Optimization model for memory bandwidth usage in x-ray image enhancement

Albers, Rob; Suijs, Eric

doi:10.1117/12.765123

Cited by 6 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [9], Albers et al use another model for the mapping and partitioning of computation to threads, but the scheduling order of the application is not taken into account. Furthermore, the focus is on the reduction of latency and not primarily on the reduction of jitter.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of scheduling heuristics for jitter reduction of real-time streaming applications on multi-core general purpose hardware

Westmijze

Bekooij

Smit

et al. 2011

2011 9th IEEE Symposium on Embedded Systems for Real-Time Multimedia

View full text Add to dashboard Cite

The real-time system research community has paid a lot of attention to the design of safety critical hard real-time systems for which the use of non-standard hardware and operating systems can be justified. However, stream processing applications like medical imaging systems are often not considered safety critical enough to justify the use of hard real-time techniques that would increase the cost of these systems significantly. Instead commercial off the shelf (COTS) hardware and OS are used, and techniques at the application level are employed to reduce the variation in the end-to-end latency of these imaging processing systems.In this paper, we study the effectiveness of a number of scheduling heuristics that are intended to reduce the latency and the jitter of stream processing applications that are executed on COTS multiprocessor systems. The proposed scheduling heuristics take the execution times of tasks into account as well as dependencies between the tasks, the data structures accessed by the tasks, and the memory hierarchy.Experiments were carried out on a quad core symmetric multiprocessing (SMP) Intel processor. These experiments show that the proposed heuristics can reduce the end-to-end latency with almost 60%, and reduce the variation in the latency with more than 90% when compared with a naive scheduling heuristic that does not consider execution times, dependencies and the memory hierarchy.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluation of scheduling heuristics for jitter reduction of real-time streaming applications on multi-core general purpose hardware

Westmijze

Bekooij

Smit

et al. 2011

2011 9th IEEE Symposium on Embedded Systems for Real-Time Multimedia

View full text Add to dashboard Cite

show abstract

“…In this way, task data blocks remain local in one of the two L2 caches, which avoid cache misses due to tasks that are moving from one core to the other. The interested reader is referred to [1,14] for more details.…”

Section: Memory-bandwidth Analysismentioning

confidence: 99%

“…In more detail for our case study, when image analysis is the main application of interest and not all resources are allocated, the live viewing stream can be enabled as well, with a quality-controlled execution of the image-enhancement tasks. In earlier work [1], we presented a resource-usage model for streaming tasks. This model is useful when the mapping requires multiple processor cores.…”

Section: Setqualitylevelmentioning

confidence: 99%

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

Albers

2011

J Real-Time Image Proc

View full text Add to dashboard Cite

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94-97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management.LowerQualityOfCandidate The function decreases the benefit level of a candidate job, the lowest level is 0. MapJobThe function calls the Resource manager routines to allocate resources defined by the quality benefit level of a job.ReportInsuffcientResources The function reports to the LQoS of a candidate job its inability of job execution.

show abstract

“…The involved image enhancement tasks can be pushed to a lower quality level or even skipped to save (a part of the) assigned resources for other tasks, like the detection of features of interest in image analysis. In earlier work [8], we have presented a resource-usage model for streaming tasks. This model is useful when the mapping requires usage of different processor cores.…”

Section: Task-level Scalabilitymentioning

confidence: 99%