Volume 3, Issue 1

Performance and power consumption analysis and characterization for computational benchmarks is important for processor designers and benchmark developers. In this paper, we characterize and analyze different High Performance Computing workloads. We analyze benchmarks characteristics and behavior on various processors and propose a performance estimation analytical model to predict performance for different processor microarchitecture parameters. Performance model is verified to predict performance within <5% error margin between estimated and measured data for different processors. We also propose a power estimation analytical model to estimate power consumption with low error deviation.

Fault tolerance is an important property in computational grids since the resources are geographically distributed. Job checkpointing is one of the most common utilized techniques for providing fault tolerance in computational grids. The efficiency of checkpointing depends on the choice of the checkpoint interval. Inappropriate checkpointing interval can delay job execution. In this paper, a fault-tolerant scheduling system based on checkpointing technique is presented and evaluated. When scheduling a job, the system uses both average failure time and failure rate of grid resources combined with resources response time to generate scheduling decisions. The system uses the failure rate of the assigned resources to calculate the checkpoint interval for each job. Extensive simulation experiments are conducted to quantify the performance of the proposed system. Experiments have shown that the proposed system can considerably improve throughput, turnaround time, grid load and failure tendency of computational grids.

Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.