Accelerate AI Workloads With Optimized GPU-to-GPU Communication

Graphic processing units (GPU) deliver the processing power that AI workloads demand, but they can be throttled if other system components are not finely tuned. One common bottleneck is GPU-to-GPU communication. Without fast data exchange, the performance of multi-GPU systems can be severely limited by communication overhead. 

GPU clusters commonly use MPI communications, which enable direct communication from GPU memory. While traditional MPI involved data transfer through the central processing unit (CPU), GPU-aware MPI allows data to be sent directly from the GPU’s memory to other GPUs or network interfaces. The result is significantly better performance and simpler code for GPU-accelerated applications.

However, MPI communications in GPU clusters can be extremely slow if not optimized, leading to poor GPU utilization and performance degradation in multi-GPU systems. It is crucial to optimize MPI communications to maximize the performance of AI workloads, particularly when scaling to multiple nodes with many GPUs.

How Inefficient Communication Impacts GPU Performance

Historically, communication between GPUs relied on routing data through the system’s CPU, a slow, indirect path that creates a significant bottleneck. Modern interconnect technologies link GPUs together to act as a single, more powerful entity. This enables them to share resources and memory, which is crucial for tackling massive workloads that exceed the memory capacity of a single GPU.

However, all communication, especially between different compute nodes, involves network latency. Unlike the sub-microsecond speeds of GPU memory access, network latency can be measured in microseconds — the equivalent of thousands of CPU operations. If communication between them is slow, GPUs may finish their assigned task and sit idle, waiting for data from other GPUs. This results in poor scaling efficiency and wasted compute resources. 

Unoptimized MPI communication can be the source of a major performance bottleneck. If adding more GPUs does not provide a proportional performance boost, inefficient communication is the likely culprit.

Minimizing Communication Bottlenecks

There are several ways organizations can minimize communication bottlenecks to enable near-linear performance scaling as more GPUs are added. The key is to optimize both the flow of data and the control mechanisms that manage these transfers. By addressing both the control and data pipelines, developers can reduce latency, increase throughput and better overlap communication with computation.

One technique is to avoid the operating system’s network stack using remote direct memory access (RDMA). RDMA allows the network interface card (NIC) to access memory directly from another node, bypassing the host CPU and operating system. RDMA is a core technology for high-performance interconnects such as InfiniBand and RoCE (RDMA over converged Ethernet).

Another option is to utilize the UCX (unified communication X) communication framework. UCX leverages network hardware capabilities such as RDMA and hardware tag matching to offload communication operations from the CPU. In addition, UCX dynamically selects the most efficient transport layer and employs techniques such as memory registration and bounce buffers to efficiently manage memory for communication.

Leveraging Low-Latency Interconnects

Organizations can also use high-speed, low-latency interconnects to move massive datasets between nodes quickly. InfiniBand and NVIDIA’s Quantum-2 are common choices, as they offer much higher bandwidth and lower latency than standard Ethernet.

A newer technology is SuperNIC, which is a class of network accelerator designed specifically to supercharge AI workloads by accelerating GPU-to-GPU communication. NVIDIA introduced the technology in 2023 with the BlueField-3 SuperNIC, and further developed it with the ConnectX-8 SuperNIC in 2024. SuperNICs can achieve speeds up to 400Gbs using RoCE technology.

Wesco Solutions

Wesco can help organizations architect AI solutions that minimize communication latency and maximize GPU utilization. Our team has expertise across the entire AI lifecycle and can help you select the right techniques and technology to improve control and data pipelines for MPI node-to-node communications. We can provide solutions for AI clusters including turnkey infrastructure from advisory and design to fully assembled, tested and shipped rack systems, with structured cabling and thermal optimized cooling. You’ll gain the foundation you need to support the most dynamic, GPU-intensive workloads and scale to meet growing AI demands.