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Automatic Horizontal Fusion for GPU Kernels

arXiv 2007.01277 ↗First posted 2 Jul 2020cs.DC cs.PLcited by 17

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Abstract (arXiv)

We present automatic horizontal fusion, a novel optimization technique that complements the standard kernel fusion techniques for GPU programs. Unlike the standard fusion, whose goal is to eliminate intermediate data round trips, our horizontal fusion technique aims to increase the thread-level parallelism to hide instruction latencies. We also present HFuse, a new source to source CUDA compiler that implements automatic horizontal fusion. Our experimental results show that horizontal fusion can speed up the running time by 2.5%-60.8%. Our results reveal that the horizontal fusion is especially beneficial for fusing kernels with instructions that require different kinds of GPU resources (e.g., a memory-intensive kernel and a compute-intensive kernel).

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2020.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 4; 2025: 2; 2026: 4.

17 papers.

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  1. 2024NanoFlow: Towards Optimal Large Language Model Serving Throughputcited by 85
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  10. 2021Collage: Seamless Integration of Deep Learning Backends with Automatic Placementcited by 2
  11. 2023Minuet: Accelerating 3D Sparse Convolutions on GPUscited by 2
  12. 2024Kernel Looping: Eliminating Synchronization Boundaries for Peak Inference Performancecited by 1
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  3. 2022ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizationscited by 0
  4. 2026CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systemscited by 0
  5. 2026KerColle: Unlocking Fine-Grained GPU Concurrency in Vision-Language-Action Modelscited by 0