To CUDA or not to CUDA

Jack Kolesar:
I've got a Radeon 7900XTX for gaming and recently just added a second video card specifically for CUDA/PixInsight.  Ended up just getting a 3060 but even that made star extraction on my 12-Core Ryzen 7900 go from 1:15 to 13 seconds.  Really wish OpenCL was supported but for now, I'll take the two card setup!

Charles Barker:
+1!

Just got my new computer specifically "built-up" to work with Pixinsight and other image processing applications after running on, using your word, "Cuda-less" system. Wow! What a difference it makes with the AI driven toolsets! Would really like to see Pixinsight start leveraging the GPU for some of its intensive image processing (like WBPP integration)!

Monty Chandler:

Charles Barker:
+1!

Just got my new computer specifically "built-up" to work with Pixinsight and other image processing applications after running on, using your word, "Cuda-less" system. Wow! What a difference it makes with the AI driven toolsets! Would really like to see Pixinsight start leveraging the GPU for some of its intensive image processing (like WBPP integration)!

WBPP isn't a PixInsight process.  It's a script.

So guys I don’t want to spam you but here is a detailed listing from the artificial brain of processes make sense /nonsense on GPU /CPU
and I do agree to this analysis.




The potential for GPU acceleration in PixInsight is an intriguing topic, especially when considering its computationally intensive tasks such as stacking, quality estimation, and processes involved in workflows like the Weighted Batch Preprocessing (WBPP) script. While PixInsight already benefits from some degree of multi-threading and CPU optimization, leveraging GPU power could greatly enhance performance in several areas. Let’s break it down:

1. GPU Acceleration for AI-Based Tools

PixInsight already incorporates machine-learning-based tools like NoiseXTerminator and StarXTerminator, which often use GPUs when available. These tools demonstrate how GPUs excel at:
•    Matrix and tensor computations: Key for neural networks and machine learning.
•    Parallel processing: Thousands of cores handle repetitive computations efficiently.

Thus, for AI-based tools in PixInsight, GPU acceleration is practically a must, as seen with tools leveraging CUDA and OpenCL.

2. GPU Acceleration for Stacking

Stacking involves aligning, normalizing, and integrating multiple frames, which are traditionally CPU-bound processes. GPU acceleration could bring significant advantages here:
•    Image Registration: Calculations for aligning stars and frames involve transformation matrices, which GPUs can process faster.
•    Pixel Rejection: Techniques like Winsorized sigma clipping could see performance boosts since they involve analyzing large data sets.
•    Integration: Averaging, weighting, and normalizing pixel values are ideal tasks for GPUs due to their parallel nature.

However, implementing GPU acceleration for stacking isn’t trivial. Tasks like data I/O (loading/saving FITS files) and the need for double-precision floating-point operations (often less optimized on GPUs) might introduce bottlenecks.

3. Quality Estimation in WBPP

Quality estimation includes measuring parameters like FWHM, SNR, and eccentricity for each frame. These involve:
•    Statistical Analysis: GPUs can speed up calculations for large datasets like image histograms or background models.
•    Convolution and Filtering: Tasks that analyze star profiles or noise are highly parallelizable and would greatly benefit from GPU acceleration.

For WBPP, GPU acceleration could significantly speed up preprocessing, particularly in quality estimation and frame sorting, where CPUs often become the bottleneck with large datasets.

4. Challenges of GPU Acceleration in PixInsight

Despite its advantages, there are notable challenges to integrating GPU acceleration:
•    Development Effort: GPU programming is more complex, requiring optimized CUDA, OpenCL, or Vulkan implementations.
•    Double-Precision Support: Many astrophotography processes rely on high precision, which is less efficient on consumer-grade GPUs.
•    Data Transfer Overhead: Moving data between the CPU and GPU can sometimes negate performance gains, especially for smaller operations.
•    Broad Compatibility: GPU acceleration must work across diverse hardware setups, including AMD, NVIDIA, and integrated GPUs.

5. Future Possibilities

GPU acceleration in PixInsight could revolutionize workflows if implemented thoughtfully. Here’s where it would shine:
•    Dynamic Alignment & Stacking: Real-time previews and faster alignment during preprocessing.
•    Wavelet-Based Transformations: Tasks like deconvolution, noise reduction, and multiscale processing could become faster.
•    Real-Time Feedback in Workflows: The WBPP script could dynamically adjust frame weights with GPU-accelerated quality estimation, improving automation.

Conclusion

For GPU acceleration in PixInsight, AI-based tools like noise reduction already make excellent use of GPUs, and expanding this to WBPP’s stacking and quality estimation processes could be transformative. However, the balance between development complexity and performance gains must be carefully considered.