Thread Count Prediction Model: Dynamically Adjusting Threads for Heterogeneous Many-Core Systems

Ju, Tao; Wu, Weiguo; Chen, Heng; Zhu, Zhengdong; Dong, Xiaoshe

doi:10.1109/icpads.2015.64

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Case IV: The performance speedup exhibits an irregular change with increasing thread count, as shown in the canneal and swaptions. These observations clearly indicate the importance of the appropriate number of cores and threads for computing performance and energy efficiency in many-core systems [4]. Many previous studies have been conducted to determine an appropriate thread count for multi-threaded applications running on multi-core or many-core systems.…”

Section: Introductionmentioning

confidence: 94%

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Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Zhang

et al. 2019

Energies

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Improving computing performance and reducing energy consumption are a major concern in heterogeneous many-core systems. The thread count directly influences the computing performance and energy consumption for a multithread application running on a heterogeneous many-core system. For this work, we studied the interrelation between the thread count and the performance of applications to improve total energy efficiency. A prediction model of the optimum thread count, hereafter the thread count prediction model (TCPM), was designed by using regression analysis based on the program running behaviors and heterogeneous many-core architecture feature. Subsequently, a dynamic predictive thread mapping (DPTM) framework was proposed. DPTM uses the prediction model to estimate the optimum thread count and dynamically adjusts the number of active hardware threads according to the phase changes of the running program in order to achieve the optimal energy efficiency. Experimental results show that DPTM obtains a nearly 49% improvement in performance and a 59% reduction in energy consumption on average. Moreover, DPTM introduces about 2% additional overhead compared with traditional thread mapping for PARSEC(The Princeton Application Repository for Shared-Memory Computers) benchmark programs running on an Intel MIC (Many integrated core)heterogeneous many-core system.

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Section: Introductionmentioning

confidence: 94%

“…The thread count is the main factor that influences program execution performance. In this section, we design an optimum thread count prediction model (TCPM) based on our previous work [4].…”

Section: Optimum Thread Count Prediction Model (Tcpm)mentioning

confidence: 99%

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Zhang

et al. 2019

Energies

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“…Our proposal di ers from theirs in the type of queuing theory used, and in the approach to collect the entrance and service time, this values are taken as an input in our tool. The work by Ju et al (2015) sought, through a prediction model, to determine the optimum number of threads to execute applications in heterogeneous systems and with multiple processors.…”

Section: Related Workmentioning

confidence: 99%

Towards optimisation of the number of threads in the integration platform engines using simulation models based on queueing theory

Haugg¹,

Frantz²,

Roos‐Frantz³

et al. 2019

RBCA

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The use of applications is important to support the business processes of companies. However, most of these applications are not designed to function collaboratively. An integration solution orchestrates a group of applications, allowing data and functionality reuse. The performance of an integration solution depends on the optimum configuration of the number of threads in the runtime engine provided by the integration platforms. It is common that this configuration relies on the empirical knowledge of the software engineers, and it has a direct impact on the performance of integration solutions. The optimum number of threads may be found by means of simulation models. This article presents a methodology and a tool to assist with the generation of simulation models based on queuing theory, in order to find the optimum number of threads to execute an integration solution focusing on performance improvement. We introduce a case of study to demonstrate and experiments to evaluate our proposal.

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Tuning Parallel Applications

Lorenzon

Filho

2019

Parallel Computing Hits the Power Wall

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Thread Count Prediction Model: Dynamically Adjusting Threads for Heterogeneous Many-Core Systems

Cited by 4 publications

References 24 publications

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Towards optimisation of the number of threads in the integration platform engines using simulation models based on queueing theory

Tuning Parallel Applications

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