NVIDIA NCCL 2.28 Enhances AI Workflows by Merging Communication and Computation

The NVIDIA Collective Communications Library (NCCL) plays an important role in managing data exchange across GPUs in AI workflows. The latest release, NCCL 2.28, introduces features that combine communication and computation to enhance efficiency in multi-GPU environments.

TL;DR

• NCCL 2.28 enables GPUs to initiate network communication, reducing latency and CPU load.
• New device APIs allow finer control over collective communication and computation coordination.
• Copy engine collectives overlap data transfer with computation to improve GPU utilization.

Communication-Compute Fusion in NCCL 2.28

Communication-compute fusion integrates data transfer directly with GPU calculations. Previously, these tasks were handled separately, which could lead to delays and inefficient GPU use. NCCL 2.28 allows GPUs to start network operations autonomously, which can reduce idle times and increase throughput.

GPU-Initiated Networking

This feature lets GPUs manage data sending and receiving without CPU intervention. By lowering latency and freeing CPU resources, it benefits AI models that run across multiple GPUs and nodes, where coordination overhead can be significant.

Device APIs for Enhanced Collective Control

NCCL 2.28 provides new device-level APIs that give developers more precise control over collective communication tasks. These APIs help align communication and computation steps more smoothly during distributed training and inference processes.

Copy Engine Collectives and GPU Utilization

Copy engine collectives enable overlapping of data movement with computation. This overlap helps keep GPUs active by ensuring they are engaged in either communication or calculation, which can reduce idle periods during AI model training.

Advantages for Multi-GPU and Multi-Node Systems

Many AI workloads run on multiple GPUs spread across machines. NCCL 2.28’s enhancements aim to reduce communication delays and improve synchronization, which are important for scaling AI workloads effectively in such distributed setups.

Considerations for Using NCCL’s Features

While these new capabilities can boost performance, their benefits depend on the scale and nature of the AI task. For smaller models or fewer GPUs, traditional approaches may be sufficient. The communication-compute fusion features are particularly suited to large-scale, communication-intensive workloads.

Summary of NCCL 2.28’s Impact

NVIDIA NCCL 2.28 introduces mechanisms that merge communication with computation, potentially improving the speed and efficiency of AI training and inference. These features respond to the needs of complex, multi-GPU AI environments.