Chapters

This book presents a hands-on, practical guide to implementing tensor operations, optimizations, and real-world applications in C. Each chapter builds upon the previous one, taking you from basic tensor structures to high-performance neural networks and embedded deployments.

Chapter 1: Setting Up Your Tensor Programming Toolkit

Start your journey by building a solid foundation for tensor programming in C. You'll set up a development environment, implement a basic tensor structure, and learn essential debugging techniques.
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Chapter 2: Implementing Core Tensor Operations from Scratch

Build the fundamental operations that form the backbone of any tensor library. This chapter focuses on creating efficient implementations without external dependencies.
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Chapter 3: Mastering Memory Layouts for Speed

Dive into the critical relationship between memory layout and performance. Learn how proper data organization can dramatically speed up tensor operations.
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Chapter 4: Parallelizing Tensor Workloads with OpenMP

Harness the power of multi-core processors to accelerate tensor operations. This chapter shows you how to safely parallelize your code for maximum performance.
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Chapter 5: Vectorizing Code with SIMD Intrinsics

Unlock the full potential of modern CPUs by leveraging SIMD instructions. Learn to use vector units for dramatic performance improvements in tensor operations.
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Chapter 6: Integrating BLAS for Production-Grade Performance

Connect your tensor library to battle-tested, highly optimized BLAS implementations. Learn when and how to leverage external libraries for maximum performance.
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Chapter 7: Debugging Memory Corruption in Tensor Programs

Tackle the most challenging bugs in tensor programming - memory corruption and leaks. This chapter equips you with powerful tools and techniques to find and fix these issues.
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Chapter 8: Profiling and Optimizing Hotspots

Identify and eliminate performance bottlenecks in your tensor code. Learn systematic approaches to profiling and optimization that yield substantial speedups.
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Chapter 9: Building a Neural Network Layer with Tensors

Apply your tensor programming skills to machine learning by implementing neural network components from scratch. This chapter bridges the gap between low-level tensor operations and high-level ML applications.
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Chapter 10: Deploying Tensor Applications on Embedded Systems

Adapt your tensor code for resource-constrained environments. Learn specialized techniques for embedded systems where memory and processing power are limited.
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