Code and Implementation

This section documents the design, structure, and technical implementation details of the High-Performance Physics Engine. It is intended for developers and researchers interested in the code architecture, performance strategies, and system-level decisions behind the engine.

The implementation is centered around a modular, performance-focused architecture using a combination of C/C++, CUDA, and Fortran.

Contents

1. Project Structure and Build System

Overview of the repository layout, build configuration using CMake, and how the system compiles across multiple languages.

(Page to be added: Code-Structure)

2. GPU Acceleration with CUDA

Detailed explanation of how CUDA is used to offload matrix-heavy operations to the GPU, including custom kernels, cuBLAS integration, and memory handling.

(Page to be added: Code-CUDA)

3. Fortran Integration

Discussion of where and why Fortran is used, how it integrates with the C++ codebase, and which numerical routines are handled by Fortran.

(Page to be added: Code-Fortran)

4. Performance Strategy and Profiling

Covers optimization techniques, hardware considerations, and tools used to measure and improve performance, including GPU/CPU balance and bottleneck resolution.

(Page to be added: Code-Performance)

Design Philosophy

The engine’s implementation avoids external simulation libraries to allow for full control and transparency. Every component is designed to:

• Reflect theoretical formulations documented in the Math section
• Prioritize computational efficiency and parallel execution
• Provide a flexible base for experimentation and expansion

The use of multiple languages is intentional:

• C/C++ provides structure, memory management, and control flow.
• CUDA handles intensive, parallelizable computations.
• Fortran is reserved for smaller, CPU-bound routines that benefit from low-level array optimization.

Each page in this section ties the implementation back to the underlying theory where appropriate.

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