Abstract
Developing and testing autonomous driving (AD) systems requires the analysis and storage of more data than ever before. Clients who can deliver insights faster while managing rapid infrastructure growth will be the industry leaders. To deliver these insights faster, the underlying IT and cloud technology must support both new big data as well as traditional applications with security, reliability, and high-performance. To handle massive, unstructured data growth, the solution must scale seamlessly while matching data value to the capabilities and costs of different storage tiers and types.
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References
NooBaa Technology https://www.noobaa.io/technology/
IBM Cloud Direct Link https://www.ibm.com/cloud/direct-link/
Equinix and NVIDIA AI LaunchPad Accelerate AI from Hybrid Cloud to Edge https://blog.equinix.com/blog/2021/06/24/equinix-and-nvidia-ai-launchpad-accelerate-ai-from-hybrid-cloud-to-edge/
NVIDIA Fast Tracks Enterprise AI Development and Deployment: https://blogs.nvidia.com/blog/2021/11/09/launchpad-global-expansion/
GPUDirect RDMA—Direct Communication between NVIDIA GPUs: https://www.mellanox.com/products/GPUDirect-RDMA/
NVIDIA Magnum IO GPUDirect Storage: https://developer.nvidia.com/gpudirect-storage/
IBM Spectrum Fusion Enterprise container-native storage solution: https://www.ibm.com/products/spectrum-fusion/
Kubernetes for automating deployment, scaling, and management of containerized applications: https://kubernetes.io/
eSync Alliance Program: https://esyncalliance.org/
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© 2022 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kraemer, F. (2022). Development and Testing Autonomous Vehicles at Scale. In: Bargende, M., Reuss, HC., Wagner, A. (eds) 22. Internationales Stuttgarter Symposium. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-37009-1_30
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DOI: https://doi.org/10.1007/978-3-658-37009-1_30
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