DITRON introduces a hierarchical multi-level tiling compiler for distributed tensor programs that matches or exceeds expert CUDA libraries with 6-30% speedups and has been deployed to improve training MFU by over 10% while saving hundreds of thousands of GPU hours monthly.
13 Msccl++: Rethinking gpu communication abstrac- tions for cutting-edge ai applications
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UCCL-Zip adds lossless compression to GPU communication to reduce LLM bottlenecks while preserving exact numerical correctness.
JANUS disaggregates attention and MoE layers onto separate GPU pools with an expert-balancing scheduler and SLO-aware scaling, delivering up to 4.7x higher per-GPU throughput than prior MoE systems under token-level latency constraints.
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.
UniEP fuses MoE communication and computation into unified MegaKernels with deterministic token ordering, delivering 1.03x-1.38x speedups over prior work while preserving training accuracy.
citing papers explorer
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DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs
DITRON introduces a hierarchical multi-level tiling compiler for distributed tensor programs that matches or exceeds expert CUDA libraries with 6-30% speedups and has been deployed to improve training MFU by over 10% while saving hundreds of thousands of GPU hours monthly.
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UCCL-Zip: Lossless Compression Supercharged GPU Communication
UCCL-Zip adds lossless compression to GPU communication to reduce LLM bottlenecks while preserving exact numerical correctness.
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Janus: Disaggregating Attention and Experts for Scalable MoE Inference
JANUS disaggregates attention and MoE layers onto separate GPU pools with an expert-balancing scheduler and SLO-aware scaling, delivering up to 4.7x higher per-GPU throughput than prior MoE systems under token-level latency constraints.
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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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UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
UniEP fuses MoE communication and computation into unified MegaKernels with deterministic token ordering, delivering 1.03x-1.38x speedups over prior work while preserving training accuracy.