Master-slave Scheduling Research Articles

A High-Performance Parallelization and Load-Balancing Approach for Modern Power-Systems

Due to the recent trends of chip-miniaturization, the performance of a single-core is plateauing and hence, improving the performance of serial-execution based legacy code has become challenging. Since the expansion in power system operation continues to increase the number of contingencies to be examined, serial-execution platforms present a crucial bottleneck in analyzing sufficiently large number of contingencies within a reasonably small time for performing power-system stability analysis. This paper presents an approach to parallelize power system contingency analysis over multicore processors using Chapel language. To achieve load-balancing for avoiding wastage of computation resources, the authors use efficient work-stealing scheduling. They discuss the important features of Chapel and design choices which enable us to achieve high performance gains. The approach is evaluated using hundreds of contingencies of a large 13029-bus power system. The authors compare the performance of serial-execution with that obtained using 2, 4, 8 and 16 threads. The simulation results have shown that the approach outperforms a conventional scheduling technique, namely master-slave scheduling and also scales effectively with increasing number of threads. It is believed that the performance gains obtained from the approach would be highly useful for control center operators in analyzing a large number of contingencies and thus taking suitable corrective and preventive action against catastrophic events. Also, the insights gained from these experiments will be useful in several business enterprises.

PARAGON: an approach for parallelization of power system contingency analysis using Go programming language

SUMMARY With the recent emphasis on N-k contingency analysis (CA) and expansion of power systems, the computational demand of CA has greatly increased to ensure power system security. The serial execution platforms are proving to be insufficient to cater to this demand. This paper presents PARAGON, an efficient approach for parallelization of power system contingency analysis using Go programming language. PARAGON uses Google's Go language to parallelize CA over multiple cores using goroutines. The main contribution of this paper is implementation of work-stealing scheduler on widely used multicore processors using concurrent programming language, which can be easily adopted by power system control centers to accelerate legacy codes. Simulations have been performed using a large power system with hundreds of contingencies, and contingency analysis has been parallelized using 2, 4, 8, 12, and 16 cores. The results show that PARAGON provides large computational advantage over serial execution and also outperforms a conventional scheduling technique, namely master-slave scheduling. Copyright © 2014 John Wiley & Sons, Ltd.

Proactive task scheduling and stealing in master-slave based load balancing for parallel contingency analysis

With increasing emphasis on analyzing N−k contingencies, use of parallel resources has become imperative. Parallelization imposes the requirement of load-balancing for achieving high resource usage efficiency. Conventional static allocation based scheduling techniques fail to achieve load balancing. To address this limitation, master-slave scheduling (MSS) has been used; however, in MSS, after task completion, slave processors wait for the next task to arrive leading to idle-wait. In the case of contention at master, the idle-wait could become significant and degrade the performance of the MSS algorithm.We present a technique to combine the advantage of proactive task scheduling and stealing with the simplicity of MSS. We refer to it as PTMSS. In PTMSS, master proactively queues an extra task at the slave processor, such that on completion of a task, the next task is immediately started. Further, when master runs out of the tasks, it steals a queued task from one slave and allocates to another slave which has completed its tasks. Simulation experiments have been conducted on a large power system with 13,029 buses and thousands of contingencies have been analyzed. The results show that PTMSS performs better than conventional MSS and also offers significant computational gains over serial execution.

The impact of heterogeneity on master-slave scheduling

In this paper, we assess the impact of heterogeneity on scheduling independent tasks on master-slave platforms. We assume a realistic one-port model where the master can communicate with a single slave at any time. We target both on-line and off-line scheduling problems, and we focus on simpler instances where all tasks have the same size. While such on-line problems can be solved in polynomial time on homogeneous platforms, we show that there does not exist any optimal deterministic algorithm for heterogeneous platforms. Whether the source of heterogeneity comes from computation speeds, or from communication bandwidths, or from both, we establish lower bounds on the competitive ratio of any deterministic algorithm. We provide such bounds for the most important objective functions: the minimization of the makespan (or total execution time), the minimization of the maximum response time (difference between completion time and release time), and the minimization of the sum of all response times. Altogether, we obtain nine theorems which nicely assess the impact of heterogeneity on on-line scheduling. For off-line scheduling, we prove several result for problems with release dates, either optimality or NP-hardness. These theoretical contributions are complemented on the practical side by the implementation of several heuristics on a small but fully heterogeneous MPI platform. Our results show the superiority of those heuristics which fully take into account the relative capacity of the communication links.