In terms of technical framework, DGPT leverages aggregated user idle arithmetic to enhance data processing and analysis capabilities.DGPT adopts a distributed computing framework that leverages advanced containerisation technologies and cloud-native architectures for task decomposition and allocation. It builds a highly scalable and flexible arithmetic network based on open source distributed computing platforms such as Kubernetes and Apache Spark. It also uses containerisation technology on top of Kubernetes to achieve task isolation and deployment. Each task is packaged into a separate container containing the required algorithms, data and dependencies. This ensures the independence and flexibility of tasks to run in different environments, making full use of distributed computing resources. By utilising the computational resources of idle arithmetic, DGPT is able to handle complex data operations and algorithmic computations more efficiently.
In terms of data processing, DGPT leverages big data processing tools, such as Apache Hadoop and Apache Spark, to efficiently process and analyse data using aggregated user idle arithmetic to enhance data processing and analysis capabilities.
In terms of data synchronisation and communication, DGPT adopts a data synchronisation and communication mechanism. When a task needs to process a large amount of data, the system distributes the data to each node in the cluster and performs synchronisation operations to ensure data consistency. At the same time, the nodes also communicate with each other to share the task status and results in order to achieve collaborative work in distributed computing.
In terms of elastic expansion and load balancing, DGPT has the ability of elastic expansion and load balancing as user participation and tasks increase. The system can dynamically adjust the size of the equipment cluster according to the actual demand and automatically balance the distribution of tasks according to the load of the nodes, in order to achieve the optimal use of resources and efficient execution of tasks.
In terms of human-computer interaction, DGPT provides APIs and SDKs and integrates them into the APP side so that users can easily access the platform and call the corresponding functions and services. Through the API and SDK, users can submit tasks, query task status, get results, etc., realising efficient communication and interaction with the platform.
In terms of storage and computation, DGPT assembles users' idle devices to form a cluster of distributed devices, connects idle user arithmetic to the cluster as client software, and uses its storage space as one of the storage nodes to participate in the processing of storage tasks, while DGPT's cloud server acts as a server. And the use of distributed storage systems, such as Hadoop Distributed File System (HDFS) and Ceph, allows data to be partitioned into multiple blocks and distributed in clusters consisting of idle arithmetic devices for parallel processing. By utilising the computational resources of idle arithmetic, DGPT is able to process complex data operations and algorithmic calculations more efficiently, providing data redundancy and high throughput storage to improve data reliability and access speed. In this way, DGPT makes use of the storage resources of idle arithmetic devices to improve the overall capacity and performance of the storage system, and at the same time, this cluster architecture achieves the conversion of idle devices into valuable arithmetic resources, providing users with a way to convert idle arithmetic into valuable resources, enabling them to participate in the DGPT ecosystem, and providing more training and reasoning for AI tasks more computing power to support the benefits from shared storage services.