Efficient Thread Mapping for Heterogeneous Multicore IoT Systems

Abstract

This paper proposes a thread scheduling mechanism primed for heterogeneously configured multicore systems. Our approach considers CPU utilization for mapping running threads with the appropriate core that can potentially deliver the actual needed capacity. The paper also introduces a mapping algorithm that is able to map threads to cores in anO(N log M)time complexity, whereNis the number of cores andMis the number of types of cores. In addition to that we also introduced a method of profiling heterogeneous architectures based on the discrepancy between the performances of individual cores. Our heterogeneity aware scheduler was able to speed up processing by 52.62% and save power by 2.22% as compared to the CFS scheduler that is a default in Linux systems.

Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

“…For each concurrently executing OpenCL application, the proposed mapping process finds the appropriate number of CPU cores and GPU cores, and the partitioning process identifies an efficient partitioning of the applications' threads between CPU and GPU cores. Birhanu et al [13] proposed the Fastest-Thread-Fastest-Core (FTFC) dynamic thread scheduling mechanism. By dynamically and periodically measuring the CPU utilization of running threads, those threads that exhibit high CPU utilization are assigned to cores that can deliver high performance when needed, whereas those threads with low CPU utilization are assigned to low performance cores.…”

“…Normalize energy e f f iciency = Speedup N t × T (13) The higher value of the normalized energy efficiency means more efficient use of energy. Figure 9 shows that DPTM's energy-performance efficiency is better than the OS_Default and Optimal in all benchmark programs, except for the streamcluster where DPTM's energy-performance efficiency was slightly lower than the Optimal, and vips where DPTM's energy-performance efficiency equaled the Optimal.…”

Distributed computational model for shared processing on Cyber-Physical System environments

“…There are recent scheduling proposals for this kind systems both for similar [28] and for heterogeneous [29] computing components. A particular case is the multicore processing system where the cores are built in the same integrated circuit and they have similar [30] or different features [31]. In order to include the unpredictability and extra delay provided by the network, other approaches for distributing the computation in heterogeneous cluster systems can be applied.…”