sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Scheduling Multi-Tenant Cloud Workloads on Accelerator-Based Systems

SESSION: Cloud Computing I


TIME: 1:30PM - 2:00PM

SESSION CHAIR: Rizos Sakellariou

AUTHOR(S):Dipanjan Sengupta, Anshuman Goswami, Karsten Schwan, Krishna Pallavi



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 system throughput and fairness improvements of up to 8.70x and 13%, respectively, compared to the CUDA runtime

Chair/Author Details:

Rizos Sakellariou (Chair) - University of Manchester

Dipanjan Sengupta - Georgia Institute of Technology

Anshuman Goswami - Georgia Institute of Technology

Karsten Schwan - Georgia Institute of Technology

Krishna Pallavi - Georgia Institute of Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society