Testing the Effectiveness and Efficiency of Data Dependence Decision Algorithms

数据依赖是构建所有自动并行性检测方法的理论基石。自动向量化在向量计算机的使用中一直是关键的，对于新的并行计算机和语言的其他高级优化都需要数据依赖信息。我们的项目将衡量数据依赖决策算法的有效性和效率。效率将通过计算可向量化和并发执行的循环的数量来衡量。效率将通过计算构建数据依赖图所用的时间来衡量。必须仔细定义效率，因为快速的数据依赖判定算法可能会向依赖图添加更多的弧，从而减慢依赖于图中的弧数的编译的其他阶段。

Data dependence is the theoretical cornerstone on which all automatic parallelism detection methods are built. Automatic vectorization, which has been critical in the use of vector computers, and other advanced optimizations for new parallel computers and languages all require data dependence information. Our project will measure both the effectiveness and the efficiency of data dependence decision algorithms. Effectiveness will be measured by counting the number of loops that can be vectorized and concurrently executed. Efficiency will be measured by computing the time taken to build the data dependence graph. Efficiency must be carefully defined, since a quick data dependence decision algorithm may add more arcs to the dependence graph, thus slowing down other phases of the compilation which depend on the number of arcs in the graph.