EPIC defines a unified abstraction for in-network collectives on Ethernet with polymorphic implementations and modular design to support incremental hardware evolution.
InSC20: International Conference for High Performance Computing, Networking, Storage and Analysis
4 Pith papers cite this work. Polarity classification is still indexing.
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DMA offloads on AMD MI300X GPUs are extended to latency-bound ML communication using untapped hardware features, closing up to 4.5x performance gap versus RCCL in collectives and delivering up to 1.5x lower latency and 1.9x higher throughput in LLM inference over vLLM.
HARP provides a fine-grained inter-operator parallel planner and a heterogeneity-aware 1F1B scheduler that together improve training throughput by 1.3x-1.6x on mixed GPU clusters compared with current homogeneous-oriented frameworks.
CCL-D detects slow/hang anomalies in CCL for distributed training via lightweight tracing probes and an intelligent analyzer, achieving near-complete coverage and 6-minute rank localization on a 4000-GPU cluster over one year.
citing papers explorer
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EPIC: Abstraction and Polymorphism of In-Network Collectives on Ethernet
EPIC defines a unified abstraction for in-network collectives on Ethernet with polymorphic implementations and modular design to support incremental hardware evolution.
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DMA-Latte: Expanding the Reach of DMA Offloads to Latency-bound ML Communication
DMA offloads on AMD MI300X GPUs are extended to latency-bound ML communication using untapped hardware features, closing up to 4.5x performance gap versus RCCL in collectives and delivering up to 1.5x lower latency and 1.9x higher throughput in LLM inference over vLLM.
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HARP: Orchestrating Automated Parallel Training on Heterogeneous GPU Clusters
HARP provides a fine-grained inter-operator parallel planner and a heterogeneity-aware 1F1B scheduler that together improve training throughput by 1.3x-1.6x on mixed GPU clusters compared with current homogeneous-oriented frameworks.
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CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training
CCL-D detects slow/hang anomalies in CCL for distributed training via lightweight tracing probes and an intelligent analyzer, achieving near-complete coverage and 6-minute rank localization on a 4000-GPU cluster over one year.