{"total":32,"rows":[{"id":"2406.06858","title":"FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion","created":"2024-06-11","cited_by_count":57,"area":null},{"id":"2308.00852","title":"CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters","created":"2023-08-01","cited_by_count":26,"area":null},{"id":"2401.16677","title":"T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives","created":"2024-01-30","cited_by_count":16,"area":null},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2504.09014","title":"MSCCL++: Rethinking GPU Communication Abstractions for AI Inference","created":"2025-04-11","cited_by_count":10,"area":null},{"id":"2409.15241","title":"Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping","created":"2024-09-23","cited_by_count":9,"area":null},{"id":"2401.09356","title":"Swing: Short-cutting Rings for Higher Bandwidth Allreduce","created":"2024-01-17","cited_by_count":8,"area":null},{"id":"2202.03356","title":"Efficient Direct-Connect Topologies for Collective Communications","created":"2022-02-07","cited_by_count":5,"area":null},{"id":"2509.10371","title":"Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective","created":"2025-09-12","cited_by_count":5,"area":null},{"id":"2304.05301","title":"TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2406.04594","title":"Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization","created":"2024-06-07","cited_by_count":4,"area":null},{"id":"2510.20171","title":"Collective Communication for 100k+ GPUs","created":"2025-10-23","cited_by_count":4,"area":null},{"id":"2512.25059","title":"Reliable and Resilient Collective Communication Library for LLM Training and Serving","created":"2025-12-31","cited_by_count":4,"area":null},{"id":"2403.07585","title":"Communication Optimization for Distributed Training: Architecture, Advances, and Opportunities","created":"2024-03-12","cited_by_count":3,"area":null},{"id":"2504.20854","title":"Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning","created":"2025-04-29","cited_by_count":3,"area":null},{"id":"2510.08755","title":"Robust Heuristic Algorithm Design with LLMs","created":"2025-10-09","cited_by_count":3,"area":null},{"id":"2402.06787","title":"ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics","created":"2024-02-09","cited_by_count":2,"area":null},{"id":"2305.18461","title":"Bandwidth Optimal Pipeline Schedule for Collective Communication","created":"2023-05-29","cited_by_count":1,"area":null},{"id":"2310.20209","title":"Network Contention-Aware Cluster Scheduling with Reinforcement Learning","created":"2023-10-31","cited_by_count":1,"area":null},{"id":"2505.23523","title":"Efficient AllReduce with Stragglers","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2402.09589","title":"MLCC: A Congestion Control Technique to Accelerate ML Training","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2503.24230","title":"GPU-centric Communication Schemes for HPC and ML Applications","created":"2025-03-31","cited_by_count":0,"area":null},{"id":"2505.23105","title":"LUMION: Fast Fault Recovery for ML Jobs Using Programmable Optical Fabrics","created":"2025-05-29","cited_by_count":0,"area":null},{"id":"2508.03674","title":"Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML","created":"2025-07-20","cited_by_count":0,"area":null},{"id":"2510.19322","title":"Enabling Reconfiguration-Communication Overlap for Collective Communication in Optical Networks","created":"2025-10-22","cited_by_count":0,"area":null},{"id":"2604.24073","title":"FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.31000","title":"HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.01680","title":"Don't Let a Few Network Failures Slow the Entire AllReduce","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2608.26453","title":"Distributed Training using an Intelligent Network","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.04417","title":"Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.13585","title":"mKernel: Fast Multi-GPU, Multi-Node Fused Kernels","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.36954","title":"Purlin: Separating Orchestration from the Datapath of Collectives","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}