# PPoPP 2025 ## Meta Info Homepage: Paper list: ### Acceptance Rate 20.1% (= 38 / 189) ## Papers ### Large Language Models (LLMs) * LLM Training * ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training * Oregon & Pacific Northwest National Laboratory & William and Mary * Mario: Near Zero-cost Activation Checkpointing in Pipeline Parallelism * ICT, CAS * WeiPipe: Weight Pipeline Parallelism for Communication-Effective Long-Context Large Model Training * THU & NUS & CETHIK & Lynxi Technology * LLM Inference * MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models * ISTA & Universidade da Coruña & ETH & IST Austria ### Mixture-of-Experts (MoEs) * MoE Training * Harnessing Inter-GPU Shared Memory for Seamless MoE Communication-Computation Fusion * WHU & NVIDIA & UMacau ### Graph Neural Networks (GNNs) * GNN Training * Adaptive Parallel Training for Graph Neural Networks \[[Code](https://anonymous.4open.science/r/APT-1CAB)] * CUHK * GNN Inference * Helios: Efficient Distributed Dynamic Graph Sampling for Online GNN Inference * ZJU & Alibaba ### GPU Sharing * SGDRC: Software-Defined Dynamic Resource Control for Concurrent DNN Inference on NVIDIA GPUs * HKUST ### Sparse Matrix-Matrix Multiplication (SpMM) * Acc-SpMM: Accelerating General-purpose Sparse Matrix-Matrix Multiplication with GPU Tensor Cores * Computer Network Information Center, CAS & RUC & Hangzhou Dianzi University * FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores * BUPT --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://paper.lingyunyang.com/reading-notes/conference/ppopp-2025.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.