TransMetric: architecture independent workload characterization for transactional memory benchmarks
Proceedings of the 23rd international conference on Supercomputing
Proceedings of the international conference on Supercomputing
Statistical measures for quantifying task and machine heterogeneities
The Journal of Supercomputing
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Transactional memory is emerging as a parallel programming paradigm for multi-core processors. Despite the recent interest in transactional memory, there has been no study to characterize the interaction between hardware transactional memory (HTM) design dimensions and multi-core microarchitecture configuration. In this paper, we investigate the use of analytical modeling techniques to build application-specific performance models for understanding the interaction between HTM and multi-core configurations across large design points and for efficiently exploring the co-design space between the two. A key feature of our modeling technique is the ability to simultaneously capture the individual and combinatorial effects of important HTM design dimensions and core microarchitectural parameters. We show that analytical models can be effective tools for assisting architects in identifying these key effects and the interactions between HTM and multi-core microarchitecture that have a high impact on the performance of transactional memory workloads. The models also enable accurate performance prediction across the joint TM/multi-core design space. By analyzing the regression trees generated from our neural network model building methods, we further reveal heterogeneous interaction between TM workloads, core microarchitectures and TM mechanisms.