CKAN @ IoT Lab2024 · Text
SLICES-SC PublicationsList of all the publications related to the SLICES-SC project.
CKAN @ IoT Lab2024 · Archive
Distributed machine learning to analyse time series data for greening RIsProcess time series data with machine learning coming from research infrastructures for the purpose of greening and to identify patterns and correlations that can inform the development of more efficient algorithms and protocols. Tests ran in the imec GPULab environment in various configurations. The dataset contains the resulting data for the experiment.
CKAN @ IoT Lab2024 · Archive · unknown
The CAP Babel Machine (Phase 2)We present an open-source and freely available natural language processing system designed for comparative policy studies. Manually labeling large corpora can be tedious and often demands extensive domain expertise. Recent advancements in machine learning and natural language processing hold the potential for language models to surpass human-level accuracy in text classification tasks. In our expe
CKAN @ IoT Lab2024 · Text
LeonR&D IoT Lab: Measurements from 8 environmental sensors of a room (id-106) for 1 month (second stage)The JSON file contains data collected from 8 environmental sensors deployed in a single room for one-month period. The data is structured as a set of objects, each representing a measurement taken by the 8 sensors at a specific time. Each object has two fields: "columns" and "values". The "columns" field is an array of strings that represent the names of the columns in the "values" array. The "val
CKAN @ IoT Lab2024 · Text
LeonR&Do IoT Lab: Total power consumption of a house (id-2) for 1 year period (second stage)The JSON file contains information about the total power consumption of a house for one year. The data is stored in a series of objects, with each object representing a measurement of the power consumption at a specific time. The columns field is an array of strings that represent the names of the columns in the values array. The values field is an array of arrays, with each inner array representi
CKAN @ IoT Lab2024 · Text
LeonR&Do IoT Lab: Total power consumption of a house (id-1) for 1 year period (second stage)The JSON file contains information about the total power consumption of a house for one year. The data is stored in a series of objects, with each object representing a measurement of the power consumption at a specific time. The columns field is an array of strings that represent the names of the columns in the values array. The values field is an array of arrays, with each inner array representi
CKAN @ IoT Lab2024 · Text
LeonR&D IoT Lab: Measurements from 8 environmental sensors of a room (id-105) for 1 month (second stage)The JSON file contains data collected from 8 environmental sensors deployed in a single room for one-month period. The data is structured as a set of objects, each representing a measurement taken by the 8 sensors at a specific time. Each object has two fields: "columns" and "values". The "columns" field is an array of strings that represent the names of the columns in the "values" array. The "val
CKAN @ IoT Lab2024 · dataset · unknown
Preprocessing the English, German, Spanish, and Portuguese Corpora of the European Literary Text Collection (ELTeC) Using the AVOBMAT Multilingual Text Mining ToolThe results of the preprocessing the English, German, Spanish and Portuguese corpora of the European Literary Text Collection (ELTeC) with the AVOBMAT (Analysis and Visualization of Bibliographic Metadata and Texts) multilingual research tool. With AVOBMAT, this data can be used to perform a wide range of dynamic text and data mining tasks. For more information, see avobmat.hu .
CKAN @ IoT Lab2024 · Excel spreadsheet
NVIDIA V100 GPU CUDA kernel FMA operation performance benchmarkNVIDIA V100 GPU CUDA kernel performance for Fused-Multiply-Add (FMA) operations.
CKAN @ IoT Lab2024 · Excel spreadsheet
NVIDIA V100 GPU CUDA kernel data load/store performance benchmarkResults of CUDA kernel data load performance (NVIDIA V100 GPU card)
CKAN @ IoT Lab2024 · Excel spreadsheet
NVIDIA V100 Fast Fourier Transform benchmark resultsSingle precision FFT execution benchmark results in function of FFT size and batch size.
CKAN @ IoT Lab2024 · Excel spreadsheet
GPULab HGX-2 File I/O benchmark resultsFilesystem performance of the IMEC GPULab HGX-2 system for transferring data from storage to host memory.
CKAN @ IoT Lab2024 · Excel spreadsheet
NVIDIA V100 memory latency benchmark resultsV100 GPU memory hierarchy latency test (register-L1/L2 cache-global memory) benchmark results.
CKAN @ IoT Lab2024 · Excel spreadsheet
NVIDIA V100 matrix multiply benchmark resultsNVIDIA V100 GPU benchmark results for various matrix-matrix multiplication implementations.
CKAN @ IoT Lab2024 · Table · CSV
Performance evaluation of Horovod distributed deep learning cluster on the imec infrastructurePerformance evaluation based on the ImageNet dataset, Resnet50 models, and the imec Virtual Wall 2 and GPULab environments in various configurations. The dataset contains the resulting data along with the source code used for the experiment.
CKAN @ IoT Lab2024 · Excel spreadsheet
CUDA kernel global memory data transfer bandwidth benchmarkGlobal memory bandwidth measured from a compute kernel on an NVIDIA HGX-2 GPU system using V100 cards.
CKAN @ IoT Lab2024 · Archive · unknown
The CAP Babel MachineWe present an open-source and freely available natural language processing system designed for comparative policy studies. Manually labeling large corpora can be tedious and often demands extensive domain expertise. Recent advancements in machine learning and natural language processing hold the potential for language models to surpass human-level accuracies in text classification tasks. In our ex
CKAN @ IoT Lab2023 · dataset
Sci-Fi ExperimentThese experimental measurements have been collected by deploying 3 IEEE 802.11(ax) wireless links. More specifically, 3 Access Points (APs) with 1 Station (STA) associated with each of them. As the experimental scope was to examine the effects of contention / interference in ultra-dense networks, the aforementioned links were deployed in close proximity (~50m2). The analytical AP capabilities are
CKAN @ IoT Lab2023 · Text · unknown
Etcd - Raft outputsThree outputs from Etcd instances operating on an NITOS node (nitos-1) and two Open5GLab nodes (eurecom-1 & eurecom-2). Since the initial size of the three output fiiles is too large , we kept only the messages related to the Raft election. The dataset is one of the outcomes of the Raft4CC experiments. Each node's Etcd instance runs for 10 seconds before failing for 5 seconds, and this cycle is re
CKAN @ IoT Lab2023 · Text
Deliverable D6.4 Review of non-ICT Public Awareness and Behavioural Change InterventionsCKAN @ IoT Lab2023 · Text
Synergy Group DigitalWater2020CKAN @ IoT Lab2023 · Table · CSV
Data set: Industrial IoT-driven remote path planningThis compressed file contains data from three different experiments during the IIoT-REPLAN experimentation phase (Industrial IoT-drive remote path planning). IIoT-REPLAN was funded by an open call from the H2020 Fed4FIRE+ project.
CKAN @ IoT Lab2023 · Text
LeonR&Do IoT Lab: Total power consumption of a house (id-3) for 1 year periodThe JSON file contains information about the total power consumption of a house for one year. The data is stored in a series of objects, with each object representing a measurement of the power consumption at a specific time. The columns field is an array of strings that represent the names of the columns in the values array. The values field is an array of arrays, with each inner array representi
CKAN @ IoT Lab2023 · Text
IoT Lab consumption 4Electrical consumption of one computer in the IoT Lab office, part 4.
CKAN @ IoT Lab2023 · Excel spreadsheet
Experimental Results for the AERO 5G projectThe corresponding results refer to the experiments conducted during the life of the Fed4FIRE+ project entitled: "AERO 5G (Augmented Reality Tour Guide Architecture for 5G)". The public availability of the results aim to help future experimenters and researchers to obtain some intuition with regard to the benefits of 5G for content-based, bandwidth consuming, MAR applications.