With the rapid growth of artificial intelligence (AI), the Internet of Things (IoT) and big data, emerging applications that cross stacks with different techniques bring new challenges to parallel computing systems. These cross-stack functionalities require one system to possess multiple characteristics, such as the ability to process data under high throughput and low latency, the ability to carry out iterative and incremental computation, transparent fault tolerance, and the ability to perform heterogeneous tasks that evolve dynamically. However, high-performance computing (HPC) and big data computing, as two categories of parallel computing architecture, are incapable of meeting all these requirements. Therefore, by performing a comparative analysis of HPC and big data computing from the perspective of the parallel programming model layer, middleware layer, and infrastructure layer, we explore the design principles of the two architectures and discuss a converged architecture to address the abovementioned challenges.

随着人工智能（AI），物联网（IoT）和大数据的快速增长，新兴的应用程序以不同的技术交叉堆叠，给并行计算系统带来了新的挑战。这些跨堆栈功能要求一个系统具有多种特性，例如在高吞吐量和低延迟下处理数据的能力，执行迭代和增量计算的能力，透明的容错能力以及执行不断发展的异构任务的能力。动态地。但是，作为并行计算架构的两类，高性能计算（HPC）和大数据计算无法满足所有这些要求。因此，通过从并行编程模型层的角度对HPC和大数据计算进行比较分析，