LUCID, the dataflow programming language
LUCID, the dataflow programming language
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
On a Class of Scheduling Algorithms for Multiprocessors Computing Systems
Proceedings of the Sagamore Computer Conference on Parallel Processing
A heterogeneous multiprocessor design and the distributed scheduling of its task group workload
ISCA '82 Proceedings of the 9th annual symposium on Computer Architecture
A hardware support mechanism for scheduling resources in a parallel machine environment
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
A large scale, homogeneous, fully distributed parallel machine, I
ISCA '77 Proceedings of the 4th annual symposium on Computer architecture
Fast algorithms for bin packing
Journal of Computer and System Sciences
Improving resource utilization for MPEG decoding in embedded end-devices
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
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Distributed processor systems are currently used for advanced, high-speed computation in application areas such as image processing, artificial intelligence, signal processing, and general data processing. The use of distributed and parallel processor computer systems today requires systems designers to partition an application into at least as many functions as there are processors. Spare processors must be allocated and function migration paths must be designed to allow fault tolerant reconfiguration. The parallel process/ parallel architecture control simulation (PPCS) models parallel task allocation on a distributed processor architecture. Parallel task allocation is a first step in designing a dynamic parallel processor operating system that automatically assigns and reassigns application tasks to processors. Advantages of this approach are: dynamic reconfigurability removing the need for spare processing power reserved for failures; the reduced need for fallback and recovery software for fault detection; more optimized partitioning of functions; and better load balancing over available processors. PPCS models various distributed processing configurations, task dependencies, and the scheduling of the tasks onto the processor architecture. The PPCS system implements fifteen different heuristic scheduling algorithms to map a set of tasks onto the processing nodes of a distributed computer. The simulation shows the feasibility of using fast algorithms to heuristically schedule a system of multiple processors allowing dynamic task allocation.