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

• URL: http://arxiv.org/abs/2607.07862v1
• Date: Wed, 08 Jul 2026 18:54:08 GMT
• Title: CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
• Abstract summary: We introduce CTA-pipelining, an execution paradigm designed to exploit shared-memory multi-GPU systems.<n>As a latency-oriented spatial scaling technique, CTA-pipelining leverages the Cooperative Thread Array level.<n>Results show CTA-pipelining reduces latency by up to 31.8% compared to micro-batching.
• Score: 2.870762512009438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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