Bojie Li

Thanks to Professor Xu Chenren and Professor Huang Qun for the invitation, I am very honored to have given a guest lecture for the Computer Network course at Peking University on December 12, 2022.

Abstract: Data center networks, wide area networks, and wireless networks provide the communication cornerstone for the intelligent world of Internet of Things.

Data center networks have traditionally been designed for easily parallelizable web services. But today, AI, big data, HPC are all large-scale heterogeneous parallel computing systems, which have high requirements for communication performance. The heavy software stack causes huge overhead, which requires the communication semantics of data center networks to evolve from byte streams to memory semantics including message semantics, synchronous and asynchronous remote memory access, RPC, and to achieve extreme latency and bandwidth with a combination of software and hardware. In the future, we hope to treat the data center as a computer, on the one hand, to achieve peer-to-peer direct access between heterogeneous computing and storage devices, making the data center interconnection as high-performance as the internal bus of the host; on the other hand, to make distributed system programming as convenient as single-machine programming through Serverless.

Large-scale live streaming and short video on-demand, real-time audio and video communication and other applications pose new challenges to the stability of wide area network transmission. Internet giants have built their own global acceleration networks and designed new transport protocols such as QUIC to achieve a high-quality user experience. In addition, due to the low energy cost in the western part of our country, the strategy of “computing in the east and calculating in the west” has become a national strategy. Through Regionless scheduling, we can achieve a “nationwide integrated large data center”.

Seamless collaboration of intelligent terminals such as mobile phones, PCs, wearable devices, smart homes, smart cars, and industrial Internet applications such as 5G to B all require stable low latency and high bandwidth, which requires wireless protocol stack optimization, and even wireless memory semantics to support Gbps-level bandwidth. In addition, through the “distributed super terminal” programming framework of HarmonyOS, more closely distributed collaboration can be enabled to achieve seamless data and service flow.

Download Slides PDF (Updated on 2022-12-15)

Download Slides PPTX (Updated on 2022-12-15)

Full text of the speech:

• Part One: The New Golden Age of Computer Networks (I): Data Centers
• Part Two: The New Golden Age of Computer Networks (II): Wide Area Networks
• Part Three: The New Golden Age of Computer Networks (III): Wireless Networks

Recently, everyone has been playing with ChatGPT, which is really impressive. Although it’s not omnipotent, it’s the first AI dialogue system that doesn’t feel like an artificial idiot to me. It handles difficult problems such as reference and memory context very well. Especially in programming problems, it is sometimes more useful than StackOverflow. If my candidates performed like this, I would definitely prioritize hiring them.

The main shortcomings of ChatGPT currently are:

1. The knowledge base is not updated enough and the coverage is not comprehensive. It cannot answer recent events or more obscure knowledge. It is suggested to combine it with a search engine or knowledge graph, first use the prompt word to search for some results, and then use NLP methods to integrate the search results. It is said that some research teams are already working in this direction.
2. Lack of logical reasoning ability, slightly complex logic can easily get wrong, and answer seriously when it’s wrong. How to solve arbitrarily complex logical problems is a big challenge. It’s even harder to recognize answers that seem correct but are actually absurd.
3. Currently, it only supports text and does not support multimodal. Now you can let ChatGPT generate prompts, and then input them into DALL-E to generate images. In the future, generative models that support multimodal input and multimodal output will make human-computer interaction more natural and may become the next generation of human-computer interaction paradigm.
4. The cost of a single answer is currently high, requiring several cents, significantly higher than the cost of a Google search. If the cost can be reduced through algorithm or hardware improvements, or if new business models can be created by combining with recommendations and advertisements, there will be room for commercial profit.

This year can be said to be the “first year” of AI-generated content. A few months ago, we were all shocked by the stable diffusion (DALL-E 2) in the CV field, and now ChatGPT has set a new SOTA for NLP. Stable diffusion and ChatGPT are both done by OpenAI, and the financial backer behind OpenAI is Microsoft, which can be considered as an important game that Microsoft has won back in the AI field. In previous years, it was always Google Deepmind’s Alpha series that stole the limelight, from Go to proteins and matrix calculations.

The intelligent assistant that can communicate naturally with people is a scene in countless science fiction movies, and it is also a vision set by major companies 20 years ago. Today, we finally see the dawn of becoming a reality. Intelligent assistants may give birth to the next trillion-dollar industry, just like mobile internet has overturned PC internet and video has overturned text, becoming a new paradigm of human-computer interaction and profoundly changing human work and life.

Below are some examples I tried in ChatGPT:

Firstly, it is the scale of business. Due to geographical and cultural reasons, most domestic companies encounter some difficulties in going global, mainly in the domestic market, which is much smaller than the European and American markets. The same is true for public clouds, where the revenue and market value of AWS, Azure, and Google Cloud in the European and American markets are higher than those of Alibaba, Tencent, and Huawei Cloud in China. Since the development cost can basically be shared, the average salary of developers in American companies is higher than that in China, which can hire relatively more excellent talents; it can also generate more profits to support relatively long-term research, such as OpenAI, Deepmind, and Microsoft Research. Breakthrough innovations like ChatGPT are hard to come from product departments with intense development rhythms, they usually come from research departments without much short-term commercial monetization pressure.

Text content to be supplemented, let’s put out a few photos first~

Click here to see the marriage certificate photos

Computer Network & Protocol Laboratory

Huawei’s Computer Network & Protocol Laboratory is a subsidiary of the Distributed and Parallel Software Laboratory of the Central Software Institute of the 2012 Laboratory, with locations in Beijing, Shanghai, Hangzhou, Shenzhen, and Tel Aviv, Israel.

Vision: Rooted in laying the foundation stone, innovation leads the future of distributed communication

Positioning: Huawei’s software engine in the field of computer network and protocol technology, covering theoretical breakthroughs, technological inventions, technological innovations, and quality delivery. Standing at the forefront of this technical field, we research and break through world-class technical problems in computing native networks and wide-area network deterministic communication, build an industry-leading full stack of distributed communication, and work with ICT, terminal, cloud, intelligent car and other main product teams to build differentiated communication competitiveness, gradually grow the industrial ecosystem, and help business success.

Team: A high-level innovation team composed of top industry-leading experts, genius youngsters, PhDs, and engineers mixed special forces, overseas legions. The technical research results are significant. Since 2018, 5 papers have been accepted by SIGCOMM, the top global network communication conference; and key technologies have been selected into Huawei’s top 10 inventions for three consecutive sessions.

I’ll update and preview the cities I have (or will) visited right here!

(July 22, 2022) Due to the pandemic, I haven’t moved for 4 months, missed a May Day wedding, and missed registering our marriage on 5/20; we haven’t seen each other for 4 months either.

Everywhere requires a 14-day travel history code now; I can produce my last 10 years of trips! Following the same logic as the travel code, brief pass-throughs aren’t counted, but transfers (flights/trains) usually are. Since December 13, 2022, the travel code was retired.

From 2012 to July 2022, I’ve been to 42 cities in total, with 259 trips (going from city A to city B counts as one; going back to A counts as two). In 2019 alone, I traveled 63 times. I was quite shocked when I saw these stats. Although I did travel a lot for work over the past three years and even visited 12 cities in Japan in 2019, I still didn’t expect the number to be this high. Why so many business trips? The main team of my first project was in Hangzhou, so from June 2019 to May 2020 I spent most of my time there. In my current project I lead three teams, in Hangzhou, Shanghai, and Israel—just not in Beijing; and as an architect I also often have to join workshops. I guess God decided I’m better suited to long-distance.

During my joint PhD training between USTC and Microsoft Research Asia (2013 senior-year internship ~ 2019 PhD graduation), I often commuted between Hefei and Beijing—already quite a lot for a student. I didn’t expect to travel even more for work afterward; on Umetrip in 2021 I exceeded 97% of users. The 3% who traveled more than me are basically frequent fliers. What I regret is that during my PhD I was too frugal to buy tickets as often, so I spent a lot of time apart from my girlfriend. Another regret is that I was often too lazy to write trip summaries; my memory isn’t great, so after a while I can only rely on photos and chat logs to recall things.

As of November 2023, I’ve been to 71 cities with 380 trips—29 more cities than a year ago—mainly from our honeymoon in Xinjiang after the wedding and my three-month trip to the United States. “City” is actually hard to define. In the US, using counties might be more reasonable; if I recorded every cross-county movement, the US portion would add many more “trips” (and within the Bay Area or Los Angeles–Irvine hardly counts as travel), and I probably wouldn’t be able to remember them all.

Of course, I don’t have permission to get base-station connection data from carriers. The trip data is collected from booking records, business trip records, etc. Since some tickets weren’t booked by me, some trips may have been missed. For example, the time I left MSRA in June 2014 to return to Hefei for my undergraduate graduation can no longer be verified. According to the internship certificate (July 9, 2013 to May 30, 2014), the Beijing-to-Hefei dates have been confirmed.

If you spot any mistakes, feel free to contact me to correct them.

My Footprints

2025

2024

This article summarizes the technical architecture of USTC iCourse.club. Founded in 2015, iCourse is a Flask-based website aiming at rating courses in USTC (University of Science and Technology of China). Although it is only a small website from a technical point of view, it offers a glimpse of the architecture of a typical web service.

USTC LUG GitLab will soon stop serving non-campus users. Although I have a campus email, to prevent sudden disconnection one day, I have backed up all my repositories locally and hung the public repositories on GitHub. As the first user of LUG GitLab, I have a total of 209 repositories, 123 of which are personal repositories. LUG GitLab was established on March 14, 2013 (Pi Day) and has been running for 9 years, even slightly earlier than Telegram. GitLab and VPN are the longest-running (9 years) network services I have established, serving thousands of users. I have long left the management and operation team, but I still have a lot of feelings for these services.

My GitHub homepage: https://github.com/bojieli

My USTC LUG GitLab homepage: https://git.lug.ustc.edu.cn/boj

These public repositories are mainly the course assignments I did at USTC, various undergraduate projects, and network services I did at LUG. Most of the projects I did during my PhD at MSRA have not been open-sourced. I only released the source code of SocksDirect and the LaTeX source code of several papers, some of which have been anonymized and do not retain internal commit information. The source code after work is even less likely to be made public at will. The already open-sourced MindSpore AKG project also anonymized the internal commit information when it was open-sourced (internal contributors after open-sourcing are directly developing on the public repo, but I have left the AKG project after open-sourcing).

Therefore, from the contributions on GitHub, you can see that the most contributions were in 2016, with 2000+ contributions; last year and the year before, there were only a pitiful number of contributions; there were 1000+ contributions from 2013-2015; only a few hundred from 2017-2019, one reason is that the project was not open-sourced, and the other is that I personally was somewhat detached from the front line of coding, pondering new research ideas all day, becoming a PPT engineer, and not spending much time on actually coding to implement ideas, which is also why I published fewer papers in the later stages of my PhD.

“Where did that gazelle go?” Mom suddenly asked me.

The gazelle Mom was talking about was a craft made of black hardwood. It was a gift from my dad when he returned from Africa. It had always been on the corner of my desk. At this moment, my heart started to pound, because I had given it to my good friend Wanfang yesterday.

As one of the founders of the USTC Course Evaluation Community, I am willing to promote the 2021 effort of several collaborators on my personal homepage: PI Review (https://pi-review.com/).

It has been 7 years since the establishment of the Course Evaluation Community in 2015. The community now has over 16,000 reviews and has a significant influence among USTC students. Many students refer to the reviews on the community when selecting courses. Anyone who has pursued a PhD or Master’s degree would likely agree that a mentor is crucial to a student’s life and future during these years. Although there are already many websites for evaluating mentors, such as Mentor Recommendation, Rate My Supervisor, and Research Control, they all have many issues. Here is a more detailed evaluation. The founders of PI Review discovered that there was no satisfactory website, so they established PI Review in 2021 and have added many new features this year.

PI Review operates on an anonymous basis. You only need to verify with your school email to post reviews, but the email verification is solely to prevent spam. Email addresses will never be made public, and we will not send spam emails. All posted reviews are anonymous. We welcome everyone to share their own or their friends’ mentors. If you think a mentor is good, you can help yourself by bringing collaborators on board. If you think a mentor is not good, you can help junior students avoid pitfalls.

PI Review currently suggests evaluating mentors across 5 dimensions, including Advisor Style & Mentorship (hands-on or hands-off, etc.), Expectations (publishing papers, work hours, etc.), Funding & Support (salary, internships, etc.), Research, and Lab Culture. Of course, this is just a reference, and you can evaluate mentors according to your own dimensions.

During my own PhD period, both of my Microsoft co-supervised mentors were very good to me, providing a lot of guidance and help. I have also written reviews on PI Review. If you are interested, you can look them up. The search function for mentors should be quite useful.