A Learning-based Fetch Thread Gating Mechanism for A Simultaneous Multithreading Processor

Ide, Yosuke; Yamasaki, Nobuyuki

doi:10.1109/candar51075.2020.00011

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…With the same considerations of previous recent works, [28] also aim to optimize the usage of the resources offered by SMT, they are proposing a solution affecting the compilation phase. Furthermore in 2020 from [29], wanted to optimize fetching policies with machine learning. All these recent papers are assuming the SMT value for enhancing performance.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Simultaneous Multi-threading and Affinity Performance for Reproducible Parallel Stochastic Simulation

Antunes,

Hill

2023

Res. Rep. Comput. Sci.

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This paper investigates whether simultaneous multi-threading (SMT) can improve performance on modern computing clusters with reproducible results on four types of applications, focused on stochastic simulations with different memory bound and compute bound constraints. We manually set the affinity of processes to compare its efficiency with the computing time obtained by the automatic assignment of the operating system. To measure SMT and affinity impact on a modern multicore processor, we parallelize up to 128 processes of the four types of applications. We expect repeatable numerical results between the sequential and parallel versions of simulations. For the three applications that are not memory bound, SMT is more effective by up to 30%. This represents an interesting increase up to 10% more performance for compute bound applications when compared to the initial papers discussing the efficiency of SMT. However, for the memory-bound application, SMT is less effective and can even decrease performance. The manual setting of core affinity does not show an increase in performance compared to the automatic assignment. All code and data used in the study are available to help reproducible research.

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Section: Related Workmentioning

confidence: 99%