Systems & Networks · Computer Science & AI
Distributed & Parallel Computing
The newest papers in Distributed & Parallel Computing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers, page 24
Color Complexity of Recolorable Graph Exploration: Upper and Lower Bounds via Block Structure
We study exploration of anonymous, port-free graphs by a single agent with no internal memory.Physically Partitioned KVCache Format for CPU--GPU Load Balancing in MoE Inference
Single-GPU long-context inference with Mixture-of-Experts (MoE) models requires spilling the key-value cache (KVCache) to CPU memory.MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge server, leveraging…OCT-FedSIR: Toward Trustworthy Federated Ophthalmic Learning under Annotation Noise
Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions cannot always be assumed.TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps
Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy risk, network latency, and per-query cost that scale…Affinity-Aware Sharding for Delayed Tensor Parallelism
Delayed Tensor Parallelism (DTP) removes the blocking all-reduce of tensor-parallel Transformer inference.Minibatch persistency, eight years later: what batch reuse costs in steps and joules, and what it saves in data
Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on the same one.Real-World Deployment and Performance Characterisation of Fog-Based Deep Learning for Cold-Chain Temperature Prediction over LoRaWAN
Fresh fruits and vegetables (FFVs) are highly perishable, and cold-chain breaks contribute significantly to global food waste.OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation
Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear attention.HoliBench: A Cross-Platform Benchmarking and Deployment Toolkit for Foundation Models in CPS-IoT Applications
Foundation models, including large language models, vision-language models, and time-series foundation models, are increasingly deployed on embedded and edge platforms for CPS and IoT applications…RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems
AI is beginning to make substantive contributions to LLM inference optimization.4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty.Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning
Federated learning over IEEE~802.11 shares the wireless channel among clients that send model updates.Dissecting GPU Utilization for LLM Inference on Nvidia Hopper
A single SM utilization percentage can make an LLM inference workload look compute-saturated while hiding how much useful work is being done.Adaptive Phase-Switching for Communication-Efficient Federated LoRA Fine-Tuning
Federated fine-tuning of large language models with low-rank adaptation reduces per-client trainable parameters, but client-to-server communication remains the dominant cost.mKernel: Fast Multi-GPU, Multi-Node Fused Kernels
Communication has become a bottleneck in distributed training and inference of large models.BOOST: Concurrent Access to Host Memory and HBM to Accelerate LLM Inference
GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference.EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogeneous sensor modalities, such as vision, text…Phase-Decoupled, Model-Calibrated Power Control for Disaggregated LLM Serving
Datacenter GPU power is the binding constraint on LLM serving capacity, and production serving has shifted to prefill/decode (PD) disaggregation.Building py-kvcache: A Performance Characterization of External KV Caching for vLLM with NVMe SSDs
Prefix caching can reduce the time to first token (TTFT) of long-context LLM requests by reusing previously computed key-value (KV) states, but for short prefixes or fast GPUs, recomputation can be…