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No, this is better
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@ -606,6 +606,7 @@ private:
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/// - const/non-const inputs instead of non-const/non-const inputs
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/// - the two most common metrics L2 and cosine distance,
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/// - the most powerful SIMD instruction set (AVX-512F).
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bool processed = false;
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#if USE_MULTITARGET_CODE
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/// ResultType is Float32 or Float64
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if constexpr (std::is_same_v<Kernel, L2Distance> || std::is_same_v<Kernel, CosineDistance>)
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@ -613,17 +614,25 @@ private:
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if constexpr (std::is_same_v<ResultType, LeftType> && std::is_same_v<ResultType, RightType>)
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{
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if (isArchSupported(TargetArch::AVX512F))
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{
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Kernel::template accumulateCombine<ResultType>(data_x.data(), data_y.data(), i + offsets_x[0], i, prev, state);
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processed = true;
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}
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}
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else if constexpr (std::is_same_v<Float32, ResultType> && std::is_same_v<BFloat16, LeftType> && std::is_same_v<BFloat16, RightType>)
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{
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if (isArchSupported(TargetArch::AVX512BF16))
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{
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Kernel::accumulateCombineBF16(data_x.data(), data_y.data(), i + offsets_x[0], i, prev, state);
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processed = true;
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}
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}
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#else
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}
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#endif
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if (!processed)
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{
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/// Process chunks in a vectorized manner.
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static constexpr size_t VEC_SIZE = 16;
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static constexpr size_t VEC_SIZE = 32;
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typename Kernel::template State<ResultType> states[VEC_SIZE];
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for (; prev + VEC_SIZE < off; i += VEC_SIZE, prev += VEC_SIZE)
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{
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@ -634,7 +643,8 @@ private:
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for (const auto & other_state : states)
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Kernel::template combine<ResultType>(state, other_state, kernel_params);
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#endif
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}
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/// Process the tail.
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for (; prev < off; ++i, ++prev)
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{
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