{"title":"TOP-C:面向任务的并行C接口","authors":"G. Cooperman","doi":"10.1109/HPDC.1996.546183","DOIUrl":null,"url":null,"abstract":"The goal of this work is to simplify parallel application development, and thus ease the learning barriers faced by non-experts. It is especially useful where there is little data-parallelism to be recognized by a compiler. The applications programmer need learn the intricacies of only one primary subroutine in order to get the full benefits of the parallel interface. The applications programmer defines a high level concept, the task, that depends only on his application, and not on any particular parallel library. The task is defined by its three phases: (a) the task input, (b) sequential code to execute the task, and (c) any modifications of global variables that occur as a result of the task. In particular, side effects (which change global variable values) must not occur in phase (b). Forcing the user to re-organize his computation in these terms allows us to present the applications programmer with a single global environment visible to all processors (whether on a SMP or a NOW architecture), in the context of a masterslave architecture. Both a shared memory implementation (running on an SGI or SUN Solaris architecture) and a NOW memory implementation (running on top of MPI) are described. The implementations were tested by a naive program for integer factorization, and by a more sophisticated Todd-Coxeter coset enumeration. Integer factorization was chosen so as to exercise the major features of TOP-C in an unambiguous context.","PeriodicalId":267002,"journal":{"name":"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1996-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"TOP-C: a task-oriented parallel C interface\",\"authors\":\"G. Cooperman\",\"doi\":\"10.1109/HPDC.1996.546183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this work is to simplify parallel application development, and thus ease the learning barriers faced by non-experts. It is especially useful where there is little data-parallelism to be recognized by a compiler. The applications programmer need learn the intricacies of only one primary subroutine in order to get the full benefits of the parallel interface. The applications programmer defines a high level concept, the task, that depends only on his application, and not on any particular parallel library. The task is defined by its three phases: (a) the task input, (b) sequential code to execute the task, and (c) any modifications of global variables that occur as a result of the task. In particular, side effects (which change global variable values) must not occur in phase (b). Forcing the user to re-organize his computation in these terms allows us to present the applications programmer with a single global environment visible to all processors (whether on a SMP or a NOW architecture), in the context of a masterslave architecture. Both a shared memory implementation (running on an SGI or SUN Solaris architecture) and a NOW memory implementation (running on top of MPI) are described. The implementations were tested by a naive program for integer factorization, and by a more sophisticated Todd-Coxeter coset enumeration. Integer factorization was chosen so as to exercise the major features of TOP-C in an unambiguous context.\",\"PeriodicalId\":267002,\"journal\":{\"name\":\"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPDC.1996.546183\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPDC.1996.546183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The goal of this work is to simplify parallel application development, and thus ease the learning barriers faced by non-experts. It is especially useful where there is little data-parallelism to be recognized by a compiler. The applications programmer need learn the intricacies of only one primary subroutine in order to get the full benefits of the parallel interface. The applications programmer defines a high level concept, the task, that depends only on his application, and not on any particular parallel library. The task is defined by its three phases: (a) the task input, (b) sequential code to execute the task, and (c) any modifications of global variables that occur as a result of the task. In particular, side effects (which change global variable values) must not occur in phase (b). Forcing the user to re-organize his computation in these terms allows us to present the applications programmer with a single global environment visible to all processors (whether on a SMP or a NOW architecture), in the context of a masterslave architecture. Both a shared memory implementation (running on an SGI or SUN Solaris architecture) and a NOW memory implementation (running on top of MPI) are described. The implementations were tested by a naive program for integer factorization, and by a more sophisticated Todd-Coxeter coset enumeration. Integer factorization was chosen so as to exercise the major features of TOP-C in an unambiguous context.