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 15
WakeKV: Reactive, Reversible KV Residency for Heads That Change Their Minds
Most KV-cache compression methods classify attention heads once, either offline or during prefill, and keep this classification fixed throughout generation.Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
Robotic guide dogs should understand their surroundings, objects, and potential risks.D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?
GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal how the gap relates to design discovery and…EdgeAgent: Orchestrating On-Device LLM inference for End-User Multi-Agent Systems on CPU-GPU Unified Memory Architectures
Emerging multi-agent LLMs demand privacy-preserving edge deployment, yet current inference systems struggle with these collaborative workflows.A Path Integral Surrogate for Multi-Step Gradient Inversion in Federated Learning
Federated learning lets many clients train a shared model together without ever sending their private data to a central server.Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU
Deep neural networks continue to grow in parameter count, driving up training and inference cost on GPUs.HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols
Large language models (LLMs) have recently been applied in systems research as a tool to reduce human-intensive engineering effort through cost-efficient automation.The Other Half of Workflow Portability: Evidence-Backed HPC Site Profiles with Agentic Discovery
Moving a workflow developed and tested at one HPC site to another rarely succeeds without some amount of trial and error.Towards a Cloud Fog Edge System for Smart Building
In this article, we present our vision and recent advancements toward creating a decentralized system capable of learning from real-time data within buildings to support sustainable and…From Alert Floods to Precedence Forests: Zero-Prior-Knowledge Incident Triage with LOGOS
Commercial observability platforms rely on domain artifacts like distributed traces, topology maps, and baseline metrics.Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning
Massively parallel GPU simulators train multi-robot policies in thousands of environments, and many fleets use private Fifth-Generation (5G) networks, where each robot's delay depends on its…Compound AI System Reliability: A Failure Taxonomy and Resilience Pattern Catalog from 150 Production Incidents
Deploying compound AI systems reliably and safely requires understanding failure modes that emerge at component boundaries, not within individual models.Cascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure
We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources.Argus: A Real-EKS Study of When Predicting Spot Interruptions Beats Simple Checkpointing
Elastic Compute Cloud (EC2) Spot is 60% to 90% cheaper than On-Demand but can be reclaimed on just a 2-minute notice; for expensive multi-node training this loss can be severe, with one reclaim…Characterizing High Bandwidth Flash for LLM Serving
Large language model (LLM) serving requires substantial memory to store model weights and KV caches.Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning
Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server.An Island-Based Parallel Biased Random-Key Genetic Algorithm for the Three-Dimensional Trailer Loading Problem
The Three-Dimensional Trailer Loading Problem (3D-TLP) involves determining the optimal placement and orientation of heterogeneous items within the confined space of a trailer while maximizing…Taming Speculative Search for Test-Time Scaling in LLM Serving
Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, substantially enhancing accuracy on challenging tasks…HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training
As model sizes continue to scale, distributed training has become inevitable.Robustifying Asynchronous SGD via Soft Throttling
Asynchronous SGD is a popular algorithm for distributed learning where each client's gradient update is applied on arrival.