GraphGuess：具有自适应校正的近似图形处理系统
GraphGuess: Approximate Graph Processing System with Adaptive Correction
摘要：
基于图的数据结构近年来引起了人们极大的关注。开发图形处理系统的大规模和快速增长的趋势主要集中在通过预处理输入图形和修改其布局来提高性能。这些系统通常需要几个小时到几天才能在高端机器上完成单个图形的处理，更不用说大部分时间占主导地位的预处理开销了。然而，对于大多数图形应用程序来说，确切的答案并不总是至关重要的，提供最终结果的粗略估计就足够了。近似计算被引入到权衡结果的准确性计算或节能，不能单独通过传统的技术来实现。在这项工作中，我们设计，实现和评估GraphGuess，从近似图论领域的启发，并将其扩展到一个通用的，实用的图形处理系统。GraphGuess本质上是一种具有自适应校正的近似图形处理技术，可以在任何图形处理系统上实现。我们构建了一个基于GraphGuess的以顶点为中心的处理系统，它允许用户权衡准确性以获得更好的性能。我们的实验研究表明，使用GraphGuess可以显着减少大规模图形的处理时间，同时保持高精度。
英文摘要：
Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.
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