Multi-objective DSE algorithms' evaluations on processor optimization

Chis, Radu; Vintan, Maria; Vintan, Lucian

doi:10.1109/iccp.2013.6646076

Cited by 5 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use different hyper-heuristic algorithms to decide on search operators for meta-heuristics to improve solution quality and compare their performance. Chis et al [26] use the Framework for Automatic Design Space Exploration to compare the performance of different multi-objective meta-heuristics. The authors show that all algorithms find similar Pareto front approximations with good solution quality.…”

Section: Related Workmentioning

confidence: 99%

“…The data type key defines the data type of the input parameter option, where we accept int and double (Section III-E4, line 25,29). The min and max keys allow the user to specify the value range the input parameter can take (Section III-E4, line 26,27,30,31). Finally, the strategies key allows the user to define for which adaptation planning strategy this input parameter is meaningful by defining a list of strategies (Section III-E4, line 33).…”

Section: Model-based Reasoning Process Model Learning Process Processmentioning

confidence: 99%

“…This idea of combined reasoning and learning can be found in the SeAC research area, whose ideas and approaches will be applied in this work. There are several approaches to situation detection [11], [12], [13], [14], [15], [16], [17], algorithm selection [18], [19], [20], [21], [22], and parameter optimization [23], [24], [25], [26], [27] especially in the SAS literature. However, there is no integrated approach that combines these ideas into a mechanism that is generalizable and applicable to a variety of use cases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Case Study on Optimization of Platooning Coordination

Hadry¹,

Kounev²,

Krupitzer³

2021

Preprint

View full text Add to dashboard Cite

In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close intervehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one platooning coordination strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Model-based Reasoning Process Model Learning Process Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Case Study on Optimization of Platooning Coordination

Hadry¹,

Kounev²,

Krupitzer³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This domain-knowledge was integrated in the mutation genetic operator, with benefits in reducing the search time and improving the solutions' quality, as presented in [1] and [2]. For the further reducing of the search time, distributed evaluations of the individuals are allowed; a database saves the simulation results for future reuse, while checkpointing and error recovery mechanisms oversee the simulations, so that the DSE process is not started again from scratch.…”

Section: A Fadsementioning

confidence: 99%

“…D. student Horia Calborean under the supervision of Prof. Lucian Vintan at the "Lucian Blaga" University of Sibiu. Computation-intensive searches using state of the art evolutionary multi-objective algorithms, guided by the human experience are automatically performed by FADSE as presented in our previous works [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective hardware-software co-optimization for the SNIPER multi-core simulator

Chis

Vintan

2014

2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP)

Self Cite

View full text Add to dashboard Cite

Modern complex microarchitectures with multicore systems like CPUs, APUs (accelerated processing units) and GPUs require hundreds or thousands of hardware parameters to be fine-tuned to get the best results regarding different objectives like: performance, hardware complexity (integration area), power consumption, temperature, etc. These are only a few of the objectives needed to be taken into consideration when designing a new multicore system. Exploring this huge design space requires special tools like automatic design space exploration frameworks to optimize the hardware parameters. Although the microarchitecture might be very complex, the performance of the applications is also highly dependent on the degree of software optimization. This adds a new challenge to the DSE process. In this paper, using the multi-objective design space exploration tool FADSE, we tried to optimize the hardware and software parameters of the multicore SNIPER simulator running SPLASH-2 benchmarks suite. We optimized the hardware parameters (nr cores, cache sizes, cache associativity, etc.) and software parameters (GCC optimizations, threads, and scheduler) values that have been varied during the DSE process and shown the impact of these parameters on the optimization's multi-objectives (performance, area and power consumption). Furthermore, for the best found Pareto configurations the temperatures will be computed so that in the end we will have a 4-dimensional objective space.

show abstract