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ASPLOS 2026

Meta Info

Homepage: https://www.asplos-conference.org/asplos2026/

Paper List

Acceptance Rate

  • Review model: ASPLOS 2026 used two submission cycles (Spring and Summer) and retained a Major Revision path for selected papers.

  • Spring Cycle: 9.6% (= 20 / 208)

    • Major Revision: 9.1% (= 19 / 208)

  • Summer Cycle: 15.7% (= 132 / 840)

  • Total: 14.5% (= 152 / 1048)

Papers

Large Language Models (LLMs)

  • LLM Inference

    • Prefill-Decode Multiplexing

      • Towards High-Goodput LLM Serving with Prefill-decode Multiplexing [Paper] [arXiv]

        • SJTU & HKU & NUS

        • Propose MuxWise, an LLM serving framework built on intra-GPU prefill-decode multiplexing.

        • Integrate a bubble-less multiplex engine, a contention-tolerant estimator, and an SLO-aware dispatcher.

      • Bullet: Boosting GPU Utilization for LLM Serving via Dynamic Spatial-Temporal Orchestration [Paper] [arXiv] [Code]

        • SYSU

        • Enable concurrent execution of prefill and decode requests.

        • Dynamically provision GPU resources based on real-time performance modeling.

      • TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill & Decode Inference [Paper]

        • PKU & Tencent YouTu Lab

        • Introduce tensor-parallel latent attention for disaggregated prefill/decode inference.

        • Combine latent attention with tensor parallelism to improve PD-disaggregated long-context serving.

    • Scheduling

      • QoServe: Breaking the Silos of LLM Inference Serving [Paper] [arXiv]

        • MSR India

        • Introduce fine-grained QoS classification so applications can specify precise latency requirements, and adapt scheduling decisions to real-time system state.

        • Leverage the predictable execution characteristics of LLM inference to implement dynamic chunking for higher throughput under strict QoS guarantees.

        • Combine hybrid prioritization with selective request relegation to balance fairness, efficiency, and graceful degradation under overload.

      • Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloads [Paper] [arXiv]

        • Snowflake

        • Introduce Shift Parallelism, a runtime that switches across inference parallelism strategies for dynamic workloads.

        • Turn parallelism selection into a runtime control decision to jointly improve latency and throughput.

      • XY-Serve: End-to-End Versatile Production Serving for Dynamic LLM Workloads [Paper]

        • Huawei & THU & Shanghai AI Lab

        • Present XY-Serve, an end-to-end serving system for dynamic production LLM workloads.

        • Coordinate scheduling, batching, and runtime resource management to sustain serving efficiency under workload variation.

      • BlendServe: Optimizing Offline Inference with Resource-Aware Batching [Paper]

        • UC Berkeley & UW & UC Davis & Rice

        • Present a resource-aware batching framework for offline inference.

        • Form batches against actual compute and memory bottlenecks to improve throughput.

    • MoE Inference

      • MoE-APEX: An Efficient MoE Inference System with Adaptive Precision Expert Offloading [Paper]

        • SJTU & CUHK

        • Introduce an MoE inference system with adaptive-precision expert offloading.

        • Jointly tune expert offloading and precision to reduce memory pressure during serving.

    • Compression

      • ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression [Paper] [arXiv] [Code]

        • HKUST-GZ & HIT-SZ & HKUST

        • Introduce hardware-aware lossless compression for LLM inference.

        • Reduce memory footprint while preserving exact model behavior and improving serving efficiency.

    • Speculative Decoding

      • DFVG: A Heterogeneous Architecture for Speculative Decoding with Draft-on-FPGA and Verify-on-GPU [Paper]

        • SJTU & Eastern Institute of Technology, Ningbo & Southeast University & Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo

        • Propose a heterogeneous speculative decoding architecture with FPGA draft generation and GPU verification.

        • Pipeline draft and verify across devices to reduce end-to-end decoding latency.

      • SwiftSpec: Disaggregated Speculative Decoding and Fused Kernels for Low-Latency LLM Inference [Paper]

        • ByteDance Seed & UChicago

        • Introduce disaggregated speculative decoding together with fused kernels for low-latency LLM inference.

        • Combine system-level disaggregation and kernel-level optimization to make speculative decoding practical in deployment.

    • Sparsity

      • SpeContext: Enabling Efficient Long-context Reasoning with Speculative Context Sparsity in LLMs [Paper]

        • SJTU & Infinigence-AI & SII & THU

        • Introduce speculative context sparsity for long-context reasoning in LLMs.

        • Avoid uniform full-context processing by speculating over sparse context usage during long-input inference.

    • Attention Mechanisms

      • I/O Analysis is All You Need: An I/O Analysis for Long-Sequence Attention [Paper]

        • IIT & ICT, CAS & UCAS

        • Present an I/O-centric analysis framework for long-sequence attention.

        • Show that data movement, rather than FLOPs alone, dominates long-context attention cost.

      • PAT: Accelerating LLM Decoding via Prefix-Aware Attention with Resource Efficient Multi-Tile Kernel [Paper]

        • TJU & Stevens Institute of Technology

        • Introduce prefix-aware attention together with a multi-tile kernel for LLM decoding.

        • Reduce decode latency by exploiting shared prefixes while keeping GPU resource usage under control.

    • Value Level Parallelism (VLP)

      • Mugi: Value Level Parallelism For Efficient LLMs [Paper]

        • CMU & UCF

        • Introduce value-level parallelism as a new execution dimension for LLM inference.

        • Exploit finer-grained parallel structure than conventional tensor or sequence parallelism.

    • KV Cache Offloading

      • REPA: Reconfigurable PIM for the Joint Acceleration of KV Cache Offloading and Processing [Paper]

        • SJTU

        • Present a reconfigurable PIM architecture for jointly offloading and processing KV cache.

        • Co-design KV movement and KV computation to reduce host-memory bottlenecks during inference.

      • STARC: Selective Token Access with Remapping and Clustering for Efficient LLM Decoding on PIM Systems [Paper]

        • RPI & UMass Amherst & IBM Research

        • Introduce selective token access with remapping and clustering for PIM-based LLM decoding.

        • Reduce unnecessary KV accesses and improve data locality during decoding.

  • LLM Training

    • RL Post-Training

      • Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter [Paper] [arXiv] [Code]

        • MIT & NVIDIA & ETH Zurich & MIT-IBM AI Lab & UMass Amherst

        • Present TLT, a reasoning RL training system that accelerates long-tail rollout generation with adaptive speculative decoding.

        • Train an Adaptive Drafter on idle GPUs during long-tail generation and use an Adaptive Rollout Engine to select speculative-decoding strategies per input batch.

      • History Doesn't Repeat Itself but Rollouts Rhyme: Accelerating Reinforcement Learning with RhymeRL [Paper]

        • SJTU & ByteDance

        • Present RhymeRL, a framework that accelerates RL by exploiting reusable structure across rollout histories.

        • Reduce redundant rollout work to improve training efficiency for LLM-aligned RL workloads.

    • Multimodal Model Training

      • DIP: Efficient Large Multimodal Model Training with Dynamic Interleaved Pipeline [Paper]

        • SJTU & StepFun & Zenergize AI

        • Introduce a dynamic interleaved pipeline for large multimodal model training.

        • Increase pipeline utilization by interleaving stages dynamically across modalities and training phases.

    • Mixed-Precision Training

      • SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training [Paper] [arXiv]

        • UMich & Meta & UMass Amherst

        • Present an adaptive mixed-precision framework for subbyte LLM training.

        • Periodically profile training statistics and solve precision allocation to assign fine-grained bitwidths.

    • Diagnosis

      • Fine-grained and Non-intrusive LLM Training Monitoring via Microsecond-level Traffic Measurement [Paper]

        • NJU & NUS & Infrawaves

        • Introduce microsecond-level traffic measurement for fine-grained, non-intrusive LLM training monitoring.

        • Infer communication and runtime behavior without intrusive application instrumentation.

    • Offloading

      • SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips [Paper] [arXiv]

        • UIUC & Microsoft & Snowflake

        • Revisit large-scale LLM training on tightly coupled GPU-CPU superchips with SuperOffload.

        • Combine adaptive weight offloading with superchip-aware runtime optimizations to improve long-context training throughput.

  • Language Processing Units (LPUs)

    • Hardwired-Neuron Language Processing Units as General-Purpose Cognitive Substrates [Paper]

      • ICT, CAS & USTC & IS, CAS & Cambricon Technologies

      • Propose Language Processing Units (LPUs) as a language-centric hardware substrate for general-purpose cognitive workloads.

      • Specialize the architecture around language processing primitives to improve efficiency on language-centric tasks.

Generative Recommenders (GRs)

  • GR Serving

    • BAT: Efficient Generative Recommender Serving with Bipartite Attention [Paper]

      • ZJU & HKU & Alibaba & NUS & Aalto University

      • Introduce bipartite attention for generative recommender serving.

      • Tailor the serving design to recommendation-style generative workloads rather than generic LLM inference.

Diffusion Models

  • Video DiT Training

    • DSV: Exploiting Dynamic Sparsity to Accelerate Large-Scale Video DiT Training [Paper] [arXiv]

      • CUHK & StepFun

      • Exploit dynamic sparsity to accelerate large-scale video DiT training.

      • Use hybrid sparsity-aware context parallelism to rebalance workloads under heterogeneous attention sparsity.

  • Diffusion Model Serving

    • TetriServe: Efficiently Serving Mixed DiT Workloads [Paper] [arXiv] [Code]

      • UMich & UW-Madison & NTU

      • Present a serving system for mixed DiT workloads.

      • Coordinate scheduling and batching across heterogeneous diffusion requests in a shared runtime.

  • Mixture-of-Diffusion Models

    • MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models [Paper] [Code]

      • UMich & Intel Labs

      • Introduce mixture-of-diffusion models for image generation serving.

      • Use specialization across diffusion sub-models to improve efficiency and quality-cost tradeoffs.

Deep Learning Training

  • T-Control: An Efficient Dynamic Tensor Rematerialization System for DNN Training [Paper]

    • ICT, CAS

    • Present a dynamic tensor rematerialization system for DNN training.

    • Adjust rematerialization online to balance memory savings and recomputation overhead.

  • NotebookOS: A Replicated Notebook Platform for Interactive Training with On-Demand GPUs [Paper]

    • GMU & Adobe Research & UVA

    • Present a replicated notebook platform for interactive model training with on-demand GPUs.

    • Combine notebook-centric workflow support with elastic GPU provisioning.

Deep Learning Compilation

  • FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow [Paper]

    • Stanford & SambaNova Systems & Barcelona Supercomputing Center

    • Present a fusion-centric compilation framework for sparse deep learning on streaming dataflow hardware.

    • Expand fusion opportunities for sparse operators to improve accelerator execution efficiency.

  • Trinity: Three-Dimensional Tensor Program Optimization via Tile-level Equality Saturation [Paper]

    • KAIST & FuriosaAI

    • Introduce tile-level equality saturation for three-dimensional tensor program optimization.

    • Use equivalence-based search to discover better accelerator-friendly tensor rewrites.

  • RedFuser: An Automatic Operator Fusion Framework for Cascaded Reductions on AI Accelerators [Paper]

    • Alibaba Cloud

    • Present an automatic operator fusion framework for cascaded reductions on AI accelerators.

    • Target reduction-heavy operator chains that are poorly handled by existing compiler passes.

  • Linear Layouts: Robust Code Generation of Efficient Tensor Computation Using F2 [Paper]

    • GMU & OpenAI

    • Introduce linear-layout abstractions for tensor code generation.

    • Improve portability and performance by reducing reliance on brittle layout-specific code generation.

GPU Systems

  • GPU Scheduling

    • gShare: Efficient GPU Sharing with Aggressive Scheduling in Multi-tenant FaaS Platform [Paper]

      • China Telecom Cloud Computing Research Institute & China Telecom Cloud Technology Co. Ltd.

      • Present an aggressive GPU sharing and scheduling framework for multi-tenant FaaS platforms.

      • Improve utilization through fine-grained temporal multiplexing across serverless tenants.

    • GFS: A Preemption-aware Scheduling Framework for GPU Clusters with Predictive Spot Instance Management [Paper]

      • SJTU & ZJU & Alibaba

      • Present a preemption-aware scheduling framework for GPU clusters with predictive spot instance management.

      • Jointly schedule jobs and volatile spot capacity to reduce disruption and improve cluster efficiency.

  • GPU Communication

    • MSCCL++: Rethinking GPU Communication Abstractions for AI Inference [Paper] [Code]

      • MSR & Microsoft Azure

      • Present a new GPU communication abstraction stack tailored to AI inference.

      • Move beyond training-centric collective abstractions to better support inference communication patterns.

  • GPU Programming

    • cuJSON: A Highly Parallel JSON Parser for GPUs [Paper]

      • UC Riverside

      • Introduce a highly parallel JSON parser for GPUs.

      • Make JSON parsing a scalable GPU primitive for preprocessing and data-serving pipelines.

    • CHERI-SIMT: Implementing Capability Memory Protection in GPGPUs [Paper]

      • Cambridge

      • Implement capability-based memory protection in GPGPUs with CHERI-SIMT.

      • Bring stronger spatial memory safety and isolation to SIMT execution.

    • Lobster: A GPU-Accelerated Framework for Neurosymbolic Programming [Paper]

      • UPenn

      • Present a GPU-accelerated framework for neurosymbolic programming.

      • Provide systems support for workloads that combine symbolic and neural computation.

Profiling

  • Deep Learning Profiling

    • DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads [Paper]

      • NCSU & GMU

      • Introduce a context-aware profiling and analysis tool for deep learning workloads across platforms and frameworks.

      • Use execution context to explain performance behavior beyond isolated kernel-level statistics.

Acronyms

  • DiT: Diffusion Transformer

  • GR: Generative Recommender

  • LLM: Large Language Model

  • LPU: Language Processing Unit

  • MoE: Mixture-of-Experts

  • PIM: Processing-in-Memory

  • RL: Reinforcement Learning

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