Scheduling multithreaded computations by work stealing
Journal of the ACM (JACM)
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
IEEE Micro
Introduction to the cell multiprocessor
IBM Journal of Research and Development - POWER5 and packaging
Automatic Dynamic Task Distribution between CPU and GPU for Real-Time Systems
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Harmony: an execution model and runtime for heterogeneous many core systems
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
A new physics engine with automatic process distribution between CPU-GPU
Sandbox '08 Proceedings of the 2008 ACM SIGGRAPH symposium on Video games
Mapping Tasks to Processors in Heterogeneous Multiprocessor Architectures: The MATEHa Algorithm
SCCC '08 Proceedings of the 2008 International Conference of the Chilean Computer Science Society
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 02
An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU
Computers in Entertainment (CIE) - SPECIAL ISSUE: Games
Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures
Concurrency and Computation: Practice & Experience - Euro-Par 2009
An Architecture with Automatic Load Balancing and Distribution for Digital Games
SBGAMES '10 Proceedings of the 2010 Brazilian Symposium on Games and Digital Entertainment
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
A Design Pattern for Parallel Programming of Games
HPCC '12 Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
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Games are simulations of the physical and imaginary worlds. Games nowadays run on commodity platforms that include different categories of powerful computing elements with varying capabilities. To benefit from this variety, suitable mapping of works to computing elements is essential for optimal performance. Arbiter Work Stealing (AWS) is a new scheduler addressing this requirement. The AWS scheduler builds on the classical work stealing algorithm by adding an upper layer that "manages" multiple running instances of the work stealing algorithm. AWS automatically schedules the dynamically generated game application tasks to appropriate processors using a cost model that takes into account current work load, execution times, data locality, and data transfer rates. Experimental results show that incorporating AWS to schedule tasks of a parallel game application yields superior performance through better utilization of the available resources and through better use of data locality in a heterogeneous computing environment.