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GITBOOK-193: Organize SOSP 24 papers
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| # SOSP 2024 | ||
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| ## Meta Info | ||
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| Homepage: [https://sigops.org/s/conferences/sosp/2024/](https://sigops.org/s/conferences/sosp/2024/) | ||
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| ## Papers | ||
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| ### Large Language Models (LLMs) | ||
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| * LLM Training | ||
| * Enabling Parallelism Hot Switching for Efficient Training of Large Language Models | ||
| * PKU | ||
| * Perseus: Removing Energy Bloat from Large Model Training \[[arXiv](https://arxiv.org/abs/2312.06902)] | ||
| * UMich | ||
| * Use a graph cut-based algorithm to obtain the "iteration time-energy" Pareto frontier; schedule the energy consumption across time. | ||
| * LLM Inference | ||
| * LoongServe: Efficiently Serving Long-context Large Language Models with Elastic Sequence Parallelism \[[arXiv](https://arxiv.org/abs/2404.09526)] | ||
| * PKU | ||
| * ESP: Elastic Sequence Parallelism | ||
| * Elastically adjust the degree of parallelism in real-time; reduce key-value cache migration overhead and overlap partial decoding communication with computation; reduce key-value cache fragmentation across instances. | ||
| * PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU \[[arXiv](https://arxiv.org/abs/2312.12456)] | ||
| * SJTU IPADS | ||
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| ### ML Serving | ||
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| * Improving DNN Inference Throughput using Practical, Per-Input Compute Adaptation | ||
| * GaTech & Princeton | ||
| * Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving \[[arXiv](https://arxiv.org/abs/2312.05385)] | ||
| * Princeton & GaTech | ||
| * Automatically apply and manage early exits (certain inputs can exit with results at intermediate layers) in ML models. | ||
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| ### Distributed Training | ||
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| * SlipStream: Adapting Pipelines for Distributed Training of Large DNNs Amid Failures \[[arXiv](https://arxiv.org/abs/2405.14009)] | ||
| * Stanford | ||
| * Dynamically re-route the work of a failed server to data-parallel peers; execute within bubbles of the original pipeline schedule. | ||
| * Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections \[[arXiv](https://arxiv.org/abs/2312.05181)] | ||
| * ICL | ||
| * **Tenplex** — a state management library. | ||
| * Enable jobs to change the parallelism dynamically. | ||
| * PTC: Parallelizable Tensor Collection | ||
| * Dataset state | ||
| * Modle state  | ||
| * Execute PTC transformations in parallel with minimum data movement between workers. | ||
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| ### ML Compilation | ||
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| * Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor \[[arXiv](https://arxiv.org/abs/2408.04808)] | ||
| * UIUC & MSRA | ||
| * **T10**, the first DL compiler to exploit the inter-core communication bandwidth and distributed on-chip memory on AI chips (i.e., Graphcore IPU). | ||
| * SilvanForge: A Schedule-Guided Retargetable Compiler for Decision Tree Inference | ||
| * IISc | ||
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| ### Serverless Computing | ||
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| * Dirigent: Lightweight Serverless Orchestration \[[arXiv](https://arxiv.org/abs/2404.16393)] | ||
| * ETH | ||
| * Simplify state management of the existing orchestration system (Kubernetes); eliminate persistent state updates; run monolithic control and data planes to minimize internal communication overheads. |