Titlebook: Benchmarking, Measuring, and Optimizing; 14th BenchCouncil In Ana Gainaru,Ce Zhang,Chunjie Luo Conference proceedings 2023 The Editor(s) (i

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nettle

Adrenaline

MSDBench: Understanding the?Performance Impact of?Isolation Domains on?Microservice-Based IoT Deployport microservices for IoT. These results indicate that deployment choices can have a dramatic impact on microservices performance, and thus, MSDBench is a useful tool for developers and researchers in this space.

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Conference proceedings 2023zation, Bench 2022, held virtually in November 2022..The 10 revised full papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: Architecture and System,?Algorithm and Dataset,?Network and Memory..

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EAIBench: An Energy Efficiency Benchmark for?AI Trainingrk introduces a new metric to quickly and accurately benchmark AI training workloads’ energy efficiency, called the Energy-Delay Product of one Epoch (EEDP). The EEDP is calculated based on the product of the energy and time consumption within one training epoch, where one epoch refers to one traini

GRIPE

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Open Source Software Supply Chain Recommendation Based on?Heterogeneous Information Networkthe personalized nonlinear fusion function into the matrix decomposition model for open source project recommendation. Finally, this paper makes a large number of comparative experiments based on the real GitHub open data set, and compares it with other project recommendation methods to verify the e

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BasicTS: An Open Source Fair Multivariate Time Series Prediction Benchmark On the one hand, for a given MTS prediction model, BasicTS evaluates its ability based on rich datasets and standard pipelines. On the other hand, BasicTS provides users with flexible and extensible interfaces to facilitate convenient designing and exhaustive evaluation of new models. In addition,

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Benchmarking Object Detection Models with?Mummy Nuts Datasetsnefits of selecting models using our Augmented dataset over the Original dataset. CNN Models overall see an increase in recall values during inference by an average of 2.77X (with the highest increase as YOLOv3 by 6.5X). For performance, over both Original and Augmented datasets, the model training

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