FlashOverlap: A Lightweight Design for Efficiently Overlapping Communication and Computation

2025-04-28

AI Summary

In multi-GPU training on consumer-grade GPUs, inter-GPU communication bottlenecks severely limit computational efficiency; existing computation-communication overlap strategies fail to simultaneously achieve fine-grained concurrency, zero computational interference, and communication-primitive agnosticism. This paper proposes a lightweight tile-wise signaling mechanism that enables efficient computation-communication overlap solely via standard NCCL APIs—without modifying the underlying communication library or compute kernels. Leveraging dependency-aware scheduling and memory reordering, our approach achieves fine-grained, primitive-agnostic overlap while preserving original compute performance. To the best of our knowledge, this is the first method to support arbitrary NCCL communication primitives with fine-grained overlap without compromising computational throughput. Experimental evaluation on consumer-grade GPUs demonstrates an end-to-end speedup of up to 1.65×, significantly outperforming state-of-the-art overlap techniques.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Agent Communication
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Privacy-enhancing technologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 is 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, a lightweight design characterized by tile-wise overlapping, interference-free computation, and communication agnosticism. FlashOverlap utilizes a novel signaling mechanism to identify tile-wise data dependency without interrupting the computation process, and reorders data to contiguous addresses, enabling communication by simply calling NCCL APIs. Experiments show that such a lightweight design achieves up to 1.65x speedup, outperforming existing works in most cases.

Problem

• Overcoming inter-GPU communication bottlenecks in multi-GPU systems
• Enabling efficient tile-wise overlapping of computation and communication
• Reducing development burden with communication-agnostic design

Innovation

• Tile-wise overlapping maximizes communication-computation overlap
• Interference-free computation preserves original performance
• Communication agnosticism simplifies varying primitive integration