Reading experience of Chapters 9-13 of "Principles and Engineering Practice of Large Language Model" - Difficult ending

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Reading experience of Chapters 9-13 of "Principles and Engineering Practice of Large Language Model" - Difficult ending [Copy link]

 

In this article, I will explore the third part (Chapters 9 to 13) of the book "Infrastructure in the Big Model Era: A Guide to Building a Big Model Computing Center". I will share my reading experience from the perspective of an AI technology enthusiast with several years of practical experience in data analysis.

In the further discussion of Chapters 9 to 13, the article carefully deconstructs the several pillars of building an efficient computing center: network architecture optimization, storage system layout, application development environment creation, cloud operation strategy, and analysis of real implementation cases. This article aims to put forward personal opinions on these key links and strive to explain them in simple terms.

What jumps into the eye in the ninth chapter is the precision engineering of GPU cluster network virtualization. Among them, VPC (virtual private cloud) is an efficient tool for tenant isolation, and the SDN (software defined network) design philosophy behind it is admirable. The essence of SDN is to separate the control layer of the network from the data transmission layer, so as to achieve the flexibility of network configuration and the intensive management. In the VPC scenario, the SDN center at the control layer is responsible for regulating the virtual network configuration of each tenant, covering the dynamic allocation of IP addresses, the setting of network routing rules, and the implementation of security policies. The data layer faithfully implements these policies and efficiently handles the transmission of data packets. This architecture is like a chess game, which not only maintains the boundaries between tenants, but also gives flexibility to resource scheduling.

The introduction of overlay tunnel technology allows different tenants' networks to run in parallel in the physical network, achieving a delicate balance of coexistence and isolation. By building a virtual network layer on top of the basic physical network, each tenant's data packet will be labeled with a unique "identity tag" (such as VXLAN encapsulation) before accessing the physical network to distinguish the virtual network to which it belongs. This "tunnel" mechanism ensures the unimpeded transmission of information at the physical level without having to pay attention to the specific content of the data packet, promoting the harmonious coexistence of cyberspace while retaining the independence of each tenant.

The NFV (Network Function Virtualization) gateway has significantly improved its performance with the help of DPDK and SR-IOV technologies. These two technologies enable the network functions that traditionally rely on hardware to be software-based without failure. DPDK bypasses the operating system network protocol stack and directly processes data packets at the user level, greatly improving the processing rate; while SR-IOV gives virtual machines the ability to directly access physical network cards, shortening latency and increasing data throughput. The experimental comparison in the chapter intuitively shows the significant performance boost of these two technologies. This tells us that how to establish an efficient dialogue mechanism between software and hardware is the core challenge of network virtualization.

Turning to Chapter 10, the focus is on the exquisite design of the GPU cluster storage architecture. Storage systems are the lifeblood of AI applications. Distributed storage technology, especially Ceph, has excellent performance and scalability, making it the preferred solution for many cloud platforms. Problems with Ceph include data rebalancing, I/O capacity competition during data migration, and the need to set a lower expansion waterline to avoid the phenomenon of the entire cluster being read-only due to a single disk being full. Considering the huge scale of object storage, we need to cast our eyes on another design of OpenStack - Swift. Unlike Ceph, Swift adopts "eventual consistency" considering the difficulty of the "CAP impossible triangle", and ensures scalability and performance by sacrificing consistency. Interestingly, I think of the "Mundell impossible triangle" exchange rate theory in international finance. I learned that the meaning of the "CAP impossible triangle" is that in a distributed system, consistency, availability, and partition tolerance cannot be achieved at the same time, and only one of them can be sacrificed to meet the other two. Ceph sacrifices C to meet A and P.

The next eleven chapters focus on the discussion of machine learning application development platforms. Kubernetes has become a leader in this field with its powerful container orchestration capabilities. Kubernetes abstracts applications into independent containers, each of which is self-contained, to achieve high isolation and collaborative operation of the application environment. Its declarative API and controller mechanism simplify the application deployment process. You only need to define the ideal state of the application, and Kubernetes will automatically adapt resources to ensure that the system state is consistent with the expected state, greatly simplifying the operation and maintenance tasks.

Chapter 12 examines the monitoring and operation of GPU clusters from a broader perspective. The combination of Prometheus and Grafana has become a star tool in the field of data monitoring. Prometheus's active data pulling method and flexible PromQL query language provide strong support for the accurate extraction and analysis of monitoring data. Grafana uses a rich visual interface to display monitoring data in an intuitive way, providing solid data support for decision-making.

The last chapter puts the previous theories into practice through an example of an autonomous driving platform project, showing the overall picture of GPU cluster construction from network, storage, computing to platform management. This is not only the integration of technology, but also the integration of concept and practice, highlighting the profound shaping of the form and connotation of GPU cluster by cloud computing, big data and AI technology.

I fully feel that building a GPU cluster is a comprehensive project that requires the deep integration of software and hardware and the clever combination of open source and commercial solutions. Every decision and every technology selection requires careful planning and polishing. As the ancients said: "A journey of a thousand miles begins with a single step", the construction of a GPU cluster needs to be gradual, with continuous iteration and innovation as technology evolves and needs change. In the future, the advancement of GPU technology and the trend of AI democratization will make GPU clusters more popular and easier to use, and we should also uphold the spirit of lifelong learning, keep pace with the times, and jointly open up a new era of AI and GPU cluster technology.

Based on the wisdom of Tao Te Ching, "Tao follows nature", we pursue simplicity and harmony in the construction of complex GPU clusters, embrace the unknown of technology with an open mind, and greet every leap forward in technology with humility.