cs.DC · 2026-09-21 · No. 120
Distributed, Parallel, and Cluster Computing, 2026-09-21.
8 new papers in cs.DC. Titles, authors,
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01 — The papers
8 entries-
01
Distributed Balanced Butterfly Counting in Signed Bipartite Graphs
Kiran Mekala, Apurba Das, Suman Banerjee
cs.DC
The balanced butterfly is a fundamental primitive for analyzing signed bipartite graphs and provides a basis for studying higher-order structural properties, such as clustering coefficients and community structure. Despite its importance, existing approaches primarily rely on serial algorithms for balanced butterfly counting, which become inefficient on large-scale graphs. To address this limitation, we propose a distributed algorithm, D-BBC,...
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02
PoVD: Efficient Consensus Protocol based on Verifiable Delay Function
Rui Jiang, Xintong Ling, Bin Cao, Jiaheng Wang, Xiqi Gao, Zhi Ding
cs.DC
Consensus protocols ensure the robustness and scalability of blockchains and decentralized applications built on them. However, existing consensus mechanisms often impose high computational cost or require heavy communication overhead. To address these challenges, we propose proof of verifiable delay (PoVD), a lightweight consensus protocol based on the verifiable delay function (VDF). We present the detailed protocol of PoVD, including the...
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03
HyperParallel-FSDP: Topology-Aware Fully Sharded Training with Layout-Driven Muon on Ascend SuperPods
Mo Sun, Yifan Yao, Yanwei Liu, Luobin Liu, Zhenzhang Yang, Kaisheng Wang, Xiangyu Meng, Chen Li, Xizheng Pang,...
cs.DC
Declarative SPMD programming uses tensor sharding descriptions to drive distributed execution, separating parallelization from model code. However, the evaluated PyTorch DTensor stack dispatches every operator below autograd, incurring repeated dispatch and metadata costs, while lacking an inexpensive end-to-end validation path. Existing FSDP and distributed Muon implementations also mismatch two-tier supernode topologies: FSDP relies on...
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04
Weave: Fine-Grained Dynamic SM Scheduling in an MoE Megakernel for Compute-Communication Overlap
Ziyu Huang, Yangjie Zhou, Chenhao Zhu, Zihan Liu, Jinyu Liu, Shulai Zhang, Xingxun Tang, Hongzhe Yan, Xinhao Luo,...
cs.DC
Mixture-of-Experts (MoE) inference under expert parallelism (EP) turns each MoE layer into a distributed computation with costly dispatch and combine communication. State-of-the-art systems reduce this cost through communication-computation overlap, splitting the GPU's SMs for communication and computation respectively. However, this approach still leaves GPU resources wasted along two dimensions. Spatially, the best SM split is determined by...
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05
TokaGLINT: A Scalable GPU-Tailored Implicit Solver for Full 3D Tokamak Electromagnetic Simulations
Zifan Yang, Haoyuan Zhang, Jialin Li, Wu Yuan, Xiazhen Liu, Jian Zhang, Jianyuan Xiao, Shan Liang
cs.DC
We introduce TokaGLINT, a GPU-accelerated implicit solver for electromagnetic field computations in full 3D tokamak simulations, aimed at efficient large-scale parallel GPU computing. Its central innovation lies in the co-design of hierarchical domain decomposition and a fast exact local solver, where hierarchical partitioning is tailored to match fine-grained intra-card subdomains and exploit the tensor-based solver dedicated to...
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06
Brain API: An Intent-Aware Control Plane for Policy-Governed Agentic Systems
Alexander Chernov
cs.DC
Contemporary cloud and distributed systems expose control through resource-centric abstractions: services, deployments, network flows, execution graphs. Agentic and tool-augmented systems have meanwhile shifted application logic toward intent-driven, adaptive execution. Existing control planes, workflow engines and service meshes lack abstractions for intent-level decision governance: they cannot represent high-level goals as first-class...
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07
AI-Driven Scientific Computing Workflows: A Systems Review of Orchestration, Execution, Reproducibility and Provenance
Jamie J. Alnasir
cs.DC
Artificial intelligence (AI) is increasingly embedded within scientific computing workflows that combine simulation, data processing, optimisation, visualisation and experimental or observational components. Learned models may serve as explicit workflow components, retain persistent state and, in adaptive settings, influence subsequent computation. Existing work has characterised scientific workflow management systems, dynamic and steered...
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08
Cloud-Side Transactional Orchestration Framework for Resource-Constrained Embedded Systems
Pravin Nagare, Aditya Sabbineni, Preetam Dedu, Willison Lopes
cs.DC · cs.NI
As digital commerce ecosystems expand into low-end consumer electronics (CE), hardware constraints-specifically limited CPU duty cycles and volatile heap fragmentation-become significant bottlenecks for complex transactional flows. Traditional on-device middleware requires high "network chattiness" to manage multi-step state machines, leading to increased latency and potential transaction failure on unstable residential networks. This paper...
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