sse

Is there a clever (ie: branchless) way to "compact" a hex number. Basically move all the 0s all to one side? eg: 0x10302040 -> 0x13240000 or 0x10302040 -> 0x00001324 I looked on Bit Twiddling Hacks but didn't see anything. It's for a SSE numerical pivoting algorithm. I need to remove any pivots that become 0. I can use _mm_cmpgt_ps to find good pivots, _mm_movemask_ps to convert that in to a mask, and then bit hacks to get something like the above. The hex value gets munged in to a mask for a _mm_shuffle_ps instruction to perform a permutation on the SSE 128 bit register. To compute mask for

Let's start by including the following: #include <vector> #include <random> using namespace std; Now, suppose that one has the following three std:vector<float> : N = 1048576; vector<float> a(N); vector<float> b(N); vector<float> c(N); default_random_engine randomGenerator(time(0)); uniform_real_distribution<float> diceroll(0.0f, 1.0f); for(int i-0; i<N; i++) { a[i] = diceroll(randomGenerator); b[i] = diceroll(randomGenerator); } Now, assume that one needs to sum a and b element-wise and store the result in c , which in scalar form looks like the following: for(int i=0; i<N; i++) { c[i] = a[i]

I'm wondering if SSE/AVX operations such as addition and multiplication can be an atomic operation? The reason I ask this is that in OpenMP the atomic construct only works on a limited set of operators. It does not work on for example SSE/AVX additions. Let's assume I had a datatype float4 that corresponds to a SSE register and that the addition operator is defined for float4 to do an SSE addition. In OpenMP I could do a reduction over an array with the following code: float4 sum4 = 0.0f; //sets all four values to zero #pragma omp parallel { float4 sum_private = 0.0f; #pragma omp for nowait

I need to learn assembly using SSE instructions and need gcc to link the ASM code with c code. I have no idea where to start and google hasn't helped. You might want to start looking through the chip documentation from intel Intel Processor Software Developer Manuals . Assembly language coding isn't a whole lot of fun, and it's usually unneccessary except in few cases where code is performance critical. Given you are looking at SSE, I would hazard that your effort may be better spent looking into CUDA, using your graphics card to perform vector computations via custom shaders. That way you don