Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Resource Sharing in GPU-Accelerated Windowing Systems
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
TimeGraph: GPU scheduling for real-time multi-tasking environments
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Bayesian real-time perception algorithms on GPU
Journal of Real-Time Image Processing
PTask: operating system abstractions to manage GPUs as compute devices
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
RGEM: A Responsive GPGPU Execution Model for Runtime Engines
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
SAFER: System-level Architecture for Failure Evasion in Real-time Applications
RTSS '12 Proceedings of the 2012 IEEE 33rd Real-Time Systems Symposium
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In this paper, we present two conceptual frameworks for GPU applications to adjust their task execution times based on total workload. These frameworks enable smart GPU resource management when many applications share GPU resources while the workloads of those applications vary. Application developers can explicitly adjust the number of GPU cores depending on their needs. An implicit adjustment will be supported by a run-time framework, which dynamically allocates the number of cores to tasks based on the total workload. The runtime support of the proposed system can be realized using functions which measure the execution times of the tasks on GPU and change the number of GPU cores. We motivate the necessity of this framework in the context of self-driving technologies, and we believe that our frameworks for GPU programming are useful contributions given the increasing emphasis on parallel heterogeneous computing.