Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

Barreiros, Willian; Melo, Alba Cristina Magalhães Alves de; Kong, Jun; Ferreira, Renato; Kurç, Tahsin; Saltz, Joel; Teodoro, George

doi:10.1016/j.jpdc.2022.02.004

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…workloads to compute nodes by meticulously decomposing and scheduling the loop executions of multi-stencil tasks. Likewise, Barreiros et al [26] proposed a novel cost-aware data partitioning strategy to accelerate image-analysis stencil tasks running in CPU-GPU systems. The primary focus is to address the load-balancing problem.…”

Section: Related Workmentioning

confidence: 99%

A compression-based memory-efficient optimization for out-of-core GPU stencil computation

et al. 2023

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Stencil computation is an extensively-utilized class of scientific-computing applications that can be efficiently accelerated by graphics processing units (GPUs). Out-of-core approaches enable a GPU to handle large stencil codes whose data size is beyond the memory capacity of the GPU. However, current research on out-of-core stencil computation primarily focus on minimizing the amount of data transferred between the CPU and GPU. Few studies consider simultaneously optimizing data transfer and kernel execution. To fill the research gap, this work presents a synergy between on-and off-chip data reuse for out-of-core stencil codes, termed SO2DR. First, overlapping regions between data chunks are shared in the off-chip memory to eliminate redundant CPU-GPU data transfer. Secondly, redundant computation at the off-chip memory level is intentionally introduced to decouple kernel execution from region sharing, hence enabling data reuse in the onchip memory. Experimental results demonstrate that SO2DR significantly enhances the kernel-execution performance while reducing the CPU-GPU data-transfer time. Specifically, SO2DR achieves average speedups of 2.78× and 1.14× for five stencil benchmarks, compared to an out-of-core stencil code which is free of redundant transfer and computation, and an in-core stencil code which is free of data transfer, respectively.

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Section: Related Workmentioning

confidence: 99%

A compression-based memory-efficient optimization for out-of-core GPU stencil computation

et al. 2023

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Debesų kompiuterijos lygiagrečiųjų skaičiavimų SAAS efektyvumo tyrimai heterogeninėse architektūrose

Bystrov

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Multi-Thread Parallelizing of Cell Characteristics of Biomedical Images

Pitsun¹

2022

UJIT

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An approach to the parallelization of the process of calculating the quantitative characteristics of cell nuclei on biomedical images (cytological, histological, immunohistochemical) is proposed, which will speed up the process of making a diagnosis. The relevance of this task lies in the fact that there are a large number of micro-objects in the image that need to be investigated, and optimization of time and rational distribution of resources will speed up the stage of calculating the area of cell nuclei and their average brightness level. In the future, these data are stored in the database for further use as a dataset for the tasks of classification, clustering, and intellectual analysis. Modern means of data classification and intellectual analysis are used to make a diagnosis. When using convolutional neural networks, the input data to the classifier are images in the format .jpg, .png, .bmp, etc. Alternative algorithms and data processing tools in most cases require quantitative characteristics. In the case of using biomedical images, the quantitative characteristics are the area, perimeter, circumference, length, and major and lateral axes of the cell nucleus. The area and other characteristics of cell nuclei characterize the normal state or the presence of pathologies. Calculating quantitative characteristics on immunohistochemical and cytological images is time-consuming because the number of cell nuclei in one image can be in the range of 10-20 units. To create a dataset of quantitative characteristics of cell nuclei, it is necessary to perform calculations on a large number of images, which in turn requires significant resources, at a particular time. The parallelization of calculating the biomedical image characteristics is implemented on the basis of computer vision algorithms to select the necessary objects and means of software parallelization of tasks at the thread level to speed up the process of calculating the cell nucleus characteristics. It was established that the existing systems of automated microscopy and diagnostic systems based on images do not have the presence of a large number of characteristics of cell nuclei and do not have mechanisms for parallelizing the process of their calculation. The proposed approach makes it possible to speed up the process of calculating the quantitative characteristics of cell nuclei by 25 %. The relevance of the problem of parallelization is due to the need to process a large amount of data for their further reduction and classification. Thread-level parallelization improves image processing speed and does not require specialized hardware.

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Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

Cited by 4 publications

References 43 publications

A compression-based memory-efficient optimization for out-of-core GPU stencil computation

A compression-based memory-efficient optimization for out-of-core GPU stencil computation

Debesų kompiuterijos lygiagrečiųjų skaičiavimų SAAS efektyvumo tyrimai heterogeninėse architektūrose

Multi-Thread Parallelizing of Cell Characteristics of Biomedical Images

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