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Effective Dynamic Scheduling on Heterogeneous Multi/Manycore Desktop Platforms

Alecio P.D. Binotto, Bernardo M.V. Pedras, Marcelo Goetz, Arjan Kuijper, Carlos E. Pereira, Andre Stork, Dieter W. Fellner
Inst. of Inf., UFRGS – Fed. Univ. of Rio Grande do Sul, Porto Alegre, Brazil
22nd International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW), 2010

@inproceedings{binotto2010effective,

   title={Effective dynamic scheduling on heterogeneous multi/manycore desktop platforms},

   author={Binotto, A.P.D. and Pedras, B.M.V. and G{\"o}etz, M. and Kuijper, A. and Pereira, C.E. and Stork, A. and Fellner, D.W.},

   booktitle={2010 22nd International Symposium on Computer Architecture and High Performance Computing Workshops},

   pages={37–42},

   year={2010},

   organization={IEEE}

}

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GPUs (Graphics Processing Units) have become one of the main co-processors that contributed to desktops towards high performance computing. Together with multicore CPUs and other co-processors, a powerful heterogeneous execution platform is built on a desktop for data intensive calculations. In our perspective, we see the modern desktop as a heterogeneous cluster that can deal with several applications’tasks at the same time. To improve application performance and explore such heterogeneity, a distribution of workload over the asymmetric PUs (Processing Units) plays an important role for the system. However, this problem faces challenges since the cost of a task at a PU is non-deterministic and can be influenced by several parameters not known a priori, like the problem size domain. We present a context-aware architecture that maximizes application performance on such platforms. This approach combines a model for a first scheduling based on an offline performance benchmark with a runtime model that keeps track of tasks’ real performance. We carried a demonstration using a CPU-GPU platform for computing iterative SLEs (Systems of Linear Equations) solvers using the number of unknowns as the main parameter for assignment decision. We achieved a gain of 38.3% in comparison to the static assignment of all tasks to the GPU (which is done by current programming models, such as Open CL and CUDA for Nvidia).
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