The use of mobile devices has increased significantly around the world in the recent years. This article presents answers to these four challenges. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. A mobile grid is a grid that includes mobile devices as resource providers. A mobile grid can be an adequate computing infrastructure for this problem. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. Email Z.Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. We'll need your help when the time comes! Ancient software: Old (SENR/NRPy+) software repository.Īll codes are licensed under the 2-clause BSD license unless otherwise specified. Please sign up for the newsletter to stay apprised of the latest progress. While the above provides a playground for black hole simulations run in the cloud, the BOINC client (which will enable you to benefit gravitational wave astronomy with your spare CPU cycles) is under development. If you are interested in more details, they are all documented in previous NRPy+ tutorial modules. It is most useful if the black holes' masses add up to 1. To whatever you like, and then returning to Step 3 above. If you want to fiddle with the black hole parameters, you can for example change the masses of the black holes by editing the line of codeĬonst REAL BH1_mass = 0.5,BH2_mass = 0.5.The whole process takes about 10 minutes, but the movie near the bottom visualizes what just happened (reproducing what is on the homepage) Finally, matplotlib is used within the notebook to visualize the output. Then the C code will be compiled into an executable and run on the cloud server. NRPy+ will first generate Einstein's equations of general relativity in the form of a highly optimized C code. Click the "Fast-forward" button at the top, and then "Restart and Run All Cells".Click the "Colliding Black Holes!" module near the bottom (in purple).Open the Interactive NRPy+ Tutorial (it might take a minute to load), hosted by the mybinder cloud.If you want to perform black hole collision simulations right now, no download or installation is necessary! You can run your own black hole collision simulations from within your web browser! Here's how: Your web browser should open the NRPy+ folder click on NRPyPlus_Tutorial.ipynb to get started!īrowse NRPy+ source code online: No Download Option (Collide black holes from your browser!)
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