With the increasing trend of data scale expansion and structure diversification, how to use the heterogeneous multi co-processors in modern link to provide a real-time and reliable parallel runtime environment for large-scale data processing has become a research hotspot in the field of high performance and database. Modern servers equipped with multi co-processors (GPU) has become the preferred high-performance platform for analyzing large-scale and irregular graph data. The overall performance of existing research designing graph computing systems and algorithms based on multi-GPU server architecture (such as breadth first traversal and shortest path algorithm) has been significantly better than that of multi-core CPU computing environment. However, the data transmission performance between multi-GPU of existing graph computing system is limited by PCI-E bandwidth and local delay, leading to being unable to achieve a linear growth trend of performance by increasing the number of GPU devices, and even serious delay jitter which can not satisfy the high scalability requirements of large-scale graph parallel computing systems. After a series of benchmark experiments, it is found that the existing system has the following two types of defects:1) the hardware architecture of the data link between modern GPU devices is rapidly updated (such as NVLink-V1 and NVLink-V2), and its link bandwidth and delay have been greatly improved. However, the existing systems are still limited by PCI-E for data communication, and can not make full use of modern GPU link resources (including link topology, connectivity and routing); 2) When dealing with irregular graph data, such systems often adopt single data movement strategy between devices, bringing a lot of unnecessary data synchronization overhead between GPU devices via PCI-E bus, resulting in excessive time-wait overhead of local computing. Therefore, it is urgent to make full use of various communication links between modern multi-GPU to design a highly scalable graph computing system. In order to achieve the high scalability of the multi-GPU graph computing system, a fine-grained communication based on hybrid perception is proposed to enhance the scalability of the multi-GPU graph computing system. It pre-awares the architecture link, uses the modular data link and communication strategy for different graph structured data, and finally selects the optimal data exchange method for large-scale graph data (structural data and application data). Based on above optimization strategies, this paper proposes and designs a graph oriented parallel computing system via multi-GPU named ChattyGraph. By optimizing data buffer and multi-GPU collaborative computing with OpenMP and NCCL, ChattyGraph can adaptively and efficiently support various graph parallel computing applications and algorithms on multi-GPU HPC platform. Several experiments of various real-world graph data on 8-GPU NVIDIA DGX server show that ChattyGraph significantly improves graph computing efficiency and scalability, and outperforms other advanced competitors. The average computing efficiency is increased by 1.2-1.5X and the average acceleration ratio is increased by 2-3X, including WS-VR and Groute.