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Enhancing manageability of execution and data for GPGPU computing
(Georgia Institute of Technology, 2016-12-13)
GPGPUs are useful for many types of compute-intensive workloads from scientific simulations to cloud-focused applications like machine learning and graph analytics. However, unlike CPUs they do not allow for software-controlled ...
System design principles for heterogeneous resource management and scheduling in accelerator-based systems
(Georgia Institute of Technology, 2016-06-08)
Accelerator-based systems are making rapid inroads into becoming platforms of choice for both high end cloud services and processing irregular applications like real-world graph analytics due to their high scalability and ...