Issue No. 09 - Sept. (2015 vol. 26)
Shouzhen Gu , College of Computer Science, Chongqing University, Chongqing, China
Qingfeng Zhuge , College of Computer Science, Chongqing University and the Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, China
Juan Yi , College of Computer Science, Chongqing University, Chongqing, China
Jingtong Hu , School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK
Edwin Hsing-Mean Sha , College of Computer Science, Chongqing University and the Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, China
Multi-core processors have been adopted in modern embedded systems to meet the ever increasing performance requirements. Scratchpad memory (SPM), a software-controlled on-chip memory, has been used in embedded systems as an alternative to hardware-controlled cache due to its advantage in die area, power consumption, and timing predictability. SPMs in multi-core systems can be accessed by both local core and remote cores. In order to alleviate data contention on a SPM unit, multi-port SPMs are employed in multi-core systems. In such systems, proper task scheduling and data assignment can significantly improve the overall performance by exploring the parallelism of computation tasks and concurrent data accesses on SPMs. Since scheduling for multi-core systems is NP-Complete in general. In this paper, we propose an ILP formulation to optimally determine the task scheduling and data assignment on multi-core systems with multi-port SPMs. Since ILP takes exponential time to finish, we also propose a heuristic method, including the
task assignment with remote access reduced (TARAR) algorithm and the minimum memory access cost (MMAC) algorithm, to obtain near optimal solutions within polynomial time. According to the experimental results, the ILP formulation can improve the system performance by 23.02 percent over the HAFF algorithm on average, while the heuristic algorithm can improve the system performance by 16.48 percent over HAFF on average.
Schedules, Heuristic algorithms, Multicore processing, Optimal scheduling, Clocks, System-on-chip
S. Gu, Q. Zhuge, J. Yi, J. Hu and E. H. Sha, "Optimizing Task and Data Assignment on Multi-Core Systems with Multi-Port SPMs," in IEEE Transactions on Parallel & Distributed Systems, vol. 26, no. 9, pp. 2549-2560, 2015.