cs.DC · 2026-06-28 · No. 37
Distributed, Parallel, and Cluster Computing, 2026-06-28.
5 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
5 entries-
01
CHAMB-GA: A Containerized HPC Scalable Microservice-Based Framework for Genetic Algorithms
Felix Bonhoff, Thiemo Pesch, Andrea Benigni, Alexander Mitsos, Manuel Dahmen
cs.DC · math.OC
Metaheuristic-based global optimization with embedded, long-running simulations is a computationally expensive process. To support various stages of development and execution, a seamless transition from personal computers to distributed clusters is desired, enabling execution across all computational scales. However, existing tool chains are often characterized by rigidity and hardware-bound constraints, which impede scalability and the...
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02
DMuon: Efficient Distributed Muon Training with Near-Adam Overhead
Vincent Chen, Starrick Liu, Regis Cheng, Dance Yang, Shalfun Li, Ryan Yu, Lucy Liang, Hang Su, Roy Gan, Hao Wang, Qian Wang
cs.DC · cs.LG
Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learning workloads. The matrix-aware updates offer a compelling alternative to conventional element-wise optimization, particularly as model architectures continue to grow in scale and heterogeneity. Yet contemporary distributed training infrastructure built around the assumption of element-wise...
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03
RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning
Rongjian Chen, Jianmin Hu, Kejiang Ye, Minxian Xu
cs.DC · cs.LG
Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models learn from ground-truth feedback on mathematical, logical, and scientific tasks. To enable flexible resource allocation and support heterogeneous training setups, modern RLVR systems adopt disaggregated architectures that decouple rollout generation and policy training across independent GPU pools....
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04
Simulating Unified Tensor Resharding in heterogeneous AI systems
Sumit Kumar, Sayantan Dasgupta, Kushal Mitra, Meet Dadhania, Rohan Sudhir Basugade, Praveen Tammana, Satananda...
cs.DC
State-of-the-art AI training simulators assume homogeneous compute and network infrastructure. However, real-world training infrastructure is becoming increasingly heterogeneous since: (a) Model architectures such as multimodal and MoE exploit heterogeneity to improve device utilization, (b) Public cloud platforms often provide limited availability of homogeneous hardware due to fast hardware evolution, and (c) Large enterprises frequently...
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05
Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch
Shaoyu Wang, Yizhuo Liang, Jaeyong Song, Chong Li, Seo Jin Park
cs.DC
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) to hundreds of billions of parameters. Serving a single MoE model requires multiple GPUs operating in parallel, typically through tensor parallelism (TP) or expert parallelism (EP). The optimal choice depends on the number of in-flight requests: TP is faster at low concurrency, whereas EP wins at high concurrency. Production workloads cross this boundary continually:...
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