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Showing posts from May, 2019

GSoC Coding Phase Starts

“The computer was born to solve problems that did not exist before.” So, finally, the coding phase started. Its been a lot of problems while setting the environment for Libroadrunner. Thanks to the Kiri sir for providing me the cmakelist and help me in this process. Sometimes we exactly followed the build article but due to a single fault, we get lots of problems. And the biggest problem is mentor being remotely located can't help as they don't have exact information. It is very difficult to debug the fault but one can always ask doubts from the mentor as they are the experienced one and guide us in the right direction. Fortunately, with the help of mentor and co-mentor, I have built roadrunner. I first set up all the environment in the Linux but I shifted to windows as all the developers are working in windows. This helps in some case if we get stuck. And It is beneficial for me in the later stage. I have designed a plugin loader with the help of poco class lo

GSoC - Community Bonding

“Make it work, make it right, make it efficient, make it fast.” My project " Developing an optimization Library for Libroadrunner " got selected in GSoC 2019 under NRNB, here is my proposal .   About the Project LibRoadrunner is a high-performance SBML based simulator that uses LLVM to generate very efficient runtime code. This enables libRoadrunner to simulate models on par with compiled C/C++ code. By combining libroadrunner with standard optimization algorithms it is possible to use libroadrunner to fit models to data. At present this is done by writing code to link the standard Python optimizers available via scipy with libroadrunner. Although this works, it is inefficient and for large models, it is not practical. In this project, we would like to develop a C/C++ based optimization library that can be used directly by libroadrunner without having to go via Python. This would enable us to provide high-performance optimization capabilities. Community Bonding