hi,
i decided to have a look at SIMD intrinsics instructions but there is a lt of documentation but i cannot find exemple.
So i decide once again to ask question about how to use SIMD with exemple.
i need only 2 exemple. Than i think a should be able to mixte practique et knowledge.
the first axemple is how to do when (*in1) are INT array . the traitment is inside this append in loop (*in1)[x] - (*in1)[y], the intrincis should be VSUB if i read correctky and VABS. But i need the syntaxe code.
ONE:
int diff1 = std::abs((*in1)[x].raw_col_min - (*in1)[y].raw_col_min); int diff2 = std::abs((*in1)[x].min - (*in1)[y].min); int diff3 = std::abs((*in1)[x].raw_col_max - (*in1)[y].raw_col_max); int diff4 = std::abs((*in1)[x].max - (*in1)[y].max); int diff5 = std::abs((*in1)[x].raw_col_min - (*in1)[y].raw_col_max); int diff6 = std::abs((*in1)[x].min - (*in1)[y].max); int diff7 = std::abs((*in1)[x].raw_col_max - (*in1)[y].raw_col_min); int diff8 = std::abs((*in1)[x].max - (*in1)[y].min);
and
TWO :
int diff1 = std::abs((*in1)[x].raw_col_min - (*in1)[y].raw_col_min); int diff2 = std::abs((*in1)[x].min - (*in1)[y].min); int diff3 = std::abs((*in1)[x].raw_col_max - (*in1)[y].raw_col_max); int diff4 = std::abs((*in1)[x].max - (*in1)[y].max);
FOUR:
int diff1 = std::abs((*in1)[x].raw_col_min - (*in1)[y].raw_col_min); int diff2 = std::abs((*in1)[x].min - (*in1)[y].min);
and how to do
if ( (diff1 < 9 && diff2 < 9 && diff3 < 9 && diff4 < 9) || (diff5 < 5 && diff6 < 5 && diff7 < 5 && diff8 < 5) ){
if ( (diff1 < 9 && diff2 < 9 && diff3 < 9 && diff4 < 9) ){
if ( (diff1 < 9 || diff2 < 9) && (diff3 < 9 || diff4 < 9) ){
i think that would be enough. Than i should be able to find my way. Or i will come back to you. ;))
Thanks a lot in advence.
PS: i work with médiatek 9200+ and Mali-G715-Immortalis MC11 r1p2
hterrolle said:So, SIMD is very good if we do not use conditional check ?
There are two separate issues.
The first issue is just about what percentage of the code can be vectorized. If you have 8 scalar instructions that turn into 2 vector instructions then you go 4x faster. If you have 8 scalar instructions that turn into 1 vector instruction and 4 scalar instructions then you only go 1.6x faster. It doesn't take much serial code to reduce the amount of uplift you get, so good SIMD-friendly algorithms try and stay in SIMD and avoid scalar code as much as possible.
The second issue around use of conditional selects is about branches and branch prediction. Modern CPUs run deep pipelines with a lot of instructions in flight, and rely on branch prediction. For a lot of data-driven algorithms branches that branch off data can be hard to predict, because there isn't a regular pattern in the data. For these unpredictable branches, using conditional selects may often actually need to execute more instructions but avoids the misprediction overhead so runs faster in practice.
hterrolle said:everything using NEON instructions clang should do the work ?
It's a question of effort vs reward. If you really care about optimizing then hand written code can often beat the compiler (especially if you know that your problem allows you to make assumptions the compiler cannot do in a generic way). However, it's more expensive to develop and maintain, and you might be happy with auto-vectorized code because it's lower long-term cost and more portable across different architectures.
HTH, Pete
Yes ! the best way to know is to tried.
So, here are the result betwwen the 2 functions. "doublonBis" my version in bleu and the peter code "neon_code" in red.
for about 100 frames.
I would be interrested by some comment about the graph because SIMD code look not being inpacted by the scalling frequencies ?
here is the last test.
I run both function inside the pthread this time.
Both doublon and neon functions take nrealy the same time. In yelow is the pthread time running alone. The pthread is much more complicated and it is faster.
Is there a limit to phtread instructions ? I will try both doublon and neon functions inside pthread. Let see.
So doublon or neon inside other pthread run faster than 1 pthread + 1 pthread with doublon or neon.
My conclusion would be to use compilator. The first picure a send in the prévious post is when i run 4 time the doublon and 4 time the neon. Dans in this case neon is faster but both are slower than function inside other pthread or function inside pthread.
PS: do not take care of the absysse, only point i a frame.
It is good to know ;))
The only limits are likely to be down to frequency scaling and thermal management - if you run a lot of threads on a lot of cores it can get hot, so frequencies get throttled if it overheats to give the device a chance to cool down