Processing graphs with barrierless asynchronous parallel model on sharedmemory systems Abstract   Graph analytics systems rely on parallel models to realize parallel processing of largescale graphs, in which the most extensively used models are the bulk synchronous parallel (BSP) and the asynchronous parallel (AP) model. The BSP model is simple and deterministic but has difficulties in expressing some graph processing algorithms, while the AP model is flexible to handle various kinds of algorithms but behaves inefficiently on memoryintensive algorithms due to higher scheduling overhead and internal global barriers. Additionally, although the matrix computation has demonstrated its highperformance characteristic in graph processing, most matrixbased systems only support the BSP model. To improve efficiency of the AP model for graph analytics, this paper proposes a barrierless AP model which uses matrix operations to process graphs and removes the global barrier in each superstep of the AP model to accelerate the execution progress of algorithms. The proposed model is evaluated by comparing it with both AP and BSPbased systems including GraphLab, GRACE, and GraphMat. Experimental results show that our model behaves better than the asynchronous systems, and even outperforms the BSPbased system on some memoryintensive graph algorithms as well as computationintensive graph algorithms.

Optimizing Ordered Graph Algorithms with GraphIt. Many graph problems can be solved using ordered parallel graph algorithms that achieve significant speedup over their unordered counterparts by reducing redundant work. This paper introduces a new prioritybased extension to GraphIt, a domainspecific language for writing graph applications, to simplify writing highperformance parallel ordered graph algorithms. The extension enables vertices to be processed in a dynamic order while hiding lowlevel implementation details from the user. We extend the compiler with new program analyses, transformations, and code generation to produce fast implementations of ordered parallel graph algorithms. We also introduce bucket fusion, a new performance optimization that fuses together different rounds of ordered algorithms to reduce synchronization overhead, resulting in 1.2times3times speedup over the fastest existing ordered algorithm implementations on road networks with large diameters. With the extension, GraphIt achieves up to 3times speedup on six ordered graph algorithms over stateoftheart frameworks and handoptimized implementations (Julienne, Galois, and GAPBS) that support ordered algorithms.