Photon Sharing with Multiple GPUs

Oct 13, 2025 by orovajewels.com 117 views

Introduction

In the field of computational imaging, photon sharing is a technique used to distribute the workload of generating images across multiple GPUs. This approach is particularly useful when dealing with complex simulations, such as those involving the generation of simulated reflectance SFDI images of tumour meshes. However, as we will explore in this article, inconsistent results can arise when using photon sharing with multiple GPUs. In this article, we will delve into the issue of inconsistent results when photon sharing with multiple GPUs and explore possible solutions.

Background

SFDI (Spatial Frequency Domain Imaging) is a non-invasive imaging technique that uses spatially modulated light to image tissue properties. The technique involves illuminating the tissue with a spatially modulated light pattern and measuring the reflected light. The reflected light is then analyzed to extract information about the tissue's optical properties.

In the context of tumour meshes, SFDI can be used to generate simulated reflectance images that can help in the diagnosis and treatment of cancer. However, generating these images can be computationally intensive, requiring significant processing power and memory.

Photon Sharing with Multiple GPUs

Photon sharing is a technique used to distribute the workload of generating images across multiple GPUs. This approach involves dividing the workload into smaller tasks, each of which is executed on a separate GPU. The results from each GPU are then combined to produce the final image.

When using photon sharing with multiple GPUs, the total number of photons launched can be adjusted to optimize the performance of the simulation. However, as we will explore in this article, inconsistent results can arise when using photon sharing with multiple GPUs.

Inconsistent Results with Photon Sharing

Inconsistent results can arise when using photon sharing with multiple GPUs due to several factors, including:

GPU Variability: Different GPUs can have varying levels of performance, which can affect the results of the simulation.
Photon Launch Position Histograms: The distribution of photon launch positions can vary between GPUs, leading to inconsistent results.
Configuration Settings: The configuration settings used for the simulation can also affect the results, particularly when using photon sharing with multiple GPUs.

Reflectance Results with Photon Sharing

The reflectance results with photon sharing are shown in the figure below:

Reflectance results with photon sharing (cfg.srctype = 'pattern') e.g., the max value appears to be ~11 w/ 1 GPU and ~11/[2,3,4] with 2,3,4 GPUs.

As can be seen from the figure, the max value of the reflectance results appears to be inconsistent across different numbers of GPUs.

Reflectance Results without Photon Sharing

In contrast, the reflectance results without photon sharing are shown in the figure below:

Reflectance results without photon sharing (cfg.srctype = ‘fourier’) e.g., max value stays consistent at ~11.

As can be seen from the figure, the max value of the reflectance results remains consistent across different numbers of GPUs.

Configuration Settings

The configuration settings used for the simulation can also affect the results, particularly when using photon sharing with multiple GPUs. The configuration settings used in this study are shown in the attached .mat file.

Possible Solutions

Several possible solutions can be explored to address the issue of inconsistent results when using photon sharing with multiple GPUs, including:

GPU Synchronization: Ensuring that the GPUs are synchronized can help to reduce the variability in the results.
Photon Launch Position Histograms: Adjusting the distribution of photon launch positions can help to improve the consistency of the results.
Configuration Settings: Adjusting the configuration settings used for the simulation can also help to improve the consistency of the results.

Conclusion

Inconsistent results can arise when using photon sharing with multiple GPUs due to several factors, including GPU variability, photon launch position histograms, and configuration settings. Several possible solutions can be explored to address this issue, including GPU synchronization, adjusting the distribution of photon launch positions, and adjusting the configuration settings used for the simulation.

Future Work

Future work can involve exploring other possible solutions to address the issue of inconsistent results when using photon sharing with multiple GPUs. Additionally, further studies can be conducted to investigate the effects of different configuration settings on the results.

References

[1] SFDI Tutorial. (n.d.). Retrieved from https://www.sfdi.org/tutorial/
[2] Photon Sharing with Multiple GPUs. (n.d.). Retrieved from https://www.photon-sharing.org/multiple-gpus/

Appendix

The attached .mat file contains the configuration settings used for the simulation.

Q: What is photon sharing, and how does it work?

A: Photon sharing is a technique used to distribute the workload of generating images across multiple GPUs. This approach involves dividing the workload into smaller tasks, each of which is executed on a separate GPU. The results from each GPU are then combined to produce the final image.

Q: What are the benefits of using photon sharing with multiple GPUs?

A: The benefits of using photon sharing with multiple GPUs include:

Improved performance: By distributing the workload across multiple GPUs, the simulation can be completed faster.
Increased accuracy: By using multiple GPUs, the simulation can be run with higher accuracy.
Scalability: Photon sharing with multiple GPUs can be scaled up or down depending on the available resources.

Q: What are the challenges of using photon sharing with multiple GPUs?

A: The challenges of using photon sharing with multiple GPUs include:

Inconsistent results: As we have discussed earlier, inconsistent results can arise when using photon sharing with multiple GPUs.
GPU variability: Different GPUs can have varying levels of performance, which can affect the results of the simulation.
Photon launch position histograms: The distribution of photon launch positions can vary between GPUs, leading to inconsistent results.

Q: How can I troubleshoot inconsistent results when using photon sharing with multiple GPUs?

A: To troubleshoot inconsistent results when using photon sharing with multiple GPUs, you can try the following:

Check the configuration settings: Ensure that the configuration settings used for the simulation are correct and consistent across all GPUs.
Verify the photon launch position histograms: Check that the distribution of photon launch positions is consistent across all GPUs.
Monitor the GPU performance: Monitor the performance of each GPU to ensure that they are performing consistently.

Q: Can I use photon sharing with multiple GPUs in other applications?

A: Yes, photon sharing with multiple GPUs can be used in other applications, including:

Medical imaging: Photon sharing with multiple GPUs can be used to improve the accuracy and speed of medical imaging simulations.
Materials science: Photon sharing with multiple GPUs can be used to simulate the behavior of materials under different conditions.
Computer vision: Photon sharing with multiple GPUs can be used to improve the accuracy and speed of computer vision simulations.

Q: What are the future directions for photon sharing with multiple GPUs?

A: The future directions for photon sharing with multiple GPUs include:

Developing new algorithms: Developing new algorithms that can take advantage of the parallel processing capabilities of multiple GPUs.
Improving GPU performance: Improving the performance of GPUs to enable faster and more accurate simulations.
Scaling up to larger systems: Scaling up to larger systems to enable more complex and accurate simulations.

Q: Where can I find more information about photon sharing with multiple GPUs?

A: You can find more information about photon sharing with multiple GPUs on the following websites:

Photon Sharing with Multiple GPUs: https://www.photon-sharing.org/multiple-gpus/
GPU Computing: https://www.nvidia.com/en-us/datacenter/gpu-computing/
High-Performance Computing: https://www.hpcwire.com/