasser: Assiri, Basem, Busch, Costas
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2016
Schlagworte:
Accuracy
Algorithms
Computer programs
Decisions
Fairness Values
Hidden Markov Model
Hidden Markov models
History
K-Nearest Neighbor
Lazy Snapshot Algorithm
Locks
Machine learning
Machine learning algorithms
Scheduling
Support Vector Machine
Support vector machines
Synchronism
Throughput
Training
Transactional Memory
ISSN:2377-5750
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Abstract Current shared memory multi-core systems require powerful software and hardware techniques to support the performance parallel computation and consistency simultaneously. The use of transactional memory results in significant improvement of performance by avoiding thread synchronization and locks overhead. Also, transactions scheduling apparently influences the performance of transactional memory. In this paper, we study the fairness of transactions' scheduling using Lazy Snapshot Algorithm. The fairness of transactions' scheduling aims to balance between transactions types which are read-only and update transactions. Indeed, we support the fairness of the scheduling procedure by a machine learning technique. The machine learning techniques improve the fairness decisions according to transactions' history. The experiments in this paper show that the throughput of the Lazy Snapshot Algorithm is improved with a machine learning support. Indeed, our experiments show that the learning significantly affects the performance if the durations of update transactions are much longer than read-only ones. We also study several machine learning techniques to investigate the fairness decisions accuracy. In fact, K-Nearest Neighbor machine learning technique shows more accuracy and more suitability, for our problem, than Support Vector Machine Model and Hidden Markov Model.
AbstractList Current shared memory multi-core systems require powerful software and hardware techniques to support the performance parallel computation and consistency simultaneously. The use of transactional memory results in significant improvement of performance by avoiding thread synchronization and locks overhead. Also, transactions scheduling apparently influences the performance of transactional memory. In this paper, we study the fairness of transactions' scheduling using Lazy Snapshot Algorithm. The fairness of transactions' scheduling aims to balance between transactions types which are read-only and update transactions. Indeed, we support the fairness of the scheduling procedure by a machine learning technique. The machine learning techniques improve the fairness decisions according to transactions' history. The experiments in this paper show that the throughput of the Lazy Snapshot Algorithm is improved with a machine learning support. Indeed, our experiments show that the learning significantly affects the performance if the durations of update transactions are much longer than read-only ones. We also study several machine learning techniques to investigate the fairness decisions accuracy. In fact, K-Nearest Neighbor machine learning technique shows more accuracy and more suitability, for our problem, than Support Vector Machine Model and Hidden Markov Model.
Author Busch, Costas
Assiri, Basem
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SubjectTerms Accuracy
Algorithms
Computer programs
Decisions
Fairness Values
Hidden Markov Model
Hidden Markov models
History
K-Nearest Neighbor
Lazy Snapshot Algorithm
Locks
Machine learning
Machine learning algorithms
Scheduling
Support Vector Machine
Support vector machines
Synchronism
Throughput
Training
Transactional Memory
Title Transactional Memory Scheduling Using Machine Learning Techniques
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