Accelerating Neural Radiance Fields with Parallel C++ Implementation

Team Members

Summary

We propose to implement a parallel version of Neural Radiance Fields (NeRF) in C++ to accelerate scene rendering. The project will explore both multi-core CPU and GPU parallelization strategies—using frameworks such as OpenMP and CUDA—to optimize the computationally intensive neural network inference and volumetric integration inherent to NeRF. This work will not only yield a high-performance rendering tool but also provide insight into effective mapping of irregular workloads to modern parallel architectures.

Background

Neural Radiance Fields (NeRF) have emerged as a powerful representation for photorealistic scene synthesis. At its core, NeRF uses a deep neural network to predict color and density at continuous 3D locations, integrating these predictions along rays to render images. While highly expressive, the method is notoriously computationally expensive, as it requires numerous evaluations of a neural network per pixel.

Our approach leverages the inherent parallelism in NeRF's rendering process:

• Pixel Independence: The ray integration for each pixel can be computed independently.
• Batch Processing: Evaluations of the neural network can be grouped and processed in parallel.
• Volumetric Integration: The numerical integration over many sample points along each ray lends itself to both data-parallel and task-parallel strategies.

The Challenge

The primary challenges of this project include:

• Load Balancing and Scheduling: Managing uneven workloads that arise from the varying complexity of scenes and ray sampling densities.
• Memory Management and Data Locality: Efficiently handling large volumes of data and ensuring that the memory access patterns (especially for the neural network's weight matrices and input data) exploit cache locality and parallel hardware.
• Integration of Multiple Parallel Models: Developing a consistent design that can operate on both CPU (using multi-threading and OpenMP) and GPU (using CUDA), and effectively comparing their performance.
• Debugging and Performance Tuning: Diagnosing performance bottlenecks in a parallel environment, particularly in a system that interleaves neural network inference with volumetric rendering, will require careful profiling and iterative optimization.

Resources

Hardware:

• Multi-core CPU systems available in the GHC labs
• NVIDIA GPUs (or equivalent) available via campus resources or PSC machines

Software and Libraries:

• C++17 or later for modern language features
• OpenMP for multi-core CPU parallelism
• CUDA Toolkit for GPU implementation
• Linear algebra libraries (e.g., Eigen) for efficient matrix operations
• Profiling tools (e.g., nvprof, gprof) for performance analysis

Goals and Deliverables

Primary Goals (Must-Achieve)

• Baseline C++ Implementation: Develop a serial version of the NeRF algorithm in C++ that accurately performs scene rendering.
• CPU Parallelization: Apply OpenMP to parallelize the computationally intensive parts (e.g., per-pixel ray integration and neural network evaluations).
• GPU Acceleration: Implement a CUDA-based version to further accelerate the workload and compare performance with the CPU version.
• Performance Evaluation: Benchmark both implementations under various scene complexities, analyze speedup, and identify bottlenecks.
• Comprehensive Documentation: Produce a detailed final report and a poster that explain the design choices, performance metrics, and insights gained from the project.

Extra Goals (Nice-to-Have)

• Hybrid CPU-GPU Implementation: Explore strategies to offload certain tasks between CPU and GPU dynamically.
• Adaptive Sampling: Implement dynamic sampling rates based on scene complexity to optimize performance.
• Live Demo: Prepare an interactive demo for the poster session, showcasing real-time rendering improvements.

Schedule