Application configuration selection for energy-efficient execution on multicore systems

Abstract

 We present a hybrid method to achieve an energy efficiency configuration. Our method utilizes concurrency levels, thread allocation, and DVFS settings. We propose a model to capture the relationship between C, P, and T in detail. We apply an analytical speedup model to predict an optimal/nearoptimal configuration. AbstractModern computer systems are designed to balance performance and energy consumption. Several run-time factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency for a workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific.While most existing works concentrate on either static analysis of the workload or run-time prediction results, in this paper, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration for a workload. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An offline optimal solution is used to compare with the proposed scheme. The experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.

Dynamic Voltage Frequency Scaling Simulator for Real Workflows Energy-Aware Management in Green Cloud Computing

“…Using this technique large reductions in power consumption with a very slight loss of efficiency in its performance are achieved. Different high performance computing platforms based on data centers such as cluster computing and supercomputing apply DVFS technologies to reduce power consumption and to get a high reliability and availability in their infrastructures [21–23]. Furthermore, currently the CPUs market provides such technologies, as is the case of Intel’s SpeedStep and AMD’s PowerNow!.…”

“…This software tool is an expected energy-aware extension of the WorkflowSim simulator based on Java [19, 20], which incorporates the Dynamic Voltage Frequency Scaling technique [21–23] in a way that real workflows can be processed considering a regulation of the voltage and frequency in the computing resources as done in current CPUs. It must be underlined that WorkflowSim is the only Cloud Computing simulator nowadays that allows the execution of real workflows considering the event of failures and different amount of workload in each server [19].…”

Adaptive variable sampling model for performance analysis in high cache-performance computing environments

“…Many researchers have suggested system performance profiling-based approaches for system optimization [ [14] , [15] , [16] , [17] , [18] , [19] , [41] , [42] , [43] ]. Due to the development of the architecture and the complexity of the composition, as shown in ( Fig.…”