Computer systems that have been successfully deployed for dense regular workloads fall short of achieving scalability and efficiency when applied to irregular and dynamic graph applications. Conventional computing systems rely heavily on static, regular, numeric intensive computations while High Performance Computing systems executing parallel graph applications exhibit little locality, spatial or temporal, and are fine-grained and memory intensive. With the strong interest in AI which depend on these very different use cases combined with the end of Moore's Law at nanoscale, dramatic alternatives in architecture and underlying execution models are required. This paper identifies an innovative non-von Neumann architecture, Continuum Computer Architecture (CCA), that redefines the nature of computing structures to yield powerful innovations in computational methods to deliver a new generation of highly parallel hardware architecture. CCA reflects a genus of highly parallel architectures that while varying in specific quantities (e.g., memory blocks), share a multiple of attributes not found in typical von Neumann machines. Among these are memory-centric components, message-driven asynchronous flow control, and lightweight out-of-order execution across a global name space. Together these innovative non-von Neumann architectural properties guided by a new original execution model will deliver the new future path for extending beyond the von Neumann model. This paper documents a series of interrelated experiments that together establish future directions for next generation non-von Neumann architectures, especially for graph processing.

翻译：成功部署于密集规则工作负载的计算机系统，在应用于不规则动态图应用时难以实现可扩展性与效率。传统计算系统严重依赖静态、规则、计算密集的数值运算，而执行并行图应用的高性能计算系统则表现出极低的空间或时间局部性，具有细粒度与内存密集型特征。随着对依赖此类迥异用例的人工智能领域的强烈兴趣，加之纳米尺度下摩尔定律的终结，亟需在体系结构与底层执行模型方面寻求根本性变革。本文提出一种创新的非冯·诺依曼体系结构——连续体计算机体系结构（CCA），其通过重新定义计算结构的本质，在计算方法上实现重大创新，从而催生新一代高度并行的硬件架构。CCA代表了一类高度并行的体系结构，虽然在具体参数（如内存块数量）上存在差异，但共享典型冯·诺依曼机器所不具备的多种特性，包括以内存为中心的组件、消息驱动的异步流控制以及全局命名空间下的轻量级乱序执行。这些由新型原创执行模型引导的创新性非冯·诺依曼架构特性，共同构成了超越冯·诺依曼模型的未来演进路径。本文记录了一系列相互关联的实验，共同为下一代非冯·诺依曼体系结构（特别是图处理领域）确立了未来发展方向。