AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR

Ahmed, Muhammad Mahmood; Usman, Sardar; Ashraf, Muhammad Usman; Alghamdi, Ahmed Mohammed; Bahaddad, Adel A.; Almarhabi, Khalid Ali

doi:10.3390/app12147073

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Numerous existing approaches leverage traditional machine learning environments for intrusion detection. Robust anomaly detection methods utilizing artificial neural networks (ANN) and deep learning surpass the limitations of conventional approaches [12][13][14][15]. The adaptability of ANN features renders them applicable across diverse domains, with a specific focus on enhancing intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

A Secured Intrusion Detection System for Mobile Edge Computing

Alsubhi

2024

Applied Sciences

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With the proliferation of mobile devices and the increasing demand for low-latency and high-throughput applications, mobile edge computing (MEC) has emerged as a promising paradigm to offload computational tasks to the network edge. However, the dynamic and resource-constrained nature of MEC environments introduces new challenges, particularly in the realm of security. In this context, intrusion detection becomes crucial to safeguard the integrity and confidentiality of sensitive data processed at the edge. This paper presents a novel Secured Edge Computing Intrusion Detection System (SEC-IDS) tailored for MEC environments. The proposed SEC-IDS framework integrates both signature-based and anomaly-based detection mechanisms to enhance the accuracy and adaptability of intrusion detection. Leveraging edge computing resources, the framework distributes detection tasks closer to the data source, thereby reducing latency and improving real-time responsiveness. To validate the effectiveness of the proposed SEC-IDS framework, extensive experiments were conducted in a simulated MEC environment. The results demonstrate superior detection rates compared to traditional centralized approaches, highlighting the efficiency and scalability of the proposed solution. Furthermore, the framework exhibits resilience to resource constraints commonly encountered in edge computing environments.

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Section: Related Workmentioning

confidence: 99%

A Secured Intrusion Detection System for Mobile Edge Computing

Alsubhi

2024

Applied Sciences

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“…It can reduce the time complexity of models by deleting certain variables. To extract the important information from a large amount of data, machine learning algorithms are frequently applied (Ahmed et al, 2022).…”

Section: Machine Learning and Feature Selectionmentioning

confidence: 99%

Applying sustainable development goals in financial forecasting using machine learning techniques

Chang,

Lee,

Lee

2023

Corp Soc Responsibility Env

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This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts.

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“…GPU-based SpMV acceleration has been studied extensively [7][8][9][10][12][13][14][15][16][17][18][19][20][21]. Different optimization techniques have been proposed, focusing on introducing new storage format [7,8], threads distribution [9,16], shared memory leverage [12], automatic format selection [13,20], performance analysis and autotuning [14,18,19,21], and load balancing [15,17].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Zeng,

Zou

2023

Electronics

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Sparse matrix-vector multiplication (SpMV) is central to many scientific, engineering, and other applications, including machine learning. Compressed Sparse Row (CSR) is a widely used sparse matrix storage format. SpMV using the CSR format on GPU computing platforms is widely studied, where the access behavior of GPU is often the performance bottleneck. The Ampere GPU architecture recently from NVIDIA provides a new asynchronous memory copy instruction, memcpy_async, for more efficient data movement in shared memory. Leveraging the capability of this new memcpy_async instruction, we first propose the CSR-Partial-Overlap to carefully overlap the data copy from global memory to shared memory and computation, allowing us to take full advantage of the data transfer time. In addition, we design the dynamic batch partition and the dynamic threads distribution to achieve effective load balancing, avoid the overhead of fixing up partial sums, and improve thread utilization. Furthermore, we propose the CSR-Full-Overlap based on the CSR-Partial-Overlap, which takes the overlap of data transfer from host to device and SpMV kernel execution into account as well. The CSR-Full-Overlap unifies the two major overlaps in SpMV and hides the computation as much as possible in the two important access behaviors of the GPU. This allows CSR-Full-Overlap to achieve the best performance gains from both overlaps. As far as we know, this paper is the first in-depth study of how memcpy_async can be potentially applied to help accelerate SpMV computation in GPU platforms. We compare CSR-Full-Overlap to the current state-of-the-art cuSPARSE, where our experimental results show an average 2.03x performance gain and up to 2.67x performance gain.

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AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR

Cited by 5 publications

References 38 publications

A Secured Intrusion Detection System for Mobile Edge Computing

A Secured Intrusion Detection System for Mobile Edge Computing

Applying sustainable development goals in financial forecasting using machine learning techniques

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

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