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    <p>If you launch multiple kernels, they will not be automatically be parallelized (i.e. no GPU task parallelism). However, the kernel invocation is asynchronous on the host side, so host code will continue running in parallel while the kernel is executing. </p> <p>To get task parallelism you have to do it by hand - in Cuda the concept is called streams, and in OpenCL command queues. Without explicitly creating multiple streams/queues and scheduling each kernel to its own queue, they will be executed in sequence (there is an OpenCL feature allowing queues to run out-of-order, but I don't know if any implementation supports it.) However, running the kernels in parallel will probably not give much benefit if each dataset is large enough to utilize all the GPU cores.</p> <p>If you have actual for loops in your kernels, they will not in themselves be parallelized, the parallelism comes from specifying a grid size, which will cause the kernel to be invoked in parallel for each element in that grid (so if you have for loops inside your kernel they will be executed in full <em>by each thread</em>). In other words, you should specify a grid size when calling the kernel, and inside the kernel use threadIdx/blockIdx (Cuda) or getGlobalId() (OpenCL) to identify which data item to process in that particular thread.</p> <p>A useful book for learning OpenCL is the <a href="http://rads.stackoverflow.com/amzn/click/0321749642" rel="nofollow">OpenCL Programming Guide</a>, but the <a href="http://www.khronos.org/opencl/" rel="nofollow">OpenCL spec</a> is also worth a look.</p>
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    1. POParallelism in GPU - CUDA / OpenCL
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    1. USEmil Styrke
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