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It took the right person to come along before the Sharpe group could take the plunge. When Philipp Germann joined the group as a postdoc, he played with the existing software for a couple of months before deciding to completely rewrite a multicellular dynamics simulation, this time designed to run on graphics processing units (GPUs). The resulting new software can run simulations with hundreds of thousands of cells in a matter of seconds on a single graphics card. The previous software would take minutes, hours or even days to run. In the words of Sharpe, \u201cIt ended up fantastically well.\u201d<\/p>\n\n\n\n
Graphical computation<\/h3>\n\n\n\n
Open up any personal computer and you will find it packed with chips and components. They include the central processing unit (CPU), which does a lot of the heavy lifting and complicated processing, and the GPU which quickly creates the images you see on your screen \u2013 such as this article you are reading, the ad in your Facebook feed or the computer game you play at the end of the day to relax. GPUs contain hundreds or thousands of very specialised processors called cores. These cores are small and simple, and although they\u2019re not as flexible as CPU cores, they can do one particular thing very, very fast: work out what your screen should display.<\/p>\n\n\n\n
In computer games, every little region of the screen is the result of an independent mathematical calculation that works out what that little bit of the screen should look like. You can do those calculations one by one, or you can divide the screen into lots of little bits, and do all the calculations in parallel. That\u2019s exactly what the GPU is good at: doing lots and lots of identical calculations simultaneously, with each one independent of the others. As computer game graphics became more complex \u2013 from Spacewar<\/em> to Call of Duty<\/em> \u2013 so did GPUs. The problem of how to render the image from a computer game is split into hundreds of thousands of little parallel calculations all done in a fraction of a second.<\/p>\n\n\n\n The central processing unit (CPU) is the \u2018brain\u2019 of a computer. Its function is to carry out calculations that enable the computer to run software. The CPU is split into processing units, called cores, that receive instructions, perform calculations, and take actions on these instructions. The key power of the CPU is its flexibility to multitask with many different types of jobs. It can perform tasks quickly but, like our conscious brain, can focus on only a few \u2018threads\u2019 at a time.<\/p>\n\n\n\n The graphics processing unit (GPU) is a specialised component that was designed to handle graphics. Whereas CPUs have from four to eight cores, GPUs consist of thousands of small cores that can handle many threads simultaneously.<\/p>\n\n\n\n GPU computing is the application of GPUs to accelerate the CPU\u2019s computing by transferring compute-intensive portions of the code to the GPU, where many threads can be handled in parallel. For suitable tasks, this makes calculations much faster to perform and offers cost and power efficiencies.<\/p>\n\n\n\n GPU computing was initially developed for graphics-intensive computational problems such as 3D rendering and gaming, but is now being applied to a variety of domains including complex modelling, simulation and cutting-edge research \u2013 such as the Sharpe group\u2019s computer simulations of mammalian limb development.<\/p>\n\n\n\n <\/p><\/div>\n\n\n\n \u201cFor us,\u201d explains Sharpe, \u201cit turns out that the kind of calculation required for computer games is similar to the kind of calculation we want to do. We try to simulate tissues and organs, how they grow, how their development works. And similarly, if we have a tissue with a hundred thousand cells, we can divide that cell population into little groups of cells, and each GPU can do the calculation for that little group.\u201d Just as the image on a screen can be divided up, so too can the model tissue. \u201cEvery cell has the same genome and every cell has to make the same calculations, that\u2019s why it fits so well into GPUs.\u201d<\/p>\n\n\n\n GPU software requires programming languages with extra features that deal with the parallelisation of the problem, such as CUDA, but \u201cprogramming and writing simulation software is similarly complicated whether you are running it on CPUs or GPUs,\u201d adds Sharpe. It can be challenging to find a programmer who can understand the biological questions and then write the code, he notes. Sharpe says that there\u2019s one major benefit to this shift to GPU computing, though: cost. A full-size CPU cluster costs a lot of money and resources to run, he explains. \u201cWe have switched over to being able to run everything in our own lab on our own computers\u2019 graphics cards. We will probably start using GPU clusters [racks of dedicated GPUs] in the future. But, still, it\u2019s saving a huge amount of time and money in this work.\u201d<\/p>\n\n\n\nWhat is GPU computing?<\/h3>\n\n\n\n
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