[2607.07862] CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2607.07862 (cs)

[Submitted on 8 Jul 2026]

Title:CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems

Authors:Tingkai Liu, Muralidhar Andoorveedu, Sanjoy Das, Sanjay Patel, Volodymyr Kindratenko<br>View a PDF of the paper titled CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems, by Tingkai Liu and 4 other authors

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Abstract:The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures. However, current software still mostly treats these coherent interconnects simply as high-speed networks. Simultaneously, the demand for serving Large Language Models under latency constraints has shifted GPU workload optimization from being throughput-driven to latency-bound, necessitating latency-oriented scaling methods beyond Tensor Parallelism (TP).

Thus, we introduce CTA-pipelining, an execution paradigm designed to exploit shared-memory multi-GPU systems. As a latency-oriented spatial scaling technique, CTA-pipelining leverages dependencies at the Cooperative Thread Array level, enabling concurrent execution of dependent kernels across GPUs. We demonstrate its capability using CUTLASS, cuBLAS, and NCCL libraries on 8-GPU H200 and B200 systems. Results show on 2-layer GEMM, representing the MLP operation, CTA-pipelining reduces latency by up to 31.8% compared to micro-batching, and 29.6% compared to TP. It can also be combined with TP as an orthogonal scaling dimension to further push the latency boundary.

Subjects:

Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

Cite as:<br>arXiv:2607.07862 [cs.DC]

(or<br>arXiv:2607.07862v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2607.07862

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arXiv-issued DOI via DataCite

Submission history<br>From: Tingkai Liu [view email]<br>[v1]<br>Wed, 8 Jul 2026 18:54:08 UTC (1,598 KB)

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