CUDA Systems Engineering: Bare-Metal Kernels, Memory Hierarchies, and Enterprise GPU Optimization

By Elmer Robinson and Denton Malcom

Stop leaving teraflops on the table. Engineer bare-metal kernels and deploy high-throughput GPU infrastructure at enterprise scale.

Writing CUDA code that successfully compiles is a baseline skill. Writing CUDA code that commands modern NVIDIA architectures and scaling it across multi-tenant enterprise clusters is a hardcore systems engineering discipline. When infrastructure compute costs millions, uncoalesced memory reads, warp divergence, and inefficient host-to-device transfers are catastrophic failures of design.

CUDA Systems Engineering is the definitive operational manual for infrastructure architects and bare-metal programmers. We bypass the introductory tutorials and dive straight into the brutal realities of the GPU memory wall, instruction-level parallelism, and large-scale deployment.

You will learn to tear down high-level abstractions and control the hardware at the atomic level. From orchestrating data through the Hopper Tensor Memory Accelerator (TMA) using raw PTX to hard-partitioning multi-tenant workloads via Multi-Instance GPU (MIG), this playbook gives you the power to write and deploy code that executes at the theoretical limit of the silicon.

Inside this manual, you will execute:
Bare-Metal Kernel Optimization: Mastering the nvcc pipeline, PTX as a virtual ISA, and SASS profiling to keep execution pipelines completely saturated.

Defeating the Memory Wall: Forcing perfect memory coalescing, eliminating distributed shared memory bank conflicts, and leveraging the TMA engine for bulk asynchronous transfers.

Lock-Free GPU Architecture: Implementing device-side queues, warp-level atomics, and cooperative groups to bypass host serialization constraints.

Tensor Core Weaponization: Exploiting mixed-precision arithmetic, FP8 pipelines, and MMA instructions to push matrix workloads to maximum throughput.

Enterprise Infrastructure Deployment: Scaling your optimized kernels into production using MIG slicing, NVLink peer-to-peer DMA, and Kubernetes container passthrough.

Who is this for?

This manual is built exclusively for HPC Engineers, AI Infrastructure Architects, Low-Latency Systems Programmers, and Technical Leads building mission-critical computing clusters. If your software runs on enterprise hardware and every wasted clock cycle is a massive financial leak, this is your blueprint for survival.
Stop treating the GPU like a black box. Grab your copy, saturate your pipelines, and dominate the hardware today.

• Format: Paperback
• Language: English
• ISBN: B0H9NQV34M
• ISBN13: 9798188212711
• Release Date: July 2026
• Publisher: Independently Published
• Length: 196 Pages
• Weight: 0.71 lbs.
• Dimensions: 0.4" x 6.7" x 9.6"