cs.DC · 2026-08-10 · No. 80
Distributed, Parallel, and Cluster Computing, 2026-08-10.
8 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
8 entries-
01
Fast end-to-end cloud application cold-start with initscripts
Ariel Szekely, Robert Morris, M. Frans Kaashoek
cs.DC · cs.OS
Serverless functions are a popular way of deploying cloud applications. Because many of these functions are short- running and experience frequent cold-starts, start latencies often dominate their execution latency. Start latency can be broken down into two components: setup and initialization. Setup involves steps the cloud platform takes when starting an application, such as downloading its binary and creating an isolated execution...
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02
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Xiangyu Meng, Peng Chen, Mingzhen Li, Jianmin Wang, Sen Wang, Guangming Tan, Weile Jia, Mohamed Wahib, Tao Luo, Xun Wang
cs.DC · cs.AI
Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock leverages Glowworm Swarm Optimization (GSO) for accuracy, yet suffers from limited parallelism, irregular computation, and severe load imbalance, preventing efficient execution on GPU supercomputers. We present SparkleDock, a scalable GSO-based docking framework...
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03
HiSparse: Scaling Sparse-Attention Decoding with Hierarchical KV Cache Management
Zhiqiang Xie, Zhangheng Huang, Tingwei Huang, Ziyi Xu, Ruiyang Ma, Christos Kozyrakis
cs.DC
Top-k sparse attention makes long-context LLM decoding cheap to compute: each step reads only a few thousand selected KV entries rather than the full context. Serving systems, however, typically keep the entire KV cache in GPU HBM so that every position stays selectable, so a request's memory bill still grows with its full context length--decoding hits a capacity wall long before it runs out of compute, and a context whose KV cache exceeds...
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04
A Kubernetes Scheduler Plugin for Cluster-Wide Placement Optimisation
Henrik Daniel Christensen, Saverio Giallorenzo, Jacopo Mauro
cs.DC
The default scheduler of Kubernetes, the state-of-the-art container orchestrator, uses fast, local placement decisions. Unfortunately, this design leads to resource fragmentation, reduced cluster usage, and overprovisioning. External solvers can compute global placement plans, but enforcing these plans in upstream clusters is hard. Kubernetes provides no native cross-node preemption, uncoordinated concurrent scheduling leads to...
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05
A Self-Adaptive Extensible CEP framework for the Cloud-Edge Continuum
Olaf Markus Link, Sukanya Bhowmik
cs.DC
Traditional complex event processing (CEP) systems focus on extracting information out of simple events in the input event streams to detect complex event patterns. In the context of CEP, a computing unit that executes a complex data transformation task is called an operator. Typically, a CEP system consists of an operator graph that could be distributed over the cloud-edge continuum. However, an operator graph distributed over heterogeneous...
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06
Stream Learning: Partition-Fair Gossip Learning Without Tokens
Fabien Mathieu, Alexandre Pham, Maria Gradinariu Potop-Butucaru, S{é}bastien Tixeuil
cs.DC · cs.LG
In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models. The state-of-the-art protocol, Partitioned Token Gossip Learning (PTGL) of Heged{ü}s et al., splits the weight matrix into S fixed partitions and disseminates them using a token-based fairness mechanism coupled with per-neighbor metadata exchange. We revisit partition scheduling by...
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07
StateFlow: Sequence Pipeline Parallelism for Long-Context Modeling with Linear Recurrence
Wenxuan Zhao, Yingfa Chen, Xu Han, Wenjing Han, Tianbo Huang, Zhiyu Li, Ao Sun, Jingheng Xu, Lin Gan, Guangwen Yang
cs.DC
Long-context training is increasingly important for large language models, and linear attention and state space models have become popular for improving long-context efficiency. However, efficiently parallelizing long-sequence training for recurrent and hybrid models remains challenging. We present StateFlow, a sequence pipeline parallelism system for models with linear recurrence. StateFlow partitions each sequence into chunks and schedules...
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08
DGEMM with Ozaki Scheme I/II on FP4 Tensor Cores: A Base-13 E2M1 Limb Representation
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri
cs.DC
This paper proposes a method and its implementation for emulating FP64 matrix multiplication (DGEMM) by constructing, on FP4 (E2M1; 2 exponent bits and 1 mantissa bit) Tensor Cores, Ozaki schemes I and II, which realize high-precision matrix multiplication on low-precision arithmetic units. Prior implementations were based on INT8 and FP8, and the use of the faster FP4 had not been realized. The key property is that every FP4 value becomes an...
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