# 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