Fast profiling framework and race detection for heterogeneous system

Lai, Cheng Kung; Yeh, Chih Wei; Tu, Chia Heng; Hung, Shih-Hao

doi:10.1016/j.sysarc.2017.10.010

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…From a number of different models, the proposed approach generates the software infrastructure required to connect different memory spaces using communication libraries. Another example is presented in [22], in which the authors propose a GPU interface to identify race conditions through simulations.…”

Section: Related Workmentioning

confidence: 99%

Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms

Andrade¹,

Crnković²,

Bosch³

2020

Preprint

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The most important way to achieve higher performance in computer systems is through heterogeneous computing, i.e., by adopting hardware platforms containing more than one type of processor, such as CPUs, GPUs, and FPGAs. Several types of algorithms can be executed significantly faster on a heterogeneous platform. However, migrating CPU-executable software to other types of execution platforms poses a number of challenges to software engineering. Significant efforts are required in such type of migration, particularly for re-architecting and re-implementing the software. Further, optimizing it in terms of performance and other runtime properties can be very challenging, making the process complex, expensive, and errorprone. Therefore, a systematic approach based on explicit and justified architectural decisions is needed for a successful refactoring process from a homogeneous to a heterogeneous platform. In this paper, we propose a decision framework that supports engineers when refactoring software systems to accommodate heterogeneous platforms. It includes the assessment of important factors in order to minimize the risk of recurrent problems in the process. Through a set of questions, practitioners are able to formulate answers that will help in making appropriate architectural decisions to accommodate heterogeneous platforms. The contents of the framework have been developed and evolved based on discussions with architects and developers in the automotive domain.

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Section: Related Workmentioning

confidence: 99%

Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms

Andrade¹,

Crnković²,

Bosch³

2020

Preprint

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“…On the other hand, 7 papers aimed at proposing a solution related to the problem of load balancing, including workload adjustment and resource management approaches. We identified 5 studies that aimed at either discussing or proposing solutions directly referring to architectural concerns: P4 [10], P11 [11], P22 [12], P23 [13] and P28 [9].…”

Section: Number Of Citationsmentioning

confidence: 99%

A Review on Software Architectures for Heterogeneous Platforms

Andrade

Crnković

2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a single processor by increasing its clock frequency and mounting more transistors so more calculations could be executed. However, it is known that the physical limits of such processors are being reached, and one way to fulfill such increasing computing demands has been to adopt a strategy based on heterogeneous computing, i.e., using a heterogeneous platform containing more than one type of processor. This way, different types of tasks can be executed by processors that are specialized in them. Heterogeneous computing, however, poses a number of challenges to software engineering, especially in the architecture and deployment phases. In this paper, we conduct an empirical study that aims at discovering the state-of-the-art in software architecture for heterogeneous computing, with focus on deployment. We conduct a systematic mapping study that retrieved 28 studies, which were critically assessed to obtain an overview of the research field. We identified gaps and trends that can be used by both researchers and practitioners as guides to further investigate the topic.

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“…NVIDIA's Jetson is a feasible enabler for the introductory phase of machine and computer vision due to its low-power processing capability that it offers as one of forefronts in artificial-intelligence hardware development for computer vision [28]- [30]. The CPU-graphics processing unit (GPU) architecture of Jetson Nano [31], [32], enables the CPU to load faster while the GPU seamlessly runs the machine-learning techniques. It has a sleek design, is portable, and uses little power, making it perfect for application domains which require constraints in weight and power.…”

Section: Introductionmentioning

confidence: 99%

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Al-Selwi

Hassan

Ghani

et al. 2023

IJ-AI

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Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

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Fast profiling framework and race detection for heterogeneous system

Cited by 13 publications

References 5 publications

Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms

Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms

A Review on Software Architectures for Heterogeneous Platforms

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

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