BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T213000Z
DTEND:20121114T220000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Heterogeneous computing nodes are becoming commonplace today, and recent trends strongly indicate that clusters, supercomputers, and cloud environments will increasingly host more heterogeneous resources, with some being massively parallel (e.g., GPU, MIC). With such heterogeneous environments becoming common, it is important to revisit scheduling problems for clusters and cloud environments. =0A=0AIn this paper, we formulate and address the problem of value-driven scheduling of independent jobs on heterogeneous clusters, which captures both the urgency  and relative  priority of jobs. Our overall scheduling goal is to maximize the aggregate value or yield of all jobs. Exploiting the portability available from the underlying programming model, we propose four novel scheduling schemes that can automatically and dynamically map jobs onto heterogeneous resources. Additionally, to improve the utilization of massively parallel resources, we also propose heuristics to automatically decide when and which jobs can share a single resource.
SUMMARY:ValuePack - Value-Based Scheduling Framework for CPU-GPU Clusters
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T213000Z
DTEND:20121114T220000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Heterogeneous computing nodes are becoming commonplace today, and recent trends strongly indicate that clusters, supercomputers, and cloud environments will increasingly host more heterogeneous resources, with some being massively parallel (e.g., GPU, MIC). With such heterogeneous environments becoming common, it is important to revisit scheduling problems for clusters and cloud environments. =0A=0AIn this paper, we formulate and address the problem of value-driven scheduling of independent jobs on heterogeneous clusters, which captures both the urgency  and relative  priority of jobs. Our overall scheduling goal is to maximize the aggregate value or yield of all jobs. Exploiting the portability available from the underlying programming model, we propose four novel scheduling schemes that can automatically and dynamically map jobs onto heterogeneous resources. Additionally, to improve the utilization of massively parallel resources, we also propose heuristics to automatically decide when and which jobs can share a single resource.
SUMMARY:ValuePack - Value-Based Scheduling Framework for CPU-GPU Clusters
PRIORITY:3
END:VEVENT
END:VCALENDAR