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Functions: optimize simple generated multi-functions 

This implements two optimizations:
* Reduce virtual function call overhead when a non-standard virtual
  array is used as input.
* Use a lambda in `type_conversion.cc`.

In my test setup, which creates a float attribute filled with the index,
the running time drops from `4.0 ms` to `2.0 ms`.

Differential Revision: https://developer.blender.org/D14585
This commit is contained in:
Jacques Lucke 2022-04-07 18:48:14 +02:00
parent 8f344b530a
commit 67c42e7f03
2 changed files with 56 additions and 5 deletions
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6
source/blender/blenkernel/intern/type_conversions.cc
View File

@ -18,7 +18,11 @@ static void add_implicit_conversion(DataTypeConversions &conversions)
static const CPPType &to_type = CPPType::get<To>();
static const std::string conversion_name = from_type.name() + " to " + to_type.name();
static fn::CustomMF_SI_SO<From, To> multi_function{conversion_name.c_str(), ConversionF};
static fn::CustomMF_SI_SO<From, To> multi_function{
conversion_name.c_str(),
/* Use lambda instead of passing #ConversionF directly, because otherwise the compiler won't
inline the function. */
[](const From &a) { return ConversionF(a); }};
static auto convert_single_to_initialized = [](const void *src, void *dst) {
*(To *)dst = ConversionF(*(const From *)src);
};

55
source/blender/functions/FN_multi_function_builder.hh
View File

@ -47,11 +47,46 @@ template<typename In1, typename Out1> class CustomMF_SI_SO : public MultiFunctio
template<typename ElementFuncT> static FunctionT create_function(ElementFuncT element_fn)
{
return [=](IndexMask mask, const VArray<In1> &in1, MutableSpan<Out1> out1) {
/* Devirtualization results in a 2-3x speedup for some simple functions. */
devirtualize_varray(in1, [&](const auto &in1) {
if (in1.is_single()) {
/* Only evaluate the function once when the input is a single value. */
const In1 in1_single = in1.get_internal_single();
const Out1 out1_single = element_fn(in1_single);
out1.fill_indices(mask, out1_single);
return;
}
if (in1.is_span()) {
const Span<In1> in1_span = in1.get_internal_span();
mask.to_best_mask_type(
[&](const auto &mask) { execute_SI_SO(element_fn, mask, in1, out1.data()); });
});
[&](auto mask) { execute_SI_SO(element_fn, mask, in1_span, out1.data()); });
return;
}
/* The input is an unknown virtual array type. To avoid virtual function call overhead for
* every element, elements are retrieved and processed in chunks. */
static constexpr int64_t MaxChunkSize = 32;
TypedBuffer<In1, MaxChunkSize> in1_buffer_owner;
MutableSpan<In1> in1_buffer{in1_buffer_owner.ptr(), MaxChunkSize};
const int64_t mask_size = mask.size();
for (int64_t chunk_start = 0; chunk_start < mask_size; chunk_start += MaxChunkSize) {
const int64_t chunk_size = std::min(mask_size - chunk_start, MaxChunkSize);
const IndexMask sliced_mask = mask.slice(chunk_start, chunk_size);
/* Load input from the virtual array. */
MutableSpan<In1> in1_chunk = in1_buffer.take_front(chunk_size);
in1.materialize_compressed_to_uninitialized(sliced_mask, in1_chunk);
if (sliced_mask.is_range()) {
execute_SI_SO(
element_fn, IndexRange(chunk_size), in1_chunk, out1.data() + sliced_mask[0]);
}
else {
execute_SI_SO_compressed(element_fn, sliced_mask, in1_chunk, out1.data());
}
destruct_n(in1_chunk.data(), chunk_size);
}
};
}
@ -66,6 +101,18 @@ template<typename In1, typename Out1> class CustomMF_SI_SO : public MultiFunctio
}
}
/** Expects the input array to be "compressed", i.e. there are no gaps between the elements. */
template<typename ElementFuncT, typename MaskT, typename In1Array>
BLI_NOINLINE static void execute_SI_SO_compressed(const ElementFuncT &element_fn,
MaskT mask,
const In1Array &in1,
Out1 *__restrict r_out)
{
for (const int64_t i : IndexRange(mask.size())) {
new (r_out + mask[i]) Out1(element_fn(in1[i]));
}
}
void call(IndexMask mask, MFParams params, MFContext UNUSED(context)) const override
{
const VArray<In1> &in1 = params.readonly_single_input<In1>(0);