Simultaneous multithreading: maximizing on-chip parallelism
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
Handling long-latency loads in a simultaneous multithreading processor
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Front-End Policies for Improved Issue Efficiency in SMT Processors
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Picking Statistically Valid and Early Simulation Points
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
The Impact of Resource Partitioning on SMT Processors
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
Dynamically Controlled Resource Allocation in SMT Processors
Proceedings of the 37th annual IEEE/ACM International Symposium on Microarchitecture
Adaptive reorder buffers for SMT processors
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
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The performance in Simultaneous Multi-Threading (SMT) processors is mainly determined by the distribution of the common resources among the threads. However, the threads exhibit dynamically complicated behavior while they compete for resources at runtime. It is a challenge to meet the changing resource requirements of the threads. This work proposes a Swarm-inspired Resource Distribution (SRD) policy to address the dynamic optimization problem of resource distribution for SMT processors, which uses the runtime performance to guide the generating of trial distributions. A computational model is established by adaptation of swarm intelligence to direct the social exploitation and self exploration activities of the trial distributions in the dynamic optimization environment. Results from simulation show that, benefiting from the good cooperation between SRD's social exploitation on historical experience and self exploration of new solutions, SRD obtains satisfying improvements of both throughput and fairness performance, especially in complicated SMT environment.