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Distributed & Parallel Computing
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507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers
KOSMOS: Self-scheduling Persistent Kernels for Communication-Compute Overlap
Tensor and sequence parallelism (TP+SP) and expert parallelism (EP) put collectives on the critical path of transformer layers.ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4
Attention is a core building block of frontier models and often dominates compute-bound prefill at long context.ZeroRAG: Adaptive Semantic Peer Routing for Decentralized Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM…SEER: A Scalable Event-Driven Runtime for Parallel Execution of Multi-Agent Reinforcement Learning Environments
Multi-agent reinforcement learning (MARL) is increasingly used in systems where many entities interact in a shared environment.GINGER: Spicing Up Graph-Based Vector Search
We present \textsc{Ginger}, a state-of-the-art open-source C++ library for graph-based approximate nearest neighbor search (ANNS).Measured Coupling for Parallel Decoding in Diffusion Language Models
Diffusion language models can write many tokens per step, but must choose which tokens are safe to commit together.StrataFlux: Memory-Hierarchy-Native Low-Bit LLM Inference
Quantization reduces the memory and bandwidth demands of large language model (LLM) inference but can degrade accuracy; offloading higher-precision data can instead stall generation.CHARM+: Cross-Hardware Attention with Re-Merge Multistream Mechanism
As sequence lengths continue to grow, distributed attention based on sequence parallel (e.g., Ring-Attention) has become a mainstream solution for large language models (LLMs).A Controlled Experimental Evaluation of Configuration and Cache Architecture in LLM Serving Engines
Multi-agent LLM applications repeat most of their prompt tokens across requests, and serving engines reuse the cached state of shared prefixes. vLLM and SGLang reuse it at different granularities…Shallow Encoding Emits Deep Memories
Hybrid language models interleave a few softmax-attention layers with many linear-attention or state-space layers, so every layer keeps a memory of earlier tokens: a key-value cache or a fixed-size…Constant-Round Massively Parallel Algorithms for Approximate MSTs in Metric Spaces
We develop a unified algorithmic framework for computing approximate minimum spanning trees (MSTs) in metric spaces in the fully scalable Massively Parallel Computation (MPC) model.Ab Initio Accuracy Beyond the Cutoff: Distributed Message-Passing Equivariant Attention at Tens-of-Millions-of-Atom Scale
Molecular dynamics predicts molecular motion from atomic forces and trajectory integration.Efficient Stable Word-RAM Simulation by Population Protocols
Population protocols model computation by indistinguishable agents interacting in uniformly random pairs, with unary inputs and outputs encoded by state counts.Chiron: Cache Management Empowered by LLM Intelligence for AI Workloads
AI clusters keep data in remote object stores and rely on local caches to stop GPUs from waiting for data.Performance Portability of Out-of-Kernel Optimizations on GPUs: A Molecular Docking Case Study
Achieving performance portability is challenging for irregular scientific applications built as repeated multi-kernel GPU pipelines, because throughput depends on out-of-kernel decisions as much as…Corralling AI Risks with Grassroots Contracts
AI's inflection point is a fountain of alarming risks.Hardwired Distributed Processing Units for Inference: Neural Decisions at the Sensor that Free the Host
We show that a hardwired distributed processing unit (HDPU) can take a neural decision at the sensor and so remove the inference from the host.Distributed Consensus, Optimization, and Learning under Communication Constraints
Many modern engineering applications require distributed problem-solving in which multiple nodes in a network cooperate to achieve a common optimization objective.Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism
Reinforcement learning post-training improves the capabilities of large language models, but generating training responses is a major computational bottleneck.The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record
Enterprises run business transactions through multi-agent AI systems spanning clouds, large language models (LLMs), orchestration harnesses, and systems of record.