For computations on large-scale graphs, one often resorts to parallel algorithms. However, parallel algorithms are difficult to write, debug and analyze. Worse still, it is difficult to make algorithms parallelly scalable, such that the more machines are used, the faster the algorithms run. Indeed, it is not yet known whether any PTIME computational problems admit parallelly scalable algorithms on shared-nothing systems. Is it possible to parallelize sequential graph algorithms and guarantee convergence at the correct results as long as the sequential algorithms are correct? Moreover, does a PTIME parallelly scalable problem exist on shared-nothing systems? This position paper answers both questions in the affirmative.

对于大规模图形的计算，通常采用并行算法。但是，并行算法很难编写，调试和分析。更糟糕的是，很难使算法具有并行可伸缩性，因此使用的计算机越多，算法运行的速度就越快。确实，尚不知道是否有任何PTIME计算问题在无共享系统上允许并行可伸缩的算法。只要顺序算法正确，是否可以并行化顺序图算法并保证收敛于正确的结果？此外，无共享系统上是否存在PTIME并行可伸缩问题？本立场文件肯定回答了这两个问题。