Partitioning and Mapping Algorithms into Fixed Size Systolic Arrays
IEEE Transactions on Computers
Advanced compiler optimizations for supercomputers
Communications of the ACM - Special issue on parallelism
Iteration-level parallel execution of do loops with a reduced set of dependence relations
Journal of Parallel and Distributed Computing
Synthesizing Linear Array Algorithms from Nested FOR Loop Algorithms
IEEE Transactions on Computers
Minimum Distance: A Method for Partitioning Recurrences for Multiprocessors
IEEE Transactions on Computers
Utilizing Multidimensional Loop Parallelism on Large Scale Parallel Processor Systems
IEEE Transactions on Computers
The parallel execution of DO loops
Communications of the ACM
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FOR-loops are the main source of parallelism in programs. A nonlinear transformation algorithm for parallelizing the execution of FOR-loop models is proposed. It is shown that by the mapping of nonlinear transformation, iterations of FOR-loops can be executed in a parallel form. The algorithm is useful in exploiting the parallelism of FOR-loops with one or more partitions on the innermost loop. Algorithms to partition and map the nested FOR-loops onto fixed size systolic arrays are discussed. Based on the time and space mapping schemes, all the iterations of FOR-loops can be correctly executed on the array processors in a parallel form.