正在加载视频...

视频加载失败

What if your application could use memory beyond a single node? At OSSEU 2026, Hu Xinwei explains what this means for the operating system, from pooling memory across nodes to keeping faults from spreading. See how openEuler approaches the SuperPoD OS🎥

556,248 次观看 • 9 天前 •via X (Twitter)

19 条评论

openEuler 的头像
openEuler9 天前

📅 Next up: Agentic Infra OS Conference & openEuler Summit 2026. Join the openEuler community in Beijing on November 13–14 to explore what's next for operating systems and AI infrastructure. Details and registration:

Biddaddy 的头像
Biddaddy8 天前

fault isolation across pooled nodes is the part everyone ignores until the first cascade failure hits good to see them tackle it directly

Kelvin Tran 的头像
Kelvin Tran8 天前

Nghe thú vị thật, gộp bộ nhớ qua nhiều nút chắc sẽ giúp hệ thống ổn định hơn nhiều nhỉ 😊

∑: Meww ∑: (❖,❖) 的头像
∑: Meww ∑: (❖,❖)8 天前

Intriguing concept for distributed memory management.

Harry Bootman 的头像
Harry Bootman7 天前

Cool to see openEuler tackling memory pooling and fault management across nodes. Definitely a step into advanced OS capabilities.

GuruVerseX 的头像
GuruVerseX8 天前

Faults shouldn't travel

Ana Carmelita 的头像
Ana Carmelita7 天前

#LulaVaiVirar

Wally Almeida 的头像
Wally Almeida8 天前

#FlavioBolsonaroNaLancha #JanonesELulaAoVivoAgora #LulaAoVivoComSeuExercito

TruthCom 的头像
TruthCom7 天前

A little kindness today can become someone's strength tomorrow.

Emly 的头像
Emly8 天前

Intriguing concept for distributed memory applications.

And 的头像
And8 天前

#FlavioBolsonaroNaLancha

Jhohanna 的头像
Jhohanna7 天前

#LulaVaiVirar

Priscila 的头像
Priscila7 天前

#TSEGolpista

Daisy Tran 的头像
Daisy Tran7 天前

Pooling memory sounds cool. Can’t wait to see how it changes things for apps.

Bông 的头像
Bông8 天前

Interesting concept, memory pooling across nodes is key.

0xBenz.eth 的头像
0xBenz.eth7 天前

This sounds like an exciting topic for the future of operating systems! Can't wait to learn more.

kayo cristian telles 的头像
kayo cristian telles8 天前

#EsquentaDaLiveDe24HS

Leliu 的头像
Leliu8 天前

Intriguing concept for distributed OS memory management.

Chris Riccio - LifeAI ☀️ supporter 的头像
Chris Riccio - LifeAI ☀️ supporter7 天前

This is huge for scaling apps past single node limits so cool

相关视频

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 次观看 • 2 年前