The aerospace/defense industry often has to solve mission-critical problems as they arise, while also planning and designing for the rigors of future workloads. Recent technological advancements allow aerospace/defense agencies to reap the benefits of AI, but understanding these advancements and the infrastructure requirements for AI training and inference is essential.
The field of machine perception is exploding today, with deep learning and machine learning having the potential to influence mission-critical objectives for aerospace/defense agencies. Once you’re past the first stage of training your neural network with all that data, the next step is to use it to perform useful, predictive tasks with it, such as recognizing images, RF signals, transmission patterns. and more.
Benefits of GPU-accelerated neural net training and AI inference
It can be time consuming to reach and execute the predictive or inference portion of the neural network. Saving as much time as possible during processing will give you a better application experience. Running neural network training and inference on CPUs today no longer provides the performance necessary to ensure an acceptable experience.
These AI workloads have led to new hardware standards that rely heavily on GPU acceleration. GPU-accelerated training and inference of neural networks is now advantageous. With the latest version of NVIDIA’s inference server software, the Triton 2.3, and the Ampere architecture with the introduction of the A100 GPU, it is now easier, faster and more efficient to use GPU acceleration.
When AI workloads are optimized, unparalleled performance and seamless scalability can be achieved. You get the kind of flexibility you don’t find in off-the-shelf solutions.
Tools to handle increased throughput requirements
The aerospace/defense industry collects mountains of data for neural network research, data modeling and training. Recording and distributing all this data can present its own challenges. GPUs can distribute data much faster than CPUs, but this can actually strain I/O bandwidth, leading to higher latency and low bandwidth issues.
It takes a platform that can keep up with these increasing throughput requirements, and it takes several tools to build that platform. Two tools that will help us build this platform are GPUDirect Storage and GPUDirect RDMA. These tools are part of a group of technologies developed by NVIDIA called Magnum IO.
GPUDirect storage essentially eliminates the memory bounce. This is when data is read from storage, copied to system memory and then copied to GPU memory. GPUDirect storage provides a straight path from local storage, or an external storage such as NVMe over Fabric (NVMe-oF), directly to GPU memory, removing the extra reads and writes from system memory and reducing the load on I/ O-bandwidth is reduced.
GPUDirect RDMA provides direct communication between GPUs in remote systems. This eliminates the system CPUs and the required buffer copies of data through system memory, which can result in vastly improved performance.
Remove roadblocks to AI
These kinds of innovations, along with fast network and storage connections, such as InfiniBand HDR with up to 200Gb/s throughput (and the soon-to-be released InfiniBand NDR 400Gb/s), enable a wide range of storage options, including NVMe-oF, RDMA over Converged Ethernet (RoCE), fast Weka storage, and almost everything else available today.
These technologies will also remove the hurdles AI modeling faces today, and Silicon Mechanics can help remove the roadblocks to help you achieve your GPU-accelerated goals.
To learn more about how the aerospace/defense industry can reap the benefits of AI, check out our webinar on demand†