# FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

arXiv: https://arxiv.org/abs/2406.06858 · HTML: https://mlsanity.com/paper/2406.06858/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning](https://mlsanity.com/paper/1705.07878/) (2017)
- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://mlsanity.com/paper/2104.07857/) (2021)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2109.13226/) (2021)
- [PipeMare: Asynchronous Pipeline Parallel DNN Training](https://mlsanity.com/paper/1910.05124/) (2019)
- [ZeRO++: Extremely Efficient Collective Communication for Giant Model Training](https://mlsanity.com/paper/2306.10209/) (2023)
- [Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads](https://mlsanity.com/paper/2105.05720/) (2021)
- [TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches](https://mlsanity.com/paper/2111.04867/) (2021)
- [Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms](https://mlsanity.com/paper/2007.00156/) (2020)
- [Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU](https://mlsanity.com/paper/2301.03598/) (2023)

## Built on by (most cited first, 30 of 57)

- [Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding](https://mlsanity.com/paper/2507.19427/) (2025)
- [Is Artificial Intelligence Generated Image Detection a Solved Problem?](https://mlsanity.com/paper/2505.12335/) (2025)
- [Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts](https://mlsanity.com/paper/2502.19811/) (2025)
- [POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference](https://mlsanity.com/paper/2410.18038/) (2024)
- [Optimizing RLHF Training for Large Language Models with Stage Fusion](https://mlsanity.com/paper/2409.13221/) (2024)
- [MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism](https://mlsanity.com/paper/2504.02263/) (2025)
- [MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production](https://mlsanity.com/paper/2505.11432/) (2025)
- [VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo](https://mlsanity.com/paper/2508.02317/) (2025)
- [Scalable Training of Mixture-of-Experts Models with Megatron Core](https://mlsanity.com/paper/2603.07685/) (2026)
- [FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models](https://mlsanity.com/paper/2501.10714/) (2025)
- [Robust LLM Training Infrastructure at ByteDance](https://mlsanity.com/paper/2509.16293/) (2025)
- [Seesaw: High-throughput LLM Inference via Model Re-sharding](https://mlsanity.com/paper/2503.06433/) (2025)
- [TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference](https://mlsanity.com/paper/2505.11329/) (2025)
- [Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering](https://mlsanity.com/paper/2504.19519/) (2025)
- [Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping](https://mlsanity.com/paper/2409.15241/) (2024)
- [Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping](https://mlsanity.com/paper/2501.06589/) (2025)
- [PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM Serving](https://mlsanity.com/paper/2501.08192/) (2025)
- [Reconstructing KV Caches with Cross-layer Fusion For Enhanced Transformers](https://mlsanity.com/paper/2512.03870/) (2025)
- [UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training](https://mlsanity.com/paper/2604.19241/) (2026)
- [Collective Communication for 100k+ GPUs](https://mlsanity.com/paper/2510.20171/) (2025)
- [ISO: Overlap of Computation and Communication within Seqenence For LLM Inference](https://mlsanity.com/paper/2409.11155/) (2024)
- [Leveraging Large Language Models For Scalable Vector Graphics Processing: A Review](https://mlsanity.com/paper/2503.04983/) (2025)
- [Constructing a 3D Scene from a Single Image](https://mlsanity.com/paper/2505.15765/) (2025)
- [Self-Evolving 3D Scene Generation from a Single Image](https://mlsanity.com/paper/2512.08905/) (2025)
- [Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training](https://mlsanity.com/paper/2601.17654/) (2026)
- [ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning](https://mlsanity.com/paper/2605.08639/) (2026)
- [Efficient LLM Serving on Hybrid Real-time and Best-effort Requests](https://mlsanity.com/paper/2504.09590/) (2025)
- [SwiftFusion: Scalable Sequence Parallelism for Distributed Inference of Diffusion Transformers on GPUs](https://mlsanity.com/paper/2601.20273/) (2026)
- [CUCo: An Agentic Framework for Compute and Communication Co-design](https://mlsanity.com/paper/2603.02376/) (2026)
- [UCCL-Zip: Lossless Compression Supercharged GPU Communication](https://mlsanity.com/paper/2604.17172/) (2026)

## Abstract (arXiv)

Large deep learning models have demonstrated strong ability to solve many tasks across a wide range of applications. Those large models typically require training and inference to be distributed. Tensor parallelism is a common technique partitioning computation of an operation or layer across devices to overcome the memory capacity limitation of a single processor, and/or to accelerate computation to meet a certain latency requirement. However, this kind of parallelism introduces additional communication that might contribute a significant portion of overall runtime. Thus limits scalability of this technique within a group of devices with high speed interconnects, such as GPUs with NVLinks in a node. This paper proposes a novel method, Flux, to significantly hide communication latencies with dependent computations for GPUs. Flux over-decomposes communication and computation operations into much finer-grained operations and further fuses them into a larger kernel to effectively hide communication without compromising kernel efficiency. Flux can potentially overlap up to 96% of communication given a fused kernel. Overall, it can achieve up to 1.24x speedups for training over Megatron-LM on a cluster of 128 GPUs with various GPU generations and interconnects, and up to 1.66x and 1.30x speedups for prefill and decoding inference over vLLM on a cluster with 8 GPUs with various GPU generations and interconnects.