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    <p>Even in a multi-core CPU, your units of work are going to be much larger than on a GPGPU. GPGPUs are appropriate for problems that scale very well, with each chunk of work being increadibly small. A GPGPU has much higher latency because you have to move data to the GPU's memory system before it can be accessed. However, once the data is there, your throughput, if the problem is appropriately scalable, will be much higher with a GPGPU. In my experience, the problem with GPGPU programming is the latency in getting data from normal memory to the GPGPU.</p> <p>Also, GPGPUs are horrible at communicating across worker processes if the worker processes don't have a sphere of locality routing. If you're trying to communicate all the way across the GPGPU, you're going to be in a lot of pain. For this reason, standard MPI libraries are a poor fit for GPGPU programming.</p> <p>All computers aren't designed like GPUs because GPUs are fantastic at high latency, high throughput calculations that are inherently parallel and can be broken down easily. Most of what the CPU doing is not inherently parallel and does not scale to thousands or millions of simultaneous workers very efficiently. Luckily, graphics programming does and that's why all this started in GPUs. People have increasingly been finding problems that they can make look like graphics problems, which has led to the rise of GPGPU programming. However, GPGPU programming is only really worth your time if it is appropriate to your problem domain.</p>
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