スーパーコピュータ用自動並列化コンパイラに関する研究

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Multitasking and microtasking have long been used for the parallel processing of Fortran programs on shared-momory type multiprocessor systems. Howerver, multitasking has shortcomings such as the difficulty for the specification of parallelism by the user and the scheduling overhead due to OS calls. Micortasking has been widely used for loop parallelization, but there still remain many loops such as complicated data dependencies among iterations and conditional branches outsides loops, which cannot be paralyzed automatically. To resolve those problems, we have proposed a method for the processing of macro dataflows. In this scheme, the compiler automatically decompose a given program into coarse-grain tasks and detects parallelisms existing among the coarse grain task by analyzing the earliest executable conditions.By using the scheduling routines generated by the compiler, the scheduling overhead can be kept minimum. In macro datafow processing, due considerations are given to minimize data transfer overheads, and more efficient parallel processing can be achieved by localizing onto local memories. The efficiency of the proposed parallel processing of coarse grain tasks through macrodata flow processing was confirmed on the prototype multiprocessor system OSCAR.It was also tested on commercial multiprocessor systems such as a Fujitsu FPP0500, an Alliant FX-4, an KSR1 and an NEC Cenju-3. It has been found that macro dataflow processing can provide more parallelism than conventional multitasking and microtasking. It was also confirmed that lower overheads can be achieved compared with conventional methods, hence faster speed for program processing.