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Job submissions of parallel applications to production supercomputer systems will have to be carefully tuned in terms of the job submission parameters to obtain minimum response times. In this work, we have developed an end-to-end resource management framework that uses predictions of queue waiting and execution times to minimize response times of user jobs submitted to supercomputer systems. Our method for predicting queue waiting times adaptively chooses a prediction method based on the cluster structure of similar jobs. Our strategy for execution time predictions dynamically learns the impact of load on execution times and uses this to predict a set of execution time ranges for the target job. We have developed two resource management techniques that employ these predictions, one that selects the number of processors for execution and the other that also dynamically changes the job submission time. Using workload simulations of large supercomputer traces, we show large-scale improvements in predictions and reductions in response times over existing techniques and baseline strategies.
The Internet of Things (IoT) promises to help solve a wide range of issues that relate to our wellbeing within application domains that include smart cities, healthcare monitoring, and environmental monitoring. IoT is bringing new wireless sensor use
In this paper we present a system for monitoring and controlling dynamic network circuits inside the USLHCNet network. This distributed service system provides in near real-time complete topological information for all the circuits, resource allocati
We explore training attention-based encoder-decoder ASR in low-resource settings. These models perform poorly when trained on small amounts of transcribed speech, in part because they depend on having sufficient target-side text to train the attentio
Most large web-scale applications are now built by composing collections (from a few up to 100s or 1000s) of microservices. Operators need to decide how many resources are allocated to each microservice, and these allocations can have a large impact
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thus, improving the execution efficiency of neural recommendation directl