cs.DC · 2026-08-06 · No. 76

Distributed, Parallel, and Cluster Computing, 2026-08-06.

6 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System

    Shiyang Li, Guangyan Sun, Jinwei Tang, Yanzhi Wang, Mingyi Hong, Caiwen Ding

    cs.DC · cs.LG

    Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning. Their GPU performance depends strongly on the input sparsity pattern and execution strategy. For the same SpMM on the same matrix, cuSPARSE exhibits a 350x performance gap between CSR and Blocked-ELL. Our study of multiple data formats, specialized systems, and sparse compilers shows that no single implementation consistently dominates across...

    arxiv.org/abs/2608.05033 · PDF

  2. 02

    RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference

    Guotao Yang, Mingxi Zhao, Haopeng Li, Zhengchao Wang, Sheng Chen, Yitao Hu, Keqiu Li

    cs.DC

    Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local deployment often requires costly hardware. Split inference offers a middle ground by executing the model head, tail, and tools locally and the middle layers in the cloud, but its local-cloud-local path transfers boundary hidden states at every invocation and creates a...

    arxiv.org/abs/2608.04991 · PDF

  3. 03

    AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks

    Guotao Yang, Hao Chen, Rui Guo, Xinyu Li, Liang Zheng, Sheng Chen, Yitao Hu, Keqiu Li

    cs.DC

    Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative block. Under a constrained uplink, candidate messages may queue while the verifier is idle. Stop-and-wait scheduling leaves edge compute underutilized; optimistic same-request runahead can waste work when a rejection or an unexpected bonus token invalidates...

    arxiv.org/abs/2608.04974 · PDF

  4. 04

    AFD-Ledger: Deployment Provisioning for Attention--FFN Disaggregation

    Chengyu Qiu, Xiao Fu, Fengcun Li, Yulei Qian, Yuchen Xie, Xunliang Cai, Yingdi Shan, Yongwei Wu, Mingxing Zhang

    cs.DC · cs.AI

    Attention--Feed-Forward Network (FFN) Disaggregation (AFD) is emerging as a promising architecture for serving Mixture-of-Experts (MoE) language models. While existing AFD systems improve the efficiency of disaggregated execution, they leave a deployment question unanswered: under the same model, workload, time-per-output-token (TPOT) service-level objective (SLO), hardware budget, hardware catalog, and runtime capabilities, does AFD provide...

    arxiv.org/abs/2608.04502 · PDF

  5. 05

    CommBench: Can LLMs Write Correct and Efficient GPU Communication Code?

    Shuang Ma, Yuyi Li, Yihan Zhang, Hezhi Xie, Danyang Chen, Shuyang Ji, Ziming Mao, Cheng Ji, Ansha Prashanth, Wenting...

    cs.DC

    Training and serving large language models (LLMs) rely heavily on high-performance GPU communication, yet implementing efficient GPU communication primitives requires deep expertise in GPU architectures, networking hardware, and distributed communication patterns, making them particularly challenging for code generation models. We present CommBench, a comprehensive benchmark for GPU communication programming, consisting of over 100...

    arxiv.org/abs/2608.04450 · PDF

  6. 06

    Zero-Instrumentation Dependency Discovery for Guided Microservice Migration Using eBPF

    Eshan Trivedi, Chandrahasa Pranava

    cs.DC · cs.NI

    Migrating microservices across virtual machines (VMs) without knowledge of their runtime communication patterns risks creating cross-VM hotspots and latency spikes that are difficult to predict from static analysis alone. We use extended Berkeley Packet Filter (eBPF) kernel-level network tracing to automatically discover inter-service dependencies at runtime, with no application instrumentation, and use the resulting dependency graph to...

    arxiv.org/abs/2608.04413 · PDF

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