Reduce overhead in calls to tensor product value function #15182
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kronbichler
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bangerth
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| const std::size_t n_batches = | ||
| n_q_points_scalar / n_lanes_internal + | ||
| (n_q_points_scalar % n_lanes_internal > 0 ? 1 : 0); | ||
| (n_q_points_scalar + n_lanes_internal - 1) / n_lanes_internal; |
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What you're doing here is rounding up n_q_points_scalar/n_lanes_internal. Can you put this into a comment?
| n_shapes, | ||
| solution_renumbered); | ||
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| if (evaluation_flags & EvaluationFlags::values) | ||
| { | ||
| for (unsigned int v = 0; v < stride && q + v < n_q_points_scalar; ++v) | ||
| for (unsigned int v = 0; | ||
| v < stride && (stride > 1 ? q + v < n_q_points_scalar : true); |
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This condition (here and below) stumped me for a bit. I think it might be easier to read as
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| v < stride && (stride > 1 ? q + v < n_q_points_scalar : true); | |
| v < stride && (stride == 1 || (q + v < n_q_points_scalar)); |
masterleinad
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Thanks for the comments, I adapted according to the suggestions. |
bangerth
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May 9, 2023
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This is a follow-up to #15137, reducing the integer code overhead a code has to go through. There are three main changes:
ArrayViewto plain pointers in the internal interfaces, which reduces the setup cost by 2 instructions and one register to passed around between function calls.stride==1, the loop termination criterionq + v < n_q_points_scalar;is the same as in the outer loop and thus always true. Avoid one additional branch instruction by spelling out this asv < stride && (stride > 1 ? q + v < n_q_points_scalar : true);- I admit this is not super readable and there are maybe better suggestions to make sure the data rearrangement, which is visible in profilers, does not incur a loop if all we add is one element.do_interpolate_xywithDEAL_II_ALWAYS_INLINE. We had a discussion in Template loop bounds for flexible evaluate/integrate function #14972 (comment) but it seems that at least my compiler (clang-16) does a bad job in guessing the cost of moving the variables in and out of registers; instead, the outer functionevaluate_tensor_product_value_and_gradient_shapesshould be the function to collect the code. From a code size perspective, I do not see an advantage of not to inline, as the only function callingdo_interpolate_xyis that other function.