【24h】

Lazy array data-flow dependence analysis

机译:惰性数组数据流依赖性分析

获取原文

摘要

Automatic parallelization of real FORTRAN programs does not live up to users expectations yet, and dependence analysis algorithms which either produce too many false dependences or are too slow to contribute significantly to this. In this paper we introduce dataflow dependence analysis algorithm which exactly computes value-based dependence relations for program fragments in which all subscripts, loop bound and IF conditions are affine. Our algorithm also computes good affine approximations of dependence relations for non-affine program fragments. Actually, we do not know about any other algorithm which can compute better approximations.

And our algorithm is efficient too, because it is lazy. When searching for write statements that supply values used by a given read statement, it starts with statements which are lexicographically close to the read statement in iteration space. Then if some of the read statement instances are not "satisfied" with these close writes, the algorithm broadensits search scope by looking into more distant writes. The search scope keeps broadening until all read instances are satisfied or no write candidates are left.

We timed our algorithm on several benchmark programs and the timing results suggest that our algorithm is fast enough to be used in commercial compilers---it usually takes 5 to 15 percent of f77 -02 compilation time to analyze a program. Most programs in the 100-line range take less than 1 second to analyze on a SUN SparcStation IPX.

机译:

真正的FORTRAN程序的自动并行化还没有达到用户的期望,并且依赖性分析算法会产生过多的虚假依赖性,或者速度太慢而无法对此做出重大贡献。在本文中,我们介绍了数据流依赖性分析算法,该算法可精确计算所有下标,循环边界和IF条件均仿射的程序片段的基于值的依赖性关系。我们的算法还为非仿射程序片段计算了依赖关系的良好仿射近似。实际上,我们不知道有任何其他算法可以计算出更好的近似值。

我们的算法也很有效,因为它很懒。当搜索提供给定read语句使用的值的write语句时,它以在字典上接近迭代空间中read语句的语句开始。然后,如果某些读取语句实例对这些紧密的写入不满意,则该算法将通过查找更远的写入来扩大其搜索范围。搜索范围会不断扩大,直到满足所有读取实例或没有剩余写入候选为止。

我们在几个基准程序上对我们的算法进行了计时,计时结果表明我们的算法足够快,可用于商业编译器-分析一个程序通常花费f77 -02编译时间的5%到15%。在100行范围内的大多数程序在SUN SparcStation IPX上进行分析的时间都少于1秒。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号