Deadline and energy aware dynamic task mapping and scheduling for Network-on-Chip based multi-core platform

Chatterjee, Navonil; Paul, Suraj; Mukherjee, Paramita; Chattopadhyay, Santanu

doi:10.1016/j.sysarc.2017.01.008

Cited by 27 publications

(18 citation statements)

References 13 publications

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“…Authors in [18] extend the work presented in [32] by incorporating task migration based mapping improvement policy to shift the tasks from heavy loaded processors to appropriate processors. A run-time unified mapping and scheduling technique presented in [4] reduces the communication energy consumption of the mapped application while meeting the task deadline.…”

Section: On-the-fly Task Allocation Techniquesmentioning

confidence: 99%

“…Advancement in VLSI technology has made it possible for designers to integrate a large number of Processing Elements (PEs), Intellectual Property (IP) cores and memory units (MUs) onto a Task allocation at run-time can be performed either with or without pre-computed task mapping results. Several efficient online heuristics have been proposed for assigning tasks of new applications submitted during run-time for on-the-fly allocation [4][5] [32]. But, due to the availability of limited computation time at run-time, task schedulability and optimal mapping may not always be ensured by these heuristics.…”

Section: Introductionmentioning

confidence: 99%

“…During run-time, 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 run-time performance. Experimental results demonstrate that the proposed approach achieves, on an 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.…”

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confidence: 99%

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

Paul

Chatterjee

Ghosal

et al. 2020

ACM Trans. Embed. Comput. Syst.

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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.

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Section: On-the-fly Task Allocation Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Paul

Chatterjee

Ghosal

et al. 2020

ACM Trans. Embed. Comput. Syst.

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“…The mapping and its optimization is a critical stage of any task graph based application running on a Networks-on-Chip (NoC) platform such as SpiNNaker. Several papers report that the mapping strategy can have a high impact on the overall system performance [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Optimized task graph mapping on a many-core neuromorphic supercomputer

Sugiarto

Campos

Dahir

et al. 2017

2017 IEEE High Performance Extreme Computing Conference (HPEC)

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Abstract-This paper presents an approach for improving the overall performance of a general purpose application running as a task graph on a many-core neuromorphic supercomputer. Our task graph framework is based on graceful degradation and amelioration paradigms that strive to achieve high reliability and performance by incorporating fault tolerance and task spawning features. The optimization is applied on an instance of the task graph by performing a soft load balancing on the data traffic between nodes in the graph. We implemented the framework and its optimization on SpiNNaker, a manycore neuromorphic platform containing a million ARM9 processing cores. We evaluate our method using several static mapping examples, where some of them were generated using an evolutionary algorithm. The experiment demonstrates that a performance improvement of up to 8.2% can be achieved when implementing our algorithm on a fully-utilized SpiNNaker communication infrastructure.

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“…片上网络(network-on-chip, NoC)作为共享资源, 极易出现不同应用程序相互干扰的问题, 主要体现在不同应用程序的通信数据流会频繁地在 NoC 的某些节点处相遇, 形成对路由器、传输链路等硬件资源的竞争, 导致无法保证应用程序的网络性能 [3] . 子网隔离方法 [4] 将多个应用程序部署在片上系统的不同连续子网中并行执行, 并采用隔离路由算法将不同应用程序的通信数据流限制在本子网内, 从而解决多个应用程序通信数据流在 NoC 中相互干扰的问题. 为了提高系统利用率并减少碎片率, 通常应用程序的部署子网为不规则子网 [5][6] .…”

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