fma

Assuming that in some C or C++ code I have a function named T fma( T a, T b, T c ) that performs 1 multiplication and 1 addition like so ( a * b ) + c ; how I'm supposed to optimize multiple mul & add steps ? For example my algorithm needs to be implemented with 3 or 4 fma operations chained and summed together, How I can write this is an efficient way and at what part of the syntax or semantics I should dedicate particular attention ? I also would like some hints on the critical part: avoid changing the rounding mode for the CPU to avoid flushing the cpu pipeline. But I'm quite sure that just

I have this assembly (AT&T syntax): mulsd %xmm0, %xmm1 addsd %xmm1, %xmm2 I want to replace it with: vfmadd231sd %xmm0, %xmm1, %xmm2 Will this transformation always leave equivalent state in all involved registers and flags? Or will the result floats differ slightly in someway? (If they differ, why is that?) (About the FMA instructions: http://en.wikipedia.org/wiki/FMA_instruction_set ) No. In fact, a major part of the benefit of fused multiply-add is that it does not (necessarily) produce the same result as a separate multiply and add. As a (somewhat contrived) example, suppose that we have:

Using MSVC 2013 and AVX 1, I've got 8 floats in a register: __m256 foo = mm256_fmadd_ps(a,b,c); Now I want to call inline void print(float) {...} for all 8 floats. It looks like the Intel AVX intrisics would make this rather complicated: print(_castu32_f32(_mm256_extract_epi32(foo, 0))); print(_castu32_f32(_mm256_extract_epi32(foo, 1))); print(_castu32_f32(_mm256_extract_epi32(foo, 2))); // ... but MSVC doesn't even have either of these two intrinsics. Sure, I could write back the values to memory and load from there, but I suspect that at assembly level there's no need to spill a register.

I have a i5-4250U which has AVX2 and FMA3. I am testing some dense matrix multiplication code in GCC 4.8.1 on Linux which I wrote. Below is a list of three difference ways I compile. SSE2: gcc matrix.cpp -o matrix_gcc -O3 -msse2 -fopenmp AVX: gcc matrix.cpp -o matrix_gcc -O3 -mavx -fopenmp AVX2+FMA: gcc matrix.cpp -o matrix_gcc -O3 -march=native -fopenmp -ffast-math The SSE2 and AVX version are clearly different in performance. However, the AVX2+FMA is no better than the AVX version. I don't understand this. I get over 80% of the peak flops of the CPU assuming there is no FMA but I think I

According to the documentation , there is a fma() function in math.h . That is very nice, and I know how FMA works and what to use it for. However, I am not so certain how this is implemented in practice? I'm mostly interested in the x86 and x86_64 architectures. Is there a floating-point (non-vector) instruction for FMA, perhaps as defined by IEEE-754 2008? Is FMA3 or FMA4 instruction used? Is there an intrinsic to make sure that a real FMA is used, when the precision is relied upon? The actual implementation varies from platform to platform, but speaking very broadly: If you tell your

I come across this page and find there is an odd floating multiply add function -- fma and fmaf . It says that the result is something like: (x * y) + z #fma(x,y,z) And the value is infinite precision and round once to the result format . However, AFAICT I've never seen such a ternary operation before. So I'm wondering what's the cumstom usage for this func. The important aspect of the fused-multiply-add instruction is the (virtually) infinite precision of the intermediate result. This helps with performance, but not so much because two operations are encoded in a single instruction — It helps