I am modifying RNNLM a neural net to study language model. However given the size of my corpus it's running real slow. I tried to optimize the matrix*vector routine (which is the one accountable for 63% of total time for small data set (I would expect it to be worse on larger sets)). Right now I am stuck with intrinsics.
for (b=0; b<(to-from)/8; b++)
{
val = _mm256_setzero_ps();
for (a=from2; a<to2; a++)
{
t1 = _mm256_set1_ps (srcvec.ac[a]);
t2 = _mm256_load_ps(&(srcmatrix[a+(b*8+from+0)*matrix_width].weight));
//val =_mm256_fmadd_ps (t1, t2, t3)
t3 = _mm256_mul_ps(t1,t2);
val = _mm256_add_ps (val, t3);
}
t4 = _mm256_load_ps(&(dest.ac[b*8+from+0]));
t4 = _mm256_add_ps(t4,val);
_mm256_store_ps (&(dest.ac[b*8+from+0]), t4);
}
This example crashes on:
_mm256_store_ps (&(dest.ac[b*8+from+0]), t4);
However if i change to
_mm256_storeu_ps (&(dest.ac[b*8+from+0]), t4);
(with u for unaligned i suppose) everything works as intended. My question is: why would load work (whereas it is not supposed to, if the data is unaligned) and store doesn't. (furthermore both are operating on the same address).
dest.ac have been allocated using
void *_aligned_calloc(size_t nelem, size_t elsize, size_t alignment=64)
{
size_t max_size = (size_t)-1;
// Watch out for overflow
if(elsize == 0 || nelem >= max_size/elsize)
return NULL;
size_t size = nelem * elsize;
void *memory = _mm_malloc(size+64, alignment);
if(memory != NULL)
memset(memory, 0, size);
return memory;
}
and it's at least 50 elements long.
(BTW with VS2012 I have an illegal instruction on some random assignment, so I use linux.)
thank you in advance,
Arkantus.
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