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// = <NAME> 杨凌云

#align(center, text(size: 20pt)[
  #text(size: 24pt)[*Lingyun Yang*] #text(font: "华文楷体", size: 20pt)[杨凌云]
])

#align(center, text(10pt)[
  #octique-inline("mail") #link("mailto:<EMAIL>") | #octique-inline("globe") #link("https://www.lingyunyang.com")[lingyunyang.com] | #octique-inline("book") #link("https://paper.lingyunyang.com")[Reading Notes] | #emoji.phone.receiver (+86) 135-0284-6103 | #octique-inline("location") Hong Kong
])

== Education
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#link("https://hkust.edu.hk")[*Hong Kong University of Science and Technology*] #h(1fr) *Hong Kong, China* \
Ph.D. in Computer Science and Engineering, Advisor: *<NAME>* #h(1fr) Sep. 2020 -- Present \
- Research Domain: Cluster Management, Model Serving Systems, Resource Scheduling

#link("https://www.scut.edu.cn/en/")[*South China University of Technology*] #h(1fr) *Guangzhou, China* \
B.Eng. in Computer Science and Technology, Elite Class (GPA: 3.82/4) #h(1fr) Sep. 2016 -- Jul. 2020 \
- National Scholarship, The First Prize Scholarship

== Research and Selected Projects
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I have a broad interest in resource management for large-scale data centers / AI infrastructure. Specifically, my research focuses on: (a) improving resource efficiency for AI/GPU clusters; (b) building efficient and low-cost AI model serving systems. \

#link("https://arxiv.org/abs/2407.02031")[*SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules*] #h(1fr) (review, 2407.02031) \
#text(style: "italic", size: 7.5pt)[*Lingyun Yang*, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, Wei Wang] \
- Built an efficient text-to-image system that generates images with diffusion models and add-on modules (i.e., ControlNets and LoRAs).
- Incorporated system designs, e.g., ControlNet-as-a-Service, async bounded LoRA loading, latent parallelelism for CFG computation.
- Achieved up to *7.8×* in serving latency and *1.6×* in throughput without compromising image quality.

*Prism: GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale* #h(1fr) (review) \
#text(style: "italic", size: 7.5pt)[*Lingyun Yang*, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>] \
- Proposed a GPU-disaggregated DLRM serving system to eliminate resource mismatch and meet elastic demand; leveraged RDMA network to offload computation on separate GPU and CPU nodes; resource-aware graph partitioning; topology-aware scheduling.
- In daily scenarios (e.g., a crowded GPU cluster with > 90% allocation rate), reduced CPU fragments by *53%* and GPU fragments by *27%*; In the Double 11 Shopping Festival, saved up to *90%* of GPUs when loaning GPU servers from training clusters.

#link("https://www.usenix.org/conference/atc23/presentation/weng")[*Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent*] #h(1fr) (ATC'23) \
#text(style: "italic", size: 7.5pt)[*Lingyun Yang*, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>] #h(1fr) #octique-inline("mark-github") #link("https://github.com/hkust-adsl/kubernetes-scheduler-simulator")[hkust-adsl/kubernetes-scheduler-simulator] \
- Formally quantified *statistical* GPU resource fragments in shared GPU clusters.
- Proposed the fragmentation gradient descent (FGD) scheduling algorithm to reduce resource fragmentation.
- Reduced unallocated GPUs by up to *49%* compared to state-of-the-art scheduling policies.

*Large-Scale GPU Sharing and Overcommitment in Production* \
- Enabled large-scale GPU sharing in production clusters, with over *10k* shared GPU containers running daily.
- Support the co-location of GPU tasks with different priorities (e.g., latency-sensitive, best-effort).
- Designed and implemented the node-level agent and the cluster-level controller.

#link("https://dl.acm.org/doi/10.1145/3472883.3486987")[*Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving*] #h(1fr) (SoCC'21) \
#text(style: "italic", size: 7.5pt)[*<NAME>*, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>] #h(1fr) #octique-inline("mark-github") #link("https://github.com/kubedl-io/morphling")[kubedl-io/morphling]
- An auto-configuration framework for AI serving on Kubernetes; part of Alibaba's open-sourced #link("https://github.com/kubedl-io/kubedl")[KubeDL], a #link("https://landscape.cncf.io/?item=provisioning--automation-configuration--kubedl")[CNCF sandbox] project.
- Combined meta-learning and bayesian optimization to quickly find the *optimal* resource configuration (e.g., CPU cores, GPU memory, GPU timeshare, GPU type) and runtime parameters (i.e., batch size).

== Work Experience
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*Alibaba Group* #h(1fr) *Hangzhou, China* \
_Research Intern_, Cluster Management Group, Mentor: *<NAME>* #h(1fr) Dec. 2020 -- Present \
- Conducted several research projects including Morphling, GPU sharing, FGD, Prism, SwiftDiffusion (details as mentioned above).

*Microsoft Research Asia* #h(1fr) *Beijing, China* \
_Research Intern_, Innovation Engineering Group #h(1fr) Jul. 2019 -- Jun. 2020 \
- Conducted research on model robustness of face recognition. Star of Tomorrow Internship Award of Excellence.

== Technical Skills
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- *Programming: * Golang, C++, Python, JavaScript, asynchronous, multithread, multiprocess, distributed, RDMA \
- *Machine Learning: * PyTorch, TensorFlow, Numpy, Matplotlib, HuggingFace \
- *Full Stack: * Web Frontend, Backend, SQL, Grafana, Docker, Kubernetes, Git, CI/CD \

