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Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.


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Proceedings ArticleDOI
11 Dec 2005
TL;DR: This paper introduces two heterogeneous placement algorithms, able to deal with the constraints of the hardware tasks, and presents a task placement mechanism to change the position of a hardware task on the FPGA by manipulating the configuration data of the task.
Abstract: The concept of partial reconfiguration offers the possibility to dynamically place and remove hardware tasks on reconfigurable architectures, like FPGAs. Common placement algorithms, e.g. Best Fit, are designed for homogeneous architectures, since they do not consider any placement constraints of the hardware tasks. Due to the integration of, e.g., dedicated memory, current FPGAs are heterogeneous reconfigurable architectures. In this paper we introduce two heterogeneous placement algorithms, which are able to deal with the constraints of the hardware tasks. Both algorithms are compared to the Best Fit algorithm by using a simulation framework for partially configurable architectures. We propose concepts of an efficient hardware realization of our placement approach with Xilinx Virtex-II FPGAs. Moreover, we present a task placement mechanism to change the position of a hardware task on the FPGA by manipulating the configuration data of the task

40 citations

Patent
30 Jul 2007
TL;DR: In this article, the authors propose a method to detect an attempt to execute an instruction of a task on a processor not supporting the instruction (non-supporting processor) and assign a task to the virtual processor based upon the representing.
Abstract: Methods and arrangements of assigning tasks to processors are discussed. Embodiments include transformations, code, state machines or other logic to detect an attempt to execute an instruction of a task on a processor not supporting the instruction (non-supporting processor). The method may involve selecting a processor supporting the instruction (supporting physical processor). In many embodiments, the method may include storing data about the attempt to execute the instruction and, based upon the data, making another assignment of the task to a physical processor supporting the instruction. In some embodiments, the method may include representing the instruction set of a virtual processor as the union of the instruction sets of the physical processors comprising the virtual processor and assigning a task to the virtual processor based upon the representing.

40 citations

Patent
Dennis L. Venable1
15 Jun 1993
TL;DR: In this paper, a control system for pipelined image processing emulates a multi-tasking environment using a single tasking application, where a number of predefined image processing tasks are provided in a library.
Abstract: A control system for pipelined image processing emulates a multi-tasking environment using a single tasking application. A number of predefined image processing tasks are provided in a library. When a host application needs a processed image from an image source, the host application creates a pipeline of initialized instantiations of one or more of the tasks from the library. When the host application invokes the pipeline, the first data request for the heater of the image travels upstream in a first channel. The processed image header is returned down the first channel. Then a data request for scanlines of image data is sent upstream in a second data channel. The data request ripples upstreamwardly to the upstream-most instantiation of one of the tasks from the task library. The upstream-most instantiation of a task obtains a scan line from an image data source and returns it downstreamwardly to the host application in the second channel. Each instantiation of a task from the task library further operates on the image data. Once all of the scanlines have been processed, the memory allocations and data structures created during initialization are released to free up that memory.

40 citations

Journal ArticleDOI
Siqi Luo1, Xu Chen1, Zhi Zhou1, Xiang Chen1, Weigang Wu1 
TL;DR: To efficiently achieve mutually beneficial task execution, the proposed mechanism groups the devices into multiple micro computing clusters (MCCs) that can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of their performances no worse than local execution or execution in the fog server.
Abstract: Fog computing is envisioned as a promising approach for supporting emerging computation-intensive applications on capacity and battery constrained mobile Internet of Things (IoT) devices. Technically speaking, a massive crowd of devices in close proximity can be harvested and collaborate for computation and communication resource sharing. Hence fog computing enables significant potentials in low-latency and energy-efficient mobile task execution. However, without an efficient incentive mechanism to stimulate resource sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. In particular, to efficiently achieve mutually beneficial task execution, the proposed mechanism groups the devices into multiple micro computing clusters (MCCs). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of their performances no worse than local execution or execution in the fog server. The solution to the MCC formation is devised by both centralized and decentralized schemes and further proven to admit nice properties such as top coalition, core solution, individual rationality and computational efficiency. Extensive numerical studies demonstrate the superior performance of our MCC formation mechanisms.

40 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented two schedulers based on integer linear programming (ILP) that schedule tasks either in the cloud or on fog resources, which differ from existing ones by the use of class of services to select the processing elements on which the tasks should be executed.
Abstract: Fog computing extends cloud services to the edge of the network In such scenario, it is necessary to decide where applications should be executed so that their quality of service requirements can be supported Thus, a cloud-fog system requires an efficient task scheduler to decide the locality where applications should run This paper presents two schedulers based on integer linear programming, that schedule tasks either in the cloud or on fog resources The schedulers differ from existing ones by the use of class of services to select the processing elements on which the tasks should be executed Numerical results evince that the proposed schedulers outperform traditional ones, eg, Random and Round Robin algorithms without causing violation of QoS requirements

40 citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202210
2021695
2020712
2019784
2018721
2017565