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I'm optimizing an element wise multiplication of two single dimensional arrays for a dual Cortex-A9 processor. Linux is running on the board and I'm using the GCC 4.5.2 compiler. So the following is my C++ inline assembler function. src1, src2 and dst are 16 byte aligned. Update: Testable Code: void Multiply( const float* __restrict__ src1, const float* __restrict__ src2, float* __restrict__ dst, const unsigned int width, const unsigned int height) { int loopBound = (width * height) / 4; asm volatile( ".loop: \n\t" "vld1.32 {q1}, [%[src1]:128]! \n\t" "vld1.32 {q2}, [%[src2]:128]! \n\t" "vmul.f32 q0, q1, q2 \n\t" "vst1.32 {q0}, [%[dst]:128]! \n\t" "subs %[lBound], %[lBound], $1 \n\t" "bge .loop \n\t" : :[dst] "r" (dst), [src1] "r" (src1), [src2] "r" (src2), [lBound] "r" (loopBound) :"memory", "d0", "d1", "d2", "d3", "d4", "d5 ); } //The following function describes how to test the element wise multiplication void Test() { const unsigned int width = 1024, height = 1024; float* src1 __attribute__((aligned(16))) = new float[width * height]; float* src2 __attribute__((aligned(16))) = new float[width * height]; float* dst __attribute__((aligned(16))) = new float[width * height]; for(unsigned int i = 0; i < (width * height); i++) { src1[i] = (float)rand(); src2[i] = (float)rand(); } Multiply(src1, src2, dst, width, height); std::cout << dst[0] << std::endl; } The calculation of 1024*1024 values takes ~0.016 s. (Two threads - each thread calculates a half of the array). Naively interpreted, the calculation of one iteration takes 122 cycles. This seems to be a bit slow. But where is the bottleneck? I even tried the pld command

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