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 6
One Megakernel is Not Enough: Predicting Where to Cut for Streaming Inference
We address a fundamental question: How far should kernel fusion extend and which boundaries are worth preserving?Governing Enterprise AI Traffic at Scale: Design and Operation of a Policy-Driven, Multi-Vendor LLM Gateway on Envoy
Large organizations consume large language models (LLMs) from several vendors at once, each with its own API dialect, credential model, streaming protocol and pricing.WorldPager: Zero-Stall Tiered Memory for Long-Horizon World Models on a Single Consumer GPU
Interactive world models keep the KV of past chunks as retrieval memory so that revisited places stay consistent.Cross-Domain Sequential Recommendation with Representation-Level User Transfer
Cross-domain sequential recommendation aims to improve next-item prediction in a target domain by leveraging user behavior from auxiliary source domains.Making the Telemetry Pipeline Measurable: An Edge IoT Testbed and Dataset for End-to-End Latency, Provenance, and Energy-Aware Benchmarking
Edge-centric IoT systems are increasingly evaluated through sensor values or platform features, yet the telemetry pipeline itself, the sequence of sensing, serialization, message mediation…Free to Look, Free to Reason: Simple LLM Harnesses Outdiagnose Hand-Crafted Root Cause Analysis Agents
Root cause analysis (RCA) of microservice failures has attracted many LLM agents whose investigation is hand-crafted, through standard operating procedures, fixed stages and role agents.Plenty of Room in the Middle: Revealing Opportunities on a GPU Cluster via Fine-Grained Monitoring
GPU clusters have become essential computing resources for AI and scientific research in academia and industry.IOSage: Detecting HPC I/O Bottleneck Patterns to Guide LLM Recommendations
HPC applications often leave much of their parallel file system's bandwidth unused, and turning their Darshan logs into concrete I/O fixes still requires a scarce I/O expert.From Generation to Scoring: Scaling LLM-Based Semantic Labeling at LinkedIn
For efficiency, industrial large language model (LLM) labelers often pack many candidate labels into one prompt and generate a YES/NO answer for each, even when each decision depends only on the…Do Bio-Inspired Algorithms Save Energy in Data Centres? Separating Server Packing from Migration Search in Virtual Machine Consolidation
Energy-efficient virtual machine (VM) consolidation matters as data centre electricity demand rises.From GPU to TPU with Coding Agents: What Works, What Fails, and What It Costs?
A coding agent can migrate a machine learning workload, report success, and still change how the model trains.ScaleRLT: Exact Log-Probabilities and Fast Deferred Rollouts for Reinforcement Learning on Looped Transformers
Looped language models such as Ouro reuse one block of layers several times per token.Speculative Decoding for Mixture-of-Experts Models: Expert Bytes, Kernel Bandwidth and the Best Draft Length
Speculative decoding checks several drafted tokens in one verification pass.NovaFabric: A Black-Box Recorder for AI Agents with Sealed, Replayable, Diffable and Auditable Runs
An AI agent run is no longer a function call.COPE: Composable Performance Emulation for Mixture-of-Experts Offloading Policies
As LLMs scale, mixture-of-experts (MoE) architectures increase model capacity while keeping per-token computation manageable through sparse expert activation.LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference
The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward distributed, memory-intensive…DualPace: Decoupled Scheduling and Cooperative GPU--CPU Decoding for LLM Serving
Large language model (LLM) serving relies on batching to improve inference throughput, but the key-value (KV) cache grows with sequence length and limits GPU-resident concurrency.Replicate What Varies: Resource Sharing for Domain-Randomized Embodied Simulation
Simulation provides a low-cost sandbox where embodied agents can attempt tasks, observe outcomes, and learn from experience.Scaling Graph Query Processing: Focused Exploration on Partitions Using Load Balancing
The focus of this paper is on query processing and not search (approximate or exact).HyScale: Scaling Long-Context Training for Learned Sparse Attention
We present HyScale, a long-context training system for learned sparse attention.