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    <p>The benchmarks I've seen indicate that OpenCL and OpenMP running on the same hardware are usually comparable in performance, or OpenMP has slightly better performance. However, I haven't seen any benchmarks that I would consider conclusive, because they've been mostly lacking in detailed explanations of their methodology. However, there are a few useful things to consider:</p> <ul> <li><p>OpenCL will always have some extra overhead when compiling the kernel at runtime. Any benchmark either needs to list this time separately, use pre-compiled native kernels, or run long enough that the kernel compilation is insignificant.</p></li> <li><p>OpenCL implementations will vary. GPU vendors like NVidia have no incentive to make sure their CPU-based OpenCL implementation is as fast as possible. None of the OpenCL implementations are likely to be as mature as a good OpenMP implementation.</p></li> <li><p>The OpenCL spec says basically nothing about how CPU-based implementations use threading under the hood, so any discussion of whether the threading is relatively lightweight or heavyweight will necessarily be implementation-specific. </p></li> <li><p>When you're running OpenCL code on a CPU, your work items don't have to be tiny and numerous. You can break down the problem in the same way you would for OpenMP.</p></li> </ul> <p>Even if OpenCL has a bit more overhead, there may be other reasons to prefer it. </p> <ul> <li><p>Obviously, if your code can make good use of a GPU, you will want to have an OpenCL implementation. OpenCL performance on a CPU may be good enough that it isn't worth it to also maintain an OpenMP fallback code path for users who don't have powerful GPUs.</p></li> <li><p>A good CPU-based OpenCL implementation means that you will automatically get the benefit of whatever instruction set extensions the CPU and OpenCL implementation support. With OpenMP, you have to do extra work to make sure that your executable includes both SSEx and AVX code paths.</p></li> <li><p>OpenCL vector primitives can help you express some explicit parallelism without the portability and readibility sacrifices you get from using SSE intrinsics.</p></li> </ul>
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    1. COI wonder whether the user-without-GPU-case really is that practical. Instead of maintaining OpenMP fallback code, you'd have to maintain OpenCL fallback code, as CPUs won't support 2D local work sizes, have problems with __local memory and whatnot. Not much gained there if you have optimized GPU kernels.
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    2. COWhy do you think that CPU-based implementations can't support 2D local work group sizes or local memory? On a CPU, cache memory is managed by hardware instead of software, so the only difference between global and local memory would be whether locking is needed to access it. The work group sizes would amount to scheduler hints for NUMA systems. Yes, a lot of the optimization effort put into OpenCL code to make it run well on a GPU won't affect performance on the CPU, but it won't break the code, either. Any kernel that will run on a GPU can run on a compliant CPU implementation.
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    3. CO@user57368: Just an addition that usage of optimizations such as explicit usage of local memory makes sense for GPU. On CPUs, having this optimizations "may negatively" affect the performance, atleast when using the Intel OpenCL implementation for x86 CPUs.
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