In this paper we explore machine-learning approaches for dynamically selecting the well suited amount of concurrent threads in applications relying on Software Transactional Memory (STM). Specifically, we present a solution that dynamically shrinks or enlarges the set of input features to be exploited by the machine-learner. This allows for tuning the concurrency level while also minimizing the overhead for input-features sampling, given that the cardinality of the input feature set is always tuned to the minimum value that still guarantees reliability of workload characterization. We also present a fully fledged implementation of our proposal within the TinySTM open source framework, and provide the results of an experimental study relying on the STAMP benchmark suite, which show significant reduction of the response time with respect to proposals based on static feature selection.

In this paper we explore machine-learning approaches for dynamically selecting the well suited amount of concurrent threads in applications relying on Software Transactional Memory (STM). Specifically, we present a solution that dynamically shrinks or enlarges the set of input features to be exploited by the machine-learner. This allows for tuning the concurrency level while also minimizing the overhead for input-features sampling, given that the cardinality of the input-feature set is always tuned to the minimum value that still guarantees reliability of workload characterization. We also present a fully heedged implementation of our proposal within the TinySTM open source framework, and provide the results of an experimental study relying on the STAMP benchmark suite, which show significant reduction of the response time with respect to proposals based on static feature selection. © 2014 IEEE.

Dynamic feature selection for machine-learning based concurrency regulation in STM

DI SANZO, PIERANGELO;
2014

Abstract

In this paper we explore machine-learning approaches for dynamically selecting the well suited amount of concurrent threads in applications relying on Software Transactional Memory (STM). Specifically, we present a solution that dynamically shrinks or enlarges the set of input features to be exploited by the machine-learner. This allows for tuning the concurrency level while also minimizing the overhead for input-features sampling, given that the cardinality of the input-feature set is always tuned to the minimum value that still guarantees reliability of workload characterization. We also present a fully heedged implementation of our proposal within the TinySTM open source framework, and provide the results of an experimental study relying on the STAMP benchmark suite, which show significant reduction of the response time with respect to proposals based on static feature selection. © 2014 IEEE.
9781479927296
In this paper we explore machine-learning approaches for dynamically selecting the well suited amount of concurrent threads in applications relying on Software Transactional Memory (STM). Specifically, we present a solution that dynamically shrinks or enlarges the set of input features to be exploited by the machine-learner. This allows for tuning the concurrency level while also minimizing the overhead for input-features sampling, given that the cardinality of the input feature set is always tuned to the minimum value that still guarantees reliability of workload characterization. We also present a fully fledged implementation of our proposal within the TinySTM open source framework, and provide the results of an experimental study relying on the STAMP benchmark suite, which show significant reduction of the response time with respect to proposals based on static feature selection.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/160356
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