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Tensor Compiler Engineering: Designing, Building, and Optimizing High-Performance Compilation Systems for Modern AI and Machine Learning Workloads
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Publisher: Independently Published
Pages: 234
Weight: 0.84
Height: 0.49 Width: 6.69 Depth: 9.61
Language: English
Tensor Compiler Engineering is your definitive guide to understanding exactly how machine learning models execute on physical hardware. I wrote this book to completely open the black box of AI infrastructure. It breaks down the complete compilation pipeline-from structuring mathematical computation graphs to generating highly optimized binary files-showing you exactly how to squeeze every drop of performance out of your deployment environment.
Imagine you just spent weeks training a massive neural network. The mathematics are perfect. But when you push it to a production server, the latency is terrible, and the system crashes from unexpected Out-of-Memory errors.
I have been exactly where you are.
For a long time, I struggled to understand why perfectly written code ran so slowly. Then I discovered the truth: the bottleneck was not the math. The bottleneck was how the math was being delivered to the physical processor. I realized that mastering the compiler was the only way to break through this invisible barrier. I spent countless hours mapping out exactly how these systems work, step-by-step, tearing down everything from memory caching to hardware threading. I structured this book so you never have to guess what is happening under the hood of your AI deployments ever again.
What's inside- Computation Graph Optimization: Master operator fusion and Multi-Level Intermediate Representation (MLIR) to streamline your math.
- Hardware Acceleration: Discover how to manipulate hardware execution using intelligent loop tiling and vector unrolling.
- Silicon Real Estate: Learn static versus dynamic memory allocation and how to aggressively recycle physical VRAM buffers.
- Backend Code Generation: See exactly how compilers target and write CUDA, ROCm, and bare-metal CPU assembly code.
- Production Integration: Seamlessly handle dynamic tensor shapes and debug numerical errors on live servers without crashing.
This book is written for machine learning engineers, systems developers, and software architects who want to stop relying on bloated software frameworks and start understanding physical execution. If you know how to build a neural network but want to learn how to engineer it to run efficiently on bare silicon, this guide is built for you.
The next generation of artificial intelligence belongs to the engineers who control the infrastructure. Do not let hardware limitations choke your software. Grab your copy today and take absolute control of your execution pipeline.
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