Machine Learning Guided Optimal Use of GPU Unified Memory

Xu, Hailu; Emani, Murali; Lin, Pei-Hung; Hu, Liting; Liao, Chunhua

doi:10.1109/mchpc49590.2019.00016

Cited by 10 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we pick a dataset and ML model generated by the XPlacer [17] to study how to FAIRify HPC datasets and ML models. The reason to choose XPlacer is that the authors released raw data with detailed documents explaining how data was generated and processed.…”

Section: Xplacer Datasetsmentioning

confidence: 99%

“…Research activities in high-performance computing (HPC) community have applied machine learning (ML) for various research needs such as performance modeling and prediction [13], memory optimization [17,18], and so on. A typical ML-enabled HPC study generates a large amount of valuable datasets from the HPC experiment outputs.…”

Section: Introductionmentioning

confidence: 99%

“…Section III discusses the methodology we design to evaluate and improve FAIRness of HPC datasets and ML models. Section V presents an example dataset and an example ML model generated from a prior project named XPlacer [17]. Experiment results are shown in Section VI.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study

Lin

Liao

Chen

et al. 2022

2022 Fourth International Conference on Transdisciplinary AI (TransAI)

Self Cite

View full text Add to dashboard Cite

The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. However, these principles have not yet been broadly adopted in the domain of machine learning-based program analyses and optimizations for High-Performance Computing (HPC). In this paper, we design a methodology to make HPC datasets and machine learning models FAIR after investigating existing FAIRness assessment and improvement techniques. Our methodology includes a comprehensive, quantitative assessment for elected data, followed by concrete, actionable suggestions to improve FAIRness with respect to common issues related to persistent identifiers, rich metadata descriptions, license and provenance information. Moreover, we select a representative training dataset to evaluate our methodology. The experiment shows the methodology can effectively improve the dataset and model's FAIRness from an initial score of 19.1% to the final score of 83.0%.

show abstract

Section: Xplacer Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study

Lin

Liao

Chen

et al. 2022

2022 Fourth International Conference on Transdisciplinary AI (TransAI)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors in [62] proposed a novel machine learning approach to find the optimal choice of GPU memory requirements for CUDA applications. The workflow of the proposed approach has two phases: (1) Offline learning; and (2) Online inference.…”

Section: Architecture/platform/framework and Strategymentioning

confidence: 99%

GPU-Based Embedded Intelligence Architectures and Applications

Ang

Seng

2021

Electronics

View full text Add to dashboard Cite

This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications.

show abstract

“…Rather than doing shifting and allocating the memory to the host and device allocate a special pointer that can be used by both CPU and GPU, this is the concept of unified memory allocation [9]. According to recent advancements in unified memory employment, a huge extent of features has been added like page fault handling for GPUs, transferring of data when requested, extra memory allotment for GPUs, and counters for accessing the data [10]. In the past, two distinct AutoSwap and SmartPool strategies have been applied to minimize GPU consumption and it prevents any human intervention [11].…”

mentioning

confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

View full text Add to dashboard Cite

<p>A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.</p>

show abstract

Machine Learning Guided Optimal Use of GPU Unified Memory

Cited by 10 publications

References 10 publications

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study

Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study

GPU-Based Embedded Intelligence Architectures and Applications

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Contact Info

Product

Resources

About