Adaptive Task Allocation and Scheduling on NoC-based Multicore Platforms with Multitasking Processors

Abstract

The application workloads in modern multicore platforms are becoming increasingly dynamic. It becomes challenging when multiple applications need to be executed in parallel in such systems. Mapping and scheduling of these applications are critical for system performance and energy consumption, especially in Network-on-Chip– (NoC) based multicore systems. These systems with multitasking processors offer a better opportunity for parallel application execution. Mapping solutions generated at design time may be inappropriate for dynamic workloads. To improve the utilization of the underlying multicore platform and cope with the dynamism of application workload, often task allocation is carried out dynamically. This article presents a hybrid task allocation and scheduling strategy that exploits the design-time results at runtime. By considering the multitasking capability of the processors, communication energy, and timing characteristics of the tasks, different allocation options are obtained at design time. During runtime, based on the availability of the platform resources and application requirements, the design-time allocations are adapted for mapping and scheduling of tasks, which result in improved runtime performance. Experimental results demonstrate that the proposed approach achieves an on average 11.5%, 22.3%, 28.6%, and 34.6% reduction in communication energy consumption as compared to CAM [18], DEAMS [4], TSMM [38], and CPNN [32], respectively, for NoC-based multicore platforms with multitasking processors. Also, the deadline satisfaction of the tasks of allocated applications improves on an average by 32.8% when compared with the state-of-the-art dynamic resource allocation approaches.

Research and optimization of task schedulingalgorithm based on heterogeneous multi-coreprocessor

“…This paper presents a task scheduling method for heterogeneous multi-core processors based on wild horse optimization algorithm. This method combines the global search capability and adaptability of wild horse optimization algorithm with the characteristics of heterogeneous multi-core processor to improve the task scheduling effect and system performance [22]. Through the steps of system modeling, population initialization, wild horse optimization algorithm search, fitness evaluation, selection and iterative search, we can obtain better task scheduling schemes and optimize system performance [23].…”

Run-Time Remapping Algorithm of Dataflow Actors on NoC-Based Heterogeneous MPSoCs

“…In [8], the authors have introduced a hybrid application mapping that combines design-time analysis with run-time mapping in the context of dynamic thermal and reliability-aware resource management. Most of the available methods focus on determining the suitable mapping of tasks before starting the execution of the application [9], [10], [11], [12], [13], [14]. The mapping of actors is also an active topic for other target platforms like Coarse-Grained Reconfigurable Arrays (CGRA) [15] or Field Programmable Gate Array (FPGA) [16].…”

Mapping Computations in Heterogeneous Multicore Systems with Statistical Regression on Program Inputs

“…e combination of heterogeneous processors featuring multiple frequency levels gives programmers many con gurations to choose from when running their applications. However, performing this choice is challenging (Azhar et al 2019;Nejat et al 2020;Nishtala et al 2017;Paul et al 2020;Petrucci et al 2015).…”