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


   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},





Source Source   



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).
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: