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Deep Learning system architects strive to design a balanced system where the computational accelerator -- FPGA, GPU, etc, is not starved for data.
Caching in the Sprite network file system , volume 21
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Flexibility, manageability, and performance in a grid storage appliance
Bent, J., Venkataramani, V., LeRoy, N., Roy, A., Stanley, J., Arpaci-Dusseau, A. C., Arpaci-Dusseau, R. H., and Livny, M · 2002
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Imagenet: A large-scale hierarchical image database
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Glusterfs one storage server to rule them all
Boyer, E. B., Broomfield, M. C., and Perrotti, T. A · 2012
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Mercury: Host-side flash caching for the data center
Byan, S., Lentini, J., Madan, A., Pabon, L., Condict, M., Kimmel, J., Kleiman, S., Small, C., and Storer, M · 2012
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Flashtier: A lightweight, consistent and durable storage cache
Saxena, M., Swift, M. M., and Zhang, Y · 2012
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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IBM deep learning service
Bhattacharjee, B., Boag, S., Doshi, C., Dube, P., Herta, B., Ishakian, V., Jayaram, K., Khalaf, R., Krishna, A., Li, Y. B., et al · 2017
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Kamali, S., Malloci, M., Pont-Tuset, J., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., and Murphy, K · 2017
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Distributed training large-scale deep architectures
Zou, S.-X., Chen, C.-Y., Wu, J.-L., Chou, C.-N., Tsao, C.-C., Tung, K.-C., Lin, T.-W., Sung, C.-L., and Chang, E. Y · 2017
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Distributed Deep Learning with Containers on Heterogeneous GPU clusters
Meng, D · 2018
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NVIDIA DGX SYSTEMS
Nvidia DGX · 2018
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NVIDIA HGX-2 SYSTEMS
Nvidia HGX · 2018
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TESLA P100 PERFORMANCE GUIDE
NVidia P100 · 2018
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TESLA V100 PERFORMANCE GUIDE
NVidia V100 · 2018
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Nvidia NVLink website
NVLink Fabric · 2018
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Paperspace web page
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Amazon S3 API Documentation
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Amazon Web Services
Amazon Inc · 2018
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Kubernetes Volume Controller (KVC): Data Management Tailored for Machine Learning Workloads in Kubernetes
Balaji, S. and Ajay, D · 2018
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Building a Successful Deep Learning Platform: Experiences in Building GPU Enabled HPC Clusters
Belgodere, B. M · 2018
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Flexible and Fast Storage for Deep Learning with Alluxio
Fu, Y · 2018
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Accelerating I/O bound deep learning on shared storage
Haußmann, E · 2018
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Kubernetes web page
Kubernetes · 2018
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Linux Administration Guide
Linux Buffer Cache documentation · 2018
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IBM Cloud
IBM Corp
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S3 API Documentation · 2018
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IBM Spectrum Scale Active File Management
Spectrum Scale AFM · 2018
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Scalable AI Infrastructure: Designing For Real-World Deep Learning Use Cases
Sundar, R. and Santosh, R · 2018
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