ASPLOS 2025

Meta Info

Homepage: https://www.asplos-conference.org/asplos2025/

Acceptance Rate

• Summer: 12.7% (65 / 510)

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] [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 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]

    • 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]

  • Stanford & NVIDIA

Parallelism

• PartIR: Composing SPMD Partitioning Strategies for Machine Learning [arXiv]

  • Google DeepMind

• GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism [arXiv]

  • NVIDIA & CMU & UC Berkeley & MIT

GPU Sharing

• Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads [arXiv]

  • 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]

  • 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