CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems (arxiv.org)

Researchers have introduced a groundbreaking execution paradigm called CTA-pipelining, aimed at optimizing latency in multi-GPU systems. This new method recognizes the need to shift from traditional throughput-driven approaches to latency-sensitive operations, especially as the demand for processing Large Language Models (LLMs) grows. By leveraging Cooperative Thread Array dependencies, CTA-pipelining enables concurrent execution of dependent kernels across GPUs, significantly enhancing performance in shared-memory structures. The significance of CTA-pipelining lies in its demonstrated capability to reduce latency in neural network operations, achieving reductions of up to 31.8% compared to micro-batching and 29.6% compared to Tensor Parallelism (TP) in 2-layer GEMM operations. This technique can be further integrated with TP, creating an orthogonal scaling dimension that allows developers to push the boundaries of latency optimization even further. As the AI/ML community increasingly focuses on latency-bound workloads, CTA-pipelining presents a vital advancement for maximizing the efficiency of multi-GPU systems.