Current advantages and disadvantages of large-scale computation

As the founding team of the technology, we are always focused on the trends of large-scale com- putation and its real-world applications. Currently, the Large-scale computation is mainly supported by two architectures: Intel general-purpose computing and NVIDIA’s heterogeneous parallel computing architecture [9, 10]. These two architectures have their respective advantages and disadvantages, and we will analyze them in details.

Intel’s General Computation

Intel’s general-purpose computation is based on a multi-core CPU architecture [9], emphasizing the performance of single-core processors while enabling parallel multi-task parallel processing. General-purpose computation is highly flexible and can be applied to a variety of computational re- quirements, especially for tasks that require highly serialized processing. However, the disadvantage of this architecture is that it is less energy-efficient and less able to cope with massively parallel com- puting scenarios, such as graphics rendering, deep learning, and scientific computation, etc.

NVIDIA’s Heterogeneous Parallel Computing Architecture

NVIDIA’s heterogeneous parallel computing archi- tecture uses Graphical Processing Unit(GPU) as computational unit[11], with a large number of par- allel processing cores. This architecture offers sig- nificant advantages in handling massively parallel tasks, such as image processing, machine learning, and big data analysis, etc. Compared to general- purpose computation, heterogeneous parallel com- putation offers energy efficiency and performance. However, it also has the limitation that for highly serialized tasks, its performance is not as good as general-purpose computation.

Advantages of Heterogeneous Parallel Architecture

By comparative analysis, heterogeneous parallel computation architectures have more potential. In more and more scenarios, the demand for massively parallel computation is constantly increasing, espe- cially in the domain of artificial intelligence(AI), big data processing and scientific computation. The heterogeneous parallel computation architec- tures can effectively improve computational perfor mance and energy efficiency to meet the needs of these areas.

However, the challenge of large-scale comput- ing devices and the ability to network services still exists. Firstly, the investment cost of large-scale computing devices is high, especially in the de- ployment of distributed computing devices. In addition, in terms of network service capability, large-scale computational networks is required to handle huge data transmission, coordination, and security. These issues need to be solved in the network construction.

In summary, heterogeneous parallel computation architecture is the development direction of large- scale computation, yet it still needs to overcome the challenges of investment cost and network ser- vice capability.Utility network is a blockchain net- work designed to solve these problems, which uses blockchain technology and computation consensus mechanism to achieve efficient utilization and coor- dination of distributed computing resources. By in- troducing technologies such as verifiable computa- tion, execution virtual machines and cross-domain data scheduling, the Utility network provides pow- erful support for large-scale computation

The decentralized nature of UT helps reduce the investment cost of large-scale computational equipment, enabling more efficient resource shar- ing between providers and demanders of comput- ing resources through incentives and rental markets. In addition, the Utility network uses advanced P2P technology and computational power certification mechanisms to achieve high performance and se- cure computation of high performance, security and reliability. As an innovative blockchain net- work, Utility network has a wide range of applica- tions and market potential. By solving the finan- cial problem of large-scale computing equipment deployment and the problem of network service capability, Utility has a great potential to become the future support for large-scale computational development.