Preliminary Program – ASPLOS 2025

The following content is copyrighted from ASPLOS 2025 | Awesome Papers, and I have extracted parts that are of interest.

LLM Inference

• Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow [arXiv] [Code]
  • 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]
  • 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 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

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]
    • UC Irvine
• Microservice
  • Embracing Imbalance: Dynamic Load Shifting among Microservice Containers in Shared Clusters
    • University of Macau
• GPU Sharing
  • Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads [arXiv]
    • Stanford & UofT