An adaptive algorithm for high‐dimensional integrals on heterogeneous CPU‐GPU systems

Laccetti, Giuliano; Lapegna, Marco; Mele, Valeria; Montella, Raffaele

doi:10.1002/cpe.4945

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…The idea of using GPUs in this framework for better performance cannot be ruled out. 39,40 Additionally, efforts are underway to enhance the proposed approach by incorporating intelligent metadata extraction, enabling the construction of consistent ontologies for smart data dissemination. 41 This integration of intelligent metadata extraction aims to empower the computational environmental science community with a practical and high-performance approach for n-D-georef query, discovery, and selection in real-world applications.…”

Section: Discussionmentioning

confidence: 99%

“…Looking toward future research directions, one area of focus involves optimizing splitting parameters to reduce data further uploading and downloading times. The idea of using GPUs in this framework for better performance cannot be ruled out 39,40 . Additionally, efforts are underway to enhance the proposed approach by incorporating intelligent metadata extraction, enabling the construction of consistent ontologies for smart data dissemination 41 .…”

Section: Discussionmentioning

confidence: 99%

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A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Mellone,

De Vita,

Sánchez‐Gallegos

et al. 2023

Concurrency and Computation

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SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Mellone,

De Vita,

Sánchez‐Gallegos

et al. 2023

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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“…As future work, we plan to improve the algorithm to implement it on heterogeneous multicore CPU / GPU architectures, as done in [ 36 ], and to optimize the portable design of memory accesses to avoid unwanted overhead on Jetson boards with low CUDA capabilities. Moreover, thanks to the introduction of Volta architecture on the recent Nvidia Tegra series, the availability of tensor cores opens up to new algorithmic designs [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

A GPU-Parallel Image Coregistration Algorithm for InSar Processing at the Edge

Romano

Lapegna

2021

Sensors

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Image Coregistration for InSAR processing is a time-consuming procedure that is usually processed in batch mode. With the availability of low-energy GPU accelerators, processing at the edge is now a promising perspective. Starting from the individuation of the most computationally intensive kernels from existing algorithms, we decomposed the cross-correlation problem from a multilevel point of view, intending to design and implement an efficient GPU-parallel algorithm for multiple settings, including the edge computing one. We analyzed the accuracy and performance of the proposed algorithm—also considering power efficiency—and its applicability to the identified settings. Results show that a significant speedup of InSAR processing is possible by exploiting GPU computing in different scenarios with no loss of accuracy, also enabling onboard processing using SoC hardware.

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“…We remark that the previous ideas take inspiration from well known and widely used procedures known as adaptive algorithms, which operate only on subproblems that show poor values of some quality index, such as, for example, the discretization error in the case of the computation of integrals or computational fluid dynamics (e.g., [ 27 , 28 , 29 , 30 ]).…”

Section: Parallelization Strategies For the Adaptive K -Means Algorithmmentioning

confidence: 99%

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

Lapegna

Balzano

Meyer

et al. 2021

Sensors

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The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

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An adaptive algorithm for high‐dimensional integrals on heterogeneous CPU‐GPU systems

Cited by 14 publications

References 31 publications

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

A GPU-Parallel Image Coregistration Algorithm for InSar Processing at the Edge

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

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