Complex Technical Components

The technical components of AnalysisG consist of high-performance C++/CUDA implementations that provide the computational backbone for the framework.

Overview

These components are designed for:

  • Maximum performance through C++17 optimizations

  • GPU acceleration via CUDA

  • Low-level memory management

  • Efficient algorithms for HEP data processing

When to Use Technical Components

Technical components are used internally by the simple interfaces. Most users will interact with these through the high-level Python/Cython APIs. Direct use of technical components is recommended only when:

  • Implementing new low-level algorithms

  • Optimizing performance-critical code

  • Adding GPU-accelerated operations

  • Extending the framework with new functionality

Component Categories

Modules Package

The modules package contains pure C++ implementations of core algorithms and data structures. These are complex technical components with private member variables and advanced C++ features.

Key modules:

  • analysis: Analysis framework implementation

  • container: Data container implementations

  • dataloader: Data loading and preprocessing

  • event: Event-level C++ implementations

  • graph: Graph data structures and algorithms

  • io: Input/output operations

  • meta: Metadata management

  • metric: Metric computation engines

  • model: Model backend implementations

  • nusol: Neutrino reconstruction algorithms

  • optimizer: Optimization algorithms

  • particle: Particle data structures

  • selection: Selection logic implementations

  • structs: Core data structures

  • tools: Utility functions

PyC Package

The PyC package provides Python-C++ interface layers with CUDA acceleration for computationally intensive operations.

Key components:

  • cutils: CUDA utility functions and atomic operations

  • graph: GPU-accelerated graph operations and PageRank

  • interface: Python-C++ interface layer

  • nusol: Neutrino reconstruction (CPU and GPU)

  • operators: Mathematical operators (CPU and GPU)

  • physics: Physics calculations (CPU and GPU)

  • transform: Coordinate transformations (CPU and GPU)

Architecture

The technical components follow a layered architecture:

  1. C++ Core Layer: Pure C++ implementations in modules/

  2. CUDA Acceleration Layer: GPU implementations in pyc/*/cuda/ and *.cu files

  3. Interface Layer: Cython bindings in pyc/interface/

  4. Python API Layer: High-level Python APIs in core/

File Organization

Technical components follow a consistent structure:

module_name/
├── CMakeLists.txt          # Build configuration
├── cxx/                    # C++ implementation files
│   ├── file1.cxx
│   └── file2.cxx
├── cuda/                   # CUDA implementations (if applicable)
│   ├── kernel1.cu
│   └── kernel2.cu
└── include/                # Header files
    └── module_name/
        ├── header1.h
        └── header2.h

See Also