SSE SIMD Optimization For Loop - visual-c++

Question

I have some code in a loop
for(int i = 0; i < n; i++)
{
u[i] = c * u[i] + s * b[i];
}
So, u and b are vectors of the same length, and c and s are scalars. Is this code a good candidate for vectorization for use with SSE in order to get a speedup?
UPDATE
I learnt vectorization (turns out it's not so hard if you use intrinsics) and implemented my loop in SSE. However, when setting the SSE2 flag in the VC++ compiler, I get about the same performance as with my own SSE code. The Intel compiler on the other hand was much faster than my SSE code or the VC++ compiler.
Here is the code I wrote for reference
double *u = (double*) _aligned_malloc(n * sizeof(double), 16);
for(int i = 0; i < n; i++)
{
u[i] = 0;
}
int j = 0;
__m128d *uSSE = (__m128d*) u;
__m128d cStore = _mm_set1_pd(c);
__m128d sStore = _mm_set1_pd(s);
for (j = 0; j <= i - 2; j+=2)
{
__m128d uStore = _mm_set_pd(u[j+1], u[j]);
__m128d cu = _mm_mul_pd(cStore, uStore);
__m128d so = _mm_mul_pd(sStore, omegaStore);
uSSE[j/2] = _mm_add_pd(cu, so);
}
for(; j <= i; ++j)
{
u[j] = c * u[j] + s * omegaCache[j];
}

Answer 1

Yes, this is an excellent candidate for vectorization. But, before you do so, make sure you've profiled your code to be sure that this is actually worth optimizing. That said, the vectorization would go something like this:
int i;
for(i = 0; i < n - 3; i += 4)
{
load elements u[i,i+1,i+2,i+3]
load elements b[i,i+1,i+2,i+3]
vector multiply u * c
vector multiply s * b
add partial results
store back to u[i,i+1,i+2,i+3]
}
// Finish up the uneven edge cases (or skip if you know n is a multiple of 4)
for( ; i < n; i++)
u[i] = c * u[i] + s * b[i];
For even more performance, you can consider prefetching further array elements, and/or unrolling the loop and using software pipelining to interleave the computation in one loop with the memory accesses from a different iteration.

Answer 2

_mm_set_pd is not vectorized. If taken literally, it reads the two doubles using scalar operations, then combines the two scalar doubles and copy them into the SSE register. Use _mm_load_pd instead.

Answer 3

probably yes, but you have to help compiler with some hints.
__restrict__ placed on pointers tells compiler that there is no alias between two pointers.
if you know alignment of your vectors, communicate that to compiler (Visual C++ may have some facility).
I am not familiar with Visual C++ myself, but I have heard it is no good for vectorization.
Consider using Intel compiler instead.
Intel allows pretty fine-grained control over assembly generated: http://www.intel.com/software/products/compilers/docs/clin/main_cls/cref_cls/common/cppref_pragma_vector.htm

Answer 4

Yes, this is a great candidate for vectorizaton, assuming there is no overlap of U and B array. But the code is bound by memory access(load/store). Vectorization helps reduce cycles per loop, but the instructions will stall due to cache-miss on U and B array . The Intel C/C++ Compiler generates the following code with default flags for Xeon x5500 processor. The compiler unrolls the loop by 8 and employs SIMD ADD (addpd) and MULTIPLY (mulpd) instructions using xmm[0-16] SIMD registers. In each cycle, the processor can issue 2 SIMD instructions yielding 4-way scalar ILP, assuming you have the data ready in the registers.
Here U, B, C and S are Double Precision (8 bytes).
..B1.14: # Preds ..B1.12 ..B1.10
movaps %xmm1, %xmm3 #5.1
unpcklpd %xmm3, %xmm3 #5.1
movaps %xmm0, %xmm2 #6.12
unpcklpd %xmm2, %xmm2 #6.12
# LOE rax rcx rbx rbp rsi rdi r8 r12 r13 r14 r15 xmm0 xmm1 xmm2 xmm3
..B1.15: # Preds ..B1.15 ..B1.14
movsd (%rsi,%rcx,8), %xmm4 #6.21
movhpd 8(%rsi,%rcx,8), %xmm4 #6.21
mulpd %xmm2, %xmm4 #6.21
movaps (%rdi,%rcx,8), %xmm5 #6.12
mulpd %xmm3, %xmm5 #6.12
addpd %xmm4, %xmm5 #6.21
movaps 16(%rdi,%rcx,8), %xmm7 #6.12
movaps 32(%rdi,%rcx,8), %xmm9 #6.12
movaps 48(%rdi,%rcx,8), %xmm11 #6.12
movaps %xmm5, (%rdi,%rcx,8) #6.3
mulpd %xmm3, %xmm7 #6.12
mulpd %xmm3, %xmm9 #6.12
mulpd %xmm3, %xmm11 #6.12
movsd 16(%rsi,%rcx,8), %xmm6 #6.21
movhpd 24(%rsi,%rcx,8), %xmm6 #6.21
mulpd %xmm2, %xmm6 #6.21
addpd %xmm6, %xmm7 #6.21
movaps %xmm7, 16(%rdi,%rcx,8) #6.3
movsd 32(%rsi,%rcx,8), %xmm8 #6.21
movhpd 40(%rsi,%rcx,8), %xmm8 #6.21
mulpd %xmm2, %xmm8 #6.21
addpd %xmm8, %xmm9 #6.21
movaps %xmm9, 32(%rdi,%rcx,8) #6.3
movsd 48(%rsi,%rcx,8), %xmm10 #6.21
movhpd 56(%rsi,%rcx,8), %xmm10 #6.21
mulpd %xmm2, %xmm10 #6.21
addpd %xmm10, %xmm11 #6.21
movaps %xmm11, 48(%rdi,%rcx,8) #6.3
addq $8, %rcx #5.1
cmpq %r8, %rcx #5.1
jl ..B1.15 # Prob 99% #5.1

Answer 5

it depends on how you placed u and b in memory.
if both memory block are far from each other, SSE wouldn't boost much in this scenario.
it is suggested that the array u and b are AOE (array of structure) instead of SOA (structure of array), because you can load both of them into register in single instruction.