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SC14: International Conference for High Performance Computing, Networking, Storage and Analysis - Scheduling Multi-tenant Cloud Workloads on Accelerator-Based Systems by Sengupta, Dipanjan; Goswami, Anshuman; Schwan, Karsten; Pallavi, Krishna is a scholarly article available to read on EtoBox.

What is SC14: International Conference for High Performance Computing, Networking, Storage and Analysis - Scheduling Multi-tenant Cloud Workloads on Accelerator-Based Systems about?

Accelerator-based systems are making rapid inroads into becoming platforms of choice for high end cloud services. There is a need therefore, to move from the current model in which high performance applications explicitly and programmatically select the GPU devices on which to run, to a dynamic model where GPUs are treated as first class schedulable entities. The Strings scheduler realizes this vision by decomposing the GPU scheduling problem into a combination of load balancing and per-device scheduling. (i) Device-level scheduling efficiently uses all of a GPU's hardware resources, including its computational and data movement engines, and (ii) load balancing goes beyond obtaining high throughput, to ensure fairness through prioritizing GPU requests that have attained least service. With its methods, Strings achieves improvements in system throughput and fairness of up to 8.70x and 13%, respectively, compared to the CUDA runtime.

Author

Sengupta, Dipanjan; Goswami, Anshuman; Schwan, Karsten; Pallavi, Krishna

Publisher

IEEE

Published

2014

Language

EN