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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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Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning
How long should each client train before communicating?ETH: Data-Balanced Heterogeneous Pipeline Parallelism for Training Large Models
Current heterogeneity-aware training systems for large models balance workloads by assigning unequal numbers of layers across pipeline stages, a strategy aimed at equalizing execution time through a…Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs
Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in.TRACE: Transformer Residual Activation Codebook Encoding for Communication-efficient Distributed Edge Inference
Distributed Transformer inference across co-located edge devices is fundamentally communication-bound, as exchanging activations between sequence-parallel partitions dominates inference latency…Fast Distributed $(Δ+ 1)$-Edge Coloring in Bipartite Graphs
We show that, on bipartite $n$-vertex graphs in the LOCAL model of distributed computation, $(Δ+ 1)$-edge coloring can be solved deterministically in $O(f(Δ) \log n)$ rounds for some function $f$.LLM-Attack Suite: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
Split language models keep input text on a client but send intermediate representations to a server.Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off
Large Language Model (LLM) serving systems must balance latency and throughput while efficiently utilizing distributed GPU resources.Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training
Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding…Dynamic 3-Coloring of Bipartite Graphs with Optimal Locality
We give a dynamic graph algorithm which maintains a 3-coloring in bipartite graphs under edge insertions such that the coloring only changes within a radius of $O(\log n)$ around an inserted edge.Computational compressive imaging reconstruction with a simulated-annealing-based Ising solver
Computational compressive imaging, such as single-pixel imaging and lensless imaging, is a promising imaging technique, enabling the reconstruction of objects from under-sampled measurements through…Delta Tokenizer: Reference-Identical Tokenization of Session Deltas and Segment-Parallelism for Long Prompts on Multicore CPUs
An agent that drives an LLM sends the model the same conversation again and again, and each turn adds only a little text at the end.JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs
LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weights.ATTUNE: Agentic Throughput Tuning for LLM Training
Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning again.DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss
Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit.A Minimum Exact Disjunctive Basis for Two-Robot Solvability Profiles
We completely characterize the solvability profiles of tasks for two autonomous mobile robots in the plane under variable disorientation, chirality, and rigid movements.No Truly Sublogarithmic-Round Algorithm for Distributed MIS in the LOCAL Model
We prove that $o(\frac{\log n}{\log\log n})$-round randomized LOCAL algorithms find a maximal independent set (MIS) with subconstant probability on some input.LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations.FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving
Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production.Federated Unlearning of Quantum Data via Classical Shadows
Quantum federated learning trains a quantum neural network (QNN) across nodes that hold quantum data, such as the states of quantum sensors.Federated Coreset Subsampling for Distributed Memory Banks
In real-world scenarios, industrial processes can span multiple factories and goods are often produced at multiple sites, leading to a shared yet non-identical distribution of data across locations.