Accommodating workload diversity in chip multiprocessors via dating tips for girlfriend

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For the abundance of computing resources, a fundamental problem is how to map application on it, or how many cores should be assigned for each application.

As the available concurrency varies widely for diverse applications or different execution phases of an individual program, the number of resource allocated should be adjusted dynamically for high utilization rate while not compromising performance.

We find that using under-performing cores improves performance by 16% on average and saves CPU energy by up to 16% across the NAS and SPEC-OMP benchmarks on a quad-core AMD platform.

Workload balancing via dynamic partitioning yields results within 5% of the overall ideal value.

The main objective of this paper is to survey and discuss the current power management techniques. in 2013 with an overall grade of excellent with honors from Mansoura University. Professor at Computers Engineering and Control systems Dept.––Faculty of Engineering––Mansoura University, Egypt.

Moreover, it proposes a new technique for power management in multi-core processors based on that survey. Attia is a teaching assistant at Computers and Control Systems Engineering Department, Mansoura University. His main research interests include Computer Architecture and Organization, Heterogeneous Multi-Core Architectures, Power-aware computing and Heterogeneous Parallel Programming. In this paper, aiming at resource management in flexible architecture, an implementation of confidence predictor, referred as speculative depth estimator (SDE), is introduced, which is able to conduct the real-time resource tuning.By applying the speculative depth estimator to dynamic resource tuning, the experiments results show that a good trade-off between concurrency exploitation and resource utilization is achieved.The goal of power management is to maximize performance within a given power budget. His major research interests are Artificial Intelligence such as Genetic Algorithms, Neural Networks, Particle Swarm Optimization, Simulated Annealing, and Fuzzy Logic. Power management techniques must balance between the demanding needs for higher performance/throughput and the impact of aggressive power consumption and negative thermal effects. Also he is interested in the application of AI in Machine Learning, Image Processing, access control and Optimization. However, with non-uniform workload partitioning, we find that using both low and high frequency cores improves performance and reduces energy consumption over just running faster cores.