# Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering

arXiv: https://arxiv.org/abs/2504.19519 · HTML: https://mlsanity.com/paper/2504.19519/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [NanoFlow: Towards Optimal Large Language Model Serving Throughput](https://mlsanity.com/paper/2408.12757/) (2024)
- [FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion](https://mlsanity.com/paper/2406.06858/) (2024)
- [xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism](https://mlsanity.com/paper/2411.01738/) (2024)
- [Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts](https://mlsanity.com/paper/2502.19811/) (2025)
- [TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives](https://mlsanity.com/paper/2503.20313/) (2025)
- [Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU](https://mlsanity.com/paper/2301.03598/) (2023)
- [Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping](https://mlsanity.com/paper/2404.19429/) (2024)
- [T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives](https://mlsanity.com/paper/2401.16677/) (2024)
- [Optimizing Distributed ML Communication with Fused Computation-Collective Operations](https://mlsanity.com/paper/2305.06942/) (2023)
- [Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping](https://mlsanity.com/paper/2409.15241/) (2024)

## Built on by (most cited first, 10 of 10)

- [LLM-42: Enabling Determinism in LLM Inference with Verified Speculation](https://mlsanity.com/paper/2601.17768/) (2026)
- [CUCo: An Agentic Framework for Compute and Communication Co-design](https://mlsanity.com/paper/2603.02376/) (2026)
- [Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap](https://mlsanity.com/paper/2512.10236/) (2025)
- [Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts](https://mlsanity.com/paper/2607.19539/) (2026)
- [MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems](https://mlsanity.com/paper/2605.05888/) (2026)
- [EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization](https://mlsanity.com/paper/2605.14249/) (2026)
- [Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads](https://mlsanity.com/paper/2606.09200/) (2026)
- [X-Stage: Modeling Post-Issue Backpressure in GPU Communication--Computation Fusion](https://mlsanity.com/paper/2607.23264/) (2026)
- [CIERA: Cross-Iteration Exponent Reuse for Lossless Allgather in Sharded MoE Training](https://mlsanity.com/paper/2609.04609/) (2026)
- [T-CCL: Resource Efficient and Performant Collective Communication using Tensor Memory Accelerator](https://mlsanity.com/paper/2610.07098/) (2026)

## Abstract (arXiv)

Generative models have achieved remarkable success across various applications, driving the demand for multi-GPU computing. Inter-GPU communication becomes a bottleneck in multi-GPU computing systems, particularly on consumer-grade GPUs. By exploiting concurrent hardware execution, overlapping computation and communication latency becomes an effective technique for mitigating the communication overhead. We identify that an efficient and adaptable overlapping design should satisfy (1) tile-wise overlapping to maximize the overlapping opportunity, (2) interference-free computation to maintain the original computational performance, and (3) communication agnosticism to reduce the development burden against varying communication primitives. Nevertheless, current designs fail to simultaneously optimize for all of those features. To address the issue, we propose FlashOverlap, which utilizes a novel signaling mechanism: when part of the output finishes, the computation kernel sends a signal to trigger the communication of that part, while continuing the computation of the remaining part (interference-free computation). Consequently, the communication of the finished part and the computation of the remaining part can be overlapped. On top of the signaling mechanism, FlashOverlap comprises two key components: (1) the determination of the signaling timing to boost the overlap efficiency (tile-wise overlapping), and (2) a pre-communication reordering to create the contiguous address for finished data, enabling communication by simply calling NCCL APIs (communication agnosticism), and a post-communication reordering to correct the data order. Experiments show that FlashOverlap achieves up to 1.65x speedup through overlap, outperforming existing works in most cases. Code is available at https://github.com/infinigence/FlashOverlap.