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Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings.
Linda in context
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It’s Time for Low Latency
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Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M., Shenker, S., and Stoica, I · 2011
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Flat datacenter storage
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Uk research software survey 2014
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Scaling distributed machine learning with the parameter server
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Interruptible tasks: Treating memory pressure as interrupts for highly scalable data-parallel programs
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Image-based recommendations on styles and substitutes
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Scalability! but at what COST?
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Software Use in Astronomy: an Informal Survey
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Large-scale computation not at the cost of expressiveness
Han, S., and Ratnasamy, S · 2013
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The case for tiny tasks in compute clusters
Ousterhout, K., Panda, A., Rosen, J., Venkataraman, S., Xin, R., Ratnasamy, S., Shenker, S., and Stoica, I · 2013
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Sparrow: distributed, low latency scheduling
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Omega: flexible, scalable schedulers for large compute clusters
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Apache Hadoop YARN: Yet another resource negotiator
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Firebox: A hardware building block for 2020 warehouse-scale computers
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Momcheva, I., and Tollerud, E · 2015
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ImageNet Large Scale Visual Recognition Challenge
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Tensorflow: A system for large-scale machine learning
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Network requirements for resource disaggregation
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Serverless computation with OpenLambda
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Ernest: Efficient performance prediction for large-scale advanced analytics
Venkataraman, S., Yang, Z., Franklin, M., Recht, B., and Stoica, I · 2016
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Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads
Fouladi, S., Wahby, R. S., Shacklett, B., Balasubramaniam, K. V., Zeng, W., Bhalerao, R., Sivaraman, A., Porter, G., and Winstein, K · 2017
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