Returning to our example above, the matrix would be read by the CPU and then transferred to the GPU where the matrix inverse would be carried out. kernel) on a GPU in a scientific code: (1) copy the input data from the CPU memory to the GPU memory, (2) load and execute the GPU kernel on the GPU and (3) copy the results from the GPU memory to CPU memory. There are typically three main steps required to execute a function (a.k.a. In scientific computing, a GPU is used as an accelerator or a piece of auxiliary hardware that is used in tandem with a CPU to quickly carry out numerically-intensive operations. A GPU can be used in this case to run the code faster. However, if the matrix is large then the speed of the computation would be limited by the capacity of the CPU. Such a code can of course be ran using only a CPU. In both cases the processing cores can be used in parallel. For certain workloads like image processing, training artificial neural networks and solving differential equations, a GPU-enabled code can vastly outperform a CPU code. Algorithms that require lots of logic such as "if" statements tend to perform better on the CPU.Ĭonsider a simple code that reads in a matrix (or 2-dimensional array of numbers) from a file, computes the inverse of the matrix and then writes the inverse to a file.
While a CPU has ones or tens of processing cores, a GPU has thousands. A CPU is always needed to run a code that uses a GPU. That is, the CPU executes the main program with the GPU being used the majority of the time to carry out specific functions. In a scientific code, a GPU is used in tandem with a CPU. GPUs are used alongside a CPU to make quick work of numerically-intensive operations. While they are still used for this purpose, beginning in around 2008, the capabilities of GPUs were extended to make them excellent hardware accelerators for scientific computing. GPUs, or graphics processing units, were originally used to process data for computer displays.
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How to Improve Your GPU Knowledge and Skills.Office of Information Technology Senior Management.Scientific Computing Administrators Meeting.
Operations Research and Financial Engineering.Center for Statistics & Machine Learning.Fall Break Parallel Programming Workshop 2021.
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