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GITBOOK-198: Organize the papers of SoCC '24
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| # SoCC 2024 | ||
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| ## Meta Info | ||
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| Homepage: [https://acmsocc.org/2024/index.html](https://acmsocc.org/2024/index.html) | ||
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| Paper list: [https://acmsocc.org/2024/schedule.html](https://acmsocc.org/2024/schedule.html) | ||
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| ## Papers | ||
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| ### Large Language Models (LLMs) | ||
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| * LLM inference | ||
| * Queue Management for SLO-Oriented Large Language Model Serving \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698523)] | ||
| * UIUC & IBM Research | ||
| * LLM training | ||
| * Distributed Training of Large Language Models on AWS Trainium \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698535)] | ||
| * AWS | ||
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| ### Mixture of Experts (MoEs) | ||
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| * MoE inference | ||
| * MoEsaic: Shared Mixture of Experts \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698521)] | ||
| * IBM Research | ||
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| ### GPU Sharing | ||
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| * KACE: Kernel-Aware Colocation for Efficient GPU Spatial Sharing \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698555)] | ||
| * Stony Brook University | ||
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| ### Serverless Computing | ||
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| * On-demand and Parallel Checkpoint/Restore for GPU Applications \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698563)] | ||
| * SJTU IPADS & Shanghai Artificial Intelligence Research Institute | ||
| * **gCROP**: **G**PU **C**heckpoint/**R**estore made **O**n-demand and **P**arallel | ||
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| ### Resource Scheduler | ||
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| * Scheduler for deep learning training workloads | ||
| * Hops: Fine-grained heterogeneous sensing, efficient and fair Deep Learning cluster scheduling system \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698515)] | ||
| * Anhui University & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center | ||
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| ### Distributed Training | ||
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| * Generative Adversarial Networks (GANs) | ||
| * ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks \[[Paper](https://dl.acm.org/doi/10.1145/3698038.3698563)] | ||
| * NUS |