SEARCH
ISTC-CC NEWSLETTER
RESEARCH HIGHLIGHTS
Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
ISTC-CC Abstract
Cura: A Cost-optimized Model for MapReduce in a Cloud
27th IEEE International Parallel & Distributed Processing Symposium (IPDPS'13),
May 2013.
Balaji Palanisamy, Aameek Singh*, Ling Liu, Bryan Langston*
College of Computing, Georgia Tech
*IBM Research - Almaden
On-chip heterogeneity has become key to balancing performance and power constraints, resulting in disparate (functionally overlapping but not equivalent) cores on a single die. Requiring developers to deal with such heterogeneity can impede adoption through increased programming effort and result in cross-platform incompatibility. To evolve systems software toward dynamically accommodating heterogeneity, this paper develops and evaluates the kinship approach and metric for mapping workloads to heterogeneous cores. For this metric, we provide a model and online methods for maximizing utility in terms of performance, power, or latency, to automatically choose the task-to-resource mappings best able to use the different features of heterogeneous cores. Such online scheduling at bounded cost is realized with a hypervisor-level implementation that is evaluated on a variety of actual, experimental heterogeneous platforms. These evaluations demonstrate the both general applicability and the utility of kinship-based scheduling, accommodating dynamic workloads with available resources and scaling both with the number of processes and with different types/ configurations of compute resources. Performance improvements with kinship-based scheduling are obtained for runs across multiple generations of heterogeneous platforms.
FULL PAPER: pdf
