Optimal Systolic Design for the Transitive Closure and the Shortest Path Problems
IEEE Transactions on Computers
Synthesizing Linear Array Algorithms from Nested FOR Loop Algorithms
IEEE Transactions on Computers
Systematic design approaches for algorithmically specified systolic arrays
Computer architecture
Mapping regular recursive algorithms to fine-grained processor arrays
Mapping regular recursive algorithms to fine-grained processor arrays
On the Relationship Between Two Systolic Array Design Methodologies
IEEE Transactions on Computers
Mapping Nested Loop Algorithms into Multidimensional Systolic Arrays
IEEE Transactions on Parallel and Distributed Systems
On Time Mapping of Uniform Dependence Algorithms into Lower Dimensional Processor Arrays
IEEE Transactions on Parallel and Distributed Systems
Optimization and interconnection complexity for: parallel processors, single-stage networks, and decision trees
Designing a Scalable Processor Array for Recurrent Computations
IEEE Transactions on Parallel and Distributed Systems
Mapping rectangular mesh algorithms onto asymptotically space-optimal arrays
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
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Processor arrays are frequently used to deliver high performance in many applications with computationally intensive operations. This paper presents the General Parameter Method (GPM), a systematic parameter-based approach for synthesizing such algorithm-specific architectures. GPM can synthesize processor arrays of any lower dimension from a uniform-recurrence description of the algorithm. The design objective is a general nonlinear and nonmonotonic user-specified function, and depends on attributes such as computation time of the recurrence on the processor array, completion time, load time, and drain time. In addition, bounds on some or all of these attributes can be specified. GPM performs an efficient search of polynomial complexity to find the optimal design satisfying the user-specified design constraints. As an illustration, we show how GPM can be used to find optimal linear processor arrays for computing transitive closures. We consider design objectives that minimize computation time, or processor count, or completion time (including load and drain times), and user-specified constraints on number of processing elements and/or computation/completion times. We show that GPM can be used to obtain optimal designs that trade between number of processing elements and completion time, thereby allowing the designer to choose a design that best meets the specified design objectives. We also show the equivalence between the model assumed in GPM and that in the popular dependence-based methods [1], [2]. Consequently, GPM can be used to find optimal designs for both models.