Oncilla: A GAS Runtime for Efficient Resource Allocation and Data Movement in Accelerated Cluster

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Provided by: Georgia Institute of Technology
Topic: Storage
Format: PDF
Accelerated and in-core implementations of big data applications typically require large amounts of host and accelerator memory as well as efficient mechanisms for transferring data to and from accelerators in heterogeneous clusters. Scheduling for heterogeneous CPU and GPU clusters has been investigated in depth in the High-Performance Computing (HPC) and cloud computing arenas, but there has been less emphasis on the management of cluster resource that is required to schedule applications across multiple nodes and devices.
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