A Gaussian Recursive Filter Parallel Implementation with Overlapping

Luca, Pasquale De; Galletti, Ardelio; Marcellino, Livia

doi:10.1109/sitis.2019.00105

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…The following tests, executed on the aforementioned hardware, have been designed to prove the accuracy of the Algorithm 3, its performance in terms of execution time, and to compare it with a similar code which implements the first-order Gaussian recursive filter [9]. All execution times reported in the following tests are taken as averages of 10 runs.…”

Section: Resultsmentioning

confidence: 99%

“…. However, to avoid a distortion effect, similar to the one discussed in [9], that is a large perturbation on the blocks boundary entries due to the application of the third order RF to each block, an overlapping procedure is also introduced. That is, each block s (0),m includes further 2m entries of the input signal, by creating overlapping areas shared by all couples of subsequent blocks.…”

Section: Gpu-parallel Software For the Third Order Rfmentioning

confidence: 99%

“…In [9], we presented an accelerated implementation, on GPU environment, of the K-iterated first-order Gaussian RF for 1D signals. The implementation is performed together with a suitable memory strategy which involves the strong usage GPU memory.…”

Section: Introductionmentioning

confidence: 99%

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GPU-CUDA Implementation of the Third Order Gaussian Recursive Filter

2021

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Section: Resultsmentioning

confidence: 99%

Section: Gpu-parallel Software For the Third Order Rfmentioning

confidence: 99%

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GPU-CUDA Implementation of the Third Order Gaussian Recursive Filter

2021

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“…In addition, many research works have optimized the performance of different aspects in crowdsourcing [2], [20], [21], [39]. In particular, [20] proposes a new online cost sensitive framework to batch atomic tasks, which can reduce the cost of each atomic task.…”

Section: Related Workmentioning

confidence: 99%

Online Task Scheduling With Workers Variabilities in Crowdsourcing

Cai

2021

IEEE Access

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Crowdsourcing system, which utilizes many workers to process computer-complexity tasks, has become an effective platform in today's online labor markets. In a crowdsourcing system, maximizing the total utility is one key design goal. This goal is extremely hard because a computer-complexity task can be a multi-dimensional large-scale task that contains thousands or millions of atomic tasks. In online situation, we need to consider both the varying service of workers and future unknown task arrivals. As we know, none of the previous work considers a multi-dimensional large-scale task assignment for utility maximization. In this paper, an online framework is proposed to solve this optimization problem by running atomic tasks in parallel on workers. To estimate worker service rates, we consider each varying worker as an arm for a multi-armed bandit in crowdsourcing system. We design the online scheduling algorithm from a bandit perspective by Online Convex Optimization (OCO) techniques. We prove that our designed algorithm can yield a sublinear regret bound. Finally, we show that our designed algorithm is better than the baseline algorithms by nearly 10% for the total utility achieved.INDEX TERMS Multi-armed bandit, online optimization, multi-dimensional large-scale crowdsourcing tasks, regret bound.

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“…In this section, we describe the technical background of the modern GPU using CUDA terminology. We introduce some of the GPU techniques according to the descriptions in prior work [26]. Figure 1 shows an overview of the system provided with a GPU accelerator.…”

Section: Introductionmentioning

confidence: 99%

Toward OS-Level and Device-Level Cooperative Scheduling for Multitasking GPUs

et al. 2020

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As one of the most popular accelerators, the graphics processing unit (GPU) has been extensively adopted throughout the world. With the burst of new applications and the growing scale of data, co-running applications on limited GPU resources has become increasingly important due to its dramatic improvement in overall system efficiency. Quality of service (QoS) support among concurrent generalpurpose GPU (GPGPU) applications is currently one of the most trending research topics. Prior efforts have been focused on providing QoS support either with OS-level or device-level scheduling methods. Each of these scheduling methods possesses pros and cons and may be unable to independently cover all the scheduling cases. In this paper, we propose a cooperative QoS scheduling scheme (C-QoS) that consists of operating-system-level (OS-level) scheduling and device-level scheduling. Our proposed scheme can control the progress of a kernel and provide thorough QoS support for concurrent applications in multitasking GPUs. Due to the accurate resource management of the copy engine and execution engine, C-QoS achieves QoS goals 23.33% more often than state-of-the-art QoS support mechanisms. The results demonstrate that cooperative methods achieve 17.27% higher system utilization than uncooperative methods. INDEX TERMS Multitasking, parallel architectures, quality of service.

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A Gaussian Recursive Filter Parallel Implementation with Overlapping

Cited by 14 publications

References 14 publications

GPU-CUDA Implementation of the Third Order Gaussian Recursive Filter

GPU-CUDA Implementation of the Third Order Gaussian Recursive Filter

Online Task Scheduling With Workers Variabilities in Crowdsourcing

Toward OS-Level and Device-Level Cooperative Scheduling for Multitasking GPUs

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