# ASPLOS 2025
## Meta Info
Homepage:
### Proceedings
* Volume 1:
* Volume 2:
### Acceptance Rate
* Overall: 17.5% (= 160 / 912)
* Fall: 14.1% (= 46 / 326)
* Major Revision: 20 (invited) -> pending
* Summer: 12.7% (= 65 / 510)
* Major Revision: 42 (invited) -> 40 (accepted)
* Spring: 2.6% (= 2 / 76)
* Major Revision: 7 (invited) -> 7 (accepted)
## Papers
### Large Language Models (LLMs)
* LLM Training
* FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
* PKU & ByteDance
* Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling
* PKU & Alibaba
* Vela: A Virtualized LLM Training System with GPU Direct RoCE
* IBM Research
* Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning
* NUS & GaTech & Alibaba & GMU & SYSU
* LLM Inference
* Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow \[[arXiv](https://arxiv.org/abs/2406.01566)] \[[Code](https://github.com/Thesys-lab/Helix-ASPLOS25)]
* CMU
* Accelerating LLM Serving for Multi-turn Dialogues with Efficient Resource Management
* Korea University
* COMET: Towards Practical W4A4KV4 LLMs Serving
* ICT, CAS
* Past-Future Scheduler for LLM Serving under SLA Guarantees
* Beihang University & SenseTime
* POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference
* UW & MSR India
* Medusa: Accelerating Serverless LLM Inference with Materialization
* THU
* vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention \[[arXiv](https://arxiv.org/abs/2405.04437)]
* MSR India & IIS
* TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
* UIUC & Microsoft Azure Research
* PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
* ICT, CAS & ETH & UofT & NVIDIA
* PIM is All You Need: A CXL-Enabled GPU-Free System for LLM Inference
* UMich & ETH & Google
* Fast On-device LLM Inference with NPUs
* PKU & BUPT
* LLM-based Applications
* Towards End-to-End Optimization of LLM-based Applications with Ayo
* CUHK
* MoE Training
* FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
* HKUST-GZ & HKUST & HIT-SZ
* MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training
* HKUST-GZ
* MoE Inference
* MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
* UC Berkeley
* Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
* SYSU & HKUST & Huawei & Peng Cheng Laboratory
* Retrieval-Augmented Generation (RAG)
* Accelerating Retrieval-Augmented Generation
* Cornell & Kansas & UMass Amherst & Samsung Electronics
* Security
* PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption
* SJTU IPADS
* Coarse-Grained Reconfigurable Array (CGRA)
* PICACHU: Plug-In CGRA Handling Upcoming Nonlinear Operations in LLMs
* NYU
### Deep Learning Recommendation Models (DLRMs)
* DLRM Training
* Frugal: Efficient and Economic Embedding Model Training with Commodity GPUs
* THU
* DLRM Inference
* Load and MLP-Aware Thread Orchestration for Recommendation Systems Inference on CPUs
* Penn State & AMD
### Resource Management
* Shared ML Clusters
* Design and Operation of Shared Machine Learning Clusters on Campus
* HKUST
* Resource Oversubscription
* Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms
* Microsoft
* Serverless Computing
* Litmus: Fair Pricing for Serverless Computing
* Binghamton & Intel Lab
* Concurrency-Informed Orchestration for Serverless Functions
* UVA & Alibaba & Amazon
* Dilu: Enabling GPU Resourcing-on-Demand for Serverless DL Serving via Introspective Elasticity
* ICT, CAS
* Graceful Degradation
* Cooperative Graceful Degradation in Containerized Clouds \[[arXiv](https://arxiv.org/abs/2312.12809)]
* UC Irvine
* Microservice
* Embracing Imbalance: Dynamic Load Shifting among Microservice Containers in Shared Clusters
* University of Macau
### Deep Learning Compilation
* Mosaic: Exploiting Instruction-Level Parallelism on Deep Learning Accelerators with iTex Tessellation
* ICT, CAS & Tencent
* Einsum Trees: An Abstraction for Optimizing the Execution of Tensor Expressions
* Friedrich Schiller University Jena
* Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
* CMU & NVIDIA
* Pruner: A Draft-then-Verify Exploration Mechanism to Accelerate Tensor Program Tuning
* USTC & NIO
* Optimizing Deep Learning Inference Efficiency through Block Dependency Analysis
* ICT, CAS
* Composing Distributed Computations Through Task and Kernel Fusion \[[arXiv](https://arxiv.org/abs/2406.18109)]
* Stanford & NVIDIA
### Parallelism
* PartIR: Composing SPMD Partitioning Strategies for Machine Learning \[[arXiv](https://arxiv.org/abs/2401.11202)]
* Google DeepMind
* GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism \[[arXiv](https://arxiv.org/abs/2406.17145)]
* NVIDIA & CMU & UC Berkeley & MIT
### GPU Sharing
* Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads \[[arXiv](https://arxiv.org/abs/2410.07381)]
* Stanford & UofT
### Performance Prediction
* Forecasting GPU Performance for Deep Learning Training and Inference
* GaTech & Meta
### Checkpointing
* PCcheck: Persistent Concurrent Checkpointing for ML
* ETH
### Memory Disaggregation
* pulse: Accelerating Distributed Pointer-Traversals on Disaggregated Memory
* Yale
* EDM: An Ultra-Low Latency Ethernet Fabric for Memory Disaggregation
* Purdue
### Acceleration
* DynaX: Sparse Attention Acceleration with Dynamic X:M Fine-Grained Structured Pruning
* CQU
* GUST: Graph Edge-Coloring Utilization for Accelerating Sparse Matrix Vector Multiplication
* UMD
* RASSM: Residue-based Acceleration of Single Sparse Matrix Computation via Adaptive Tiling
* GaTech
* Squeezing Operator Performance Potential for the Ascend Architecture
* NJU & Huawei
### Performance Tuning
* DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments \[[Paper](https://dl.acm.org/doi/10.1145/3669940.3707259)]
* Utah & MIT & NEU
### DPU Offloading
* OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with Object Storage
* Alibaba & ZJU
### Tracing
* Automatic Tracing in Task-Based Runtime Systems
* Stanford & NVIDIA
---
# 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/asplos-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.