Discusses the advantages of computing with heterogeneous parallel machines, and examines the research challenges for automating the use of such systems. One type of heterogeneous computing system is a mixed-mode machi...
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Discusses the advantages of computing with heterogeneous parallel machines, and examines the research challenges for automating the use of such systems. One type of heterogeneous computing system is a mixed-mode machine, where a single machine can operate in different modes of parallelism. Another is a mixed-machine system, where a suite of different kinds of parallel machines are interconnected by high-speed links. To exploit such systems, a task must be decomposed into subtasks, where each subtask is computationally homogeneous. The subtasks are then assigned to and executed with the machines (or modes) that will result in a minimal overall execution time. Typically, users must specify this decomposition and assignment. One long-term pursuit in heterogeneous computing is to do this automatically. An overview of a conceptual model of what this involves is given. As an example of the research in this area, a genetic-algorithm-based approach to the subtask assignment and scheduling problem is explored. Open problems in heterogeneous computing are described.
PASM is a concept for a parallelprocessing system that allows experimentation with different architectural design alternatives. PASM is dynamically reconfigurable along three dimensions: partitionability into indepen...
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PASM is a concept for a parallelprocessing system that allows experimentation with different architectural design alternatives. PASM is dynamically reconfigurable along three dimensions: partitionability into independent or communicating submachines, variable interprocessor connections, and mixed-mode SIMD/MIMD parallelism. With mixed-mode parallelism, a program can switch between SIMD (synchronous) and MIMD (asynchronous) parallelism at instruction-level granularity, allowing the use of both modes in a single machine. The PASM concept is presented, showing the ways in which reconfiguration can be accomplished. Trade-offs among SIMD/MIMD, and mixed-mode parallelism are explored. The small-scale PASM prototype with 16 processing elements is described. The ELP mixed-mode programming language used on the prototype is discussed. An example of a prototype-based study that demonstrates the potential of mixed-mode parallelism is given.
To assure the parallel implementation of an algorithm performs to its maximum potential, a knowledge of the specific parallel machine being used is required. Mapping gray-scale morphological operators and a filter in ...
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To assure the parallel implementation of an algorithm performs to its maximum potential, a knowledge of the specific parallel machine being used is required. Mapping gray-scale morphological operators and a filter in a SIMD, a MIMD, and a mixed-mode environment is analyzed. The matching of several algorithmic techniques and machine features are examined analytically and experimentally. Issues considered include concurrent execution of subtasks, data layout, choice of data transfer protocols, and the mode of parallelism used. Experiments are performed using the MIMD Intel Paragon, SIMD MasPar MP-I, and the mixed-mode PASM prototype. The analytical results and experimental procedures can be applied to other systems as well.
The problem of minimizing the execution time of programs within a heterogeneous environment is considered. Different computational characteristics within a parallel algorithm may make switching execution from one mach...
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The problem of minimizing the execution time of programs within a heterogeneous environment is considered. Different computational characteristics within a parallel algorithm may make switching execution from one mach...
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The problem of minimizing the execution time of programs within a heterogeneous environment is considered. Different computational characteristics within a parallel algorithm may make switching execution from one machine to another beneficial; however, the cost of switching between machines during the execution of a program must be considered. This cost is not constant, but depends on data transfers needed as a result of the move. Therefore, determining a minimum-cost assignment of machines to program segments is not straightforward. A previously presented block-based mode selection (BBMS) approach is used as a basis to develop a heuristic method for assigning machines to program segments of data-parallel algorithms. Simulation results of parallel program behavior using the heuristic indicate that good assignments are possible without resorting to exhaustive search techniques.< >
A framework for estimating the relative execution time of a data-parallel algorithm in an environment capable of the SIMD and SPMD (Single Program - Multiple Data) modes of computation is presented. Given a data-paral...
Mixed-mode parallel processors can quickly switch from executing instructions in Single Instruction, Multiple Data stream (SIMD) mode to executing them in Multiple Instruction, Multiple Data stream (MIMD) mode. To ful...
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Experimentation aimed at determining the potential benefit of mixed-mode SIMD/MIMD parallel architectures is reported. The experimentation is based on timing measurements made on the PASM system prototype at Purdue ut...
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(纸本)9780897914123
Experimentation aimed at determining the potential benefit of mixed-mode SIMD/MIMD parallel architectures is reported. The experimentation is based on timing measurements made on the PASM system prototype at Purdue utilizing carefully coded synthetic variations of a well-known algorithm. The synthetic algorithms used to measure and evaluate this system were based on bitonic sorting of sequences stored in the processing elements. This computation was mapped to both the SIMD and MIMD modes of parallelism, as well as two hybrids of the SIMD and MIMD modes. The computations were coded in these four ways and experiments were performed that explore the trade-offs among them. The results of these experiments are presented and are discussed with special consideration of the effects of the system's architecture. The goal is to (as much as possible) obtain implementation independent analyses of the attributes of mixed-mode parallelprocessing with respect to the computational characteristics of the application being examined. The results are used to gain insight into the impact of computation mode on synchronization and data-conditional aspects of system performance.
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