Author

Wei-Gong Zhang

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Open access Aug 2026

Janus: Realizing Practical Operator Parallelism for Latency-Sensitive DNN Inference on GPUs

Janus is proposed, a novel resource- and latency-aware operator scheduling framework to enable efficient and practical operator parallelism for DNN inference on GPUs and introduces an effective stream allocation mechanism that fully incorporates operator resource constraints and latency heterogeneity.

Yi-Feng Zhang, Hao-Xuan Ma, Yu-Xing Long et al. · ACM Transactions on Architec... · 0 citations

Open access Aug 2026

Janus: Realizing Practical Operator Parallelism for Latency-Sensitive DNN Inference on GPUs

With the growing deployment of Deep Neural Networks (DNNs) in latency-critical services, optimizing inference efficiency on GPUs has become crucial. While exploiting operator parallelism offers a promising avenue to accelerate inference and improve hardware utilization, existing approaches often overlook two critical f...

Yifeng Zhang, Haoxuan Ma, Yuxing Long et al. · ACM Transactions on Architec... · 0 citations