AI Workload Acceleration with DOCA Congestion Control

Modern AI workloads demand massive data throughput and ultra-low latency, making efficient network traffic management essential. When network congestion occurs, AI training and inference tasks slow down significantly. This text-based course guides you through the foundational concepts of Remote Direct Memory Access (RDMA) and teaches you how to programmatically prevent network bottlenecks. You will transition from understanding basic traffic management to writing custom congestion control algorithms that keep your infrastructure running at peak performance. What you'll learn: - Understand the core principles of RDMA, RoCE, and network congestion in high-performance computing. - Explore the architecture of DOCA and how it interfaces with network hardware. - Configure the DOCA PCC (Programmable Congestion Control) SDK for custom algorithm development. - Design and implement proactive congestion control algorithms to optimize data flow. - Analyze network telemetry data to identify and mitigate bottlenecks in real time. - Apply modern best practices for testing and validating network algorithms under simulated AI workloads. This course begins with a thorough introduction to high-speed networking terminology, hardware-offloading concepts, and the fundamentals of congestion. From there, you will progress through written step-by-step guides that show you how to write, compile, and deploy your custom algorithms directly to the network interface card. This course is designed for network engineers, system administrators, and infrastructure developers who are new to DOCA and want to optimize high-performance computing environments. No prior experience with hardware programming is required, though a basic understanding of C programming and networking concepts is helpful. Start reading today to unlock the full potential of your high-performance network fabric.