论文标题
团结控制和数据并行性：朝着可扩展的内存驱动的动态图处理处理
Uniting Control and Data Parallelism: Towards Scalable Memory-Driven Dynamic Graph Processing
论文作者
论文摘要
控制并行性和数据并行性大多被认为并优化为单独的函数。因此，不规则，细粒和动态的工作负载（例如动态图处理）变得很难扩展。本文提出了一种合成并行计算的先前技术技术的计算机架构研究方法。我们建立了研究进行的背景和动机，并提供了高度平行的非元音诺伊曼，以内存为中心和记忆驱动的提议的计算系统的详细描述。我们还提出了一个称为“扩散计算”的消息驱动（或均匀驱动的）编程模型，并使用SSSP和三角计数问题作为示例提供了对其属性的见解。
Control parallelism and data parallelism is mostly reasoned and optimized as separate functions. Because of this, workloads that are irregular, fine-grain and dynamic such as dynamic graph processing become very hard to scale. An experimental research approach to computer architecture that synthesizes prior techniques of parallel computing along with new innovations is proposed in this paper. We establish the background and motivation of the research undertaking and provide a detailed description of the proposed omputing system that is highly parallel non-von Neumann, memory-centric and memory-driven. We also present a message-driven (or even-driven) programming model called "diffusive computation" and provide insights into its properties using SSSP and Triangle Counting problems as examples.
