Create neighborhood: 15.615 sec
80r = Search block 5120872: 0.296 sec
40r = Search block 639449: 0.114 sec
20r = Search block 79971: 0.047 sec
10r = Search block 20031: 0.021 sec
Cleanup: 0.012 sec
40r 4d: sig11
40r 3d: .119 sec
40r 2d: .057 sec
40r 1d: .023 sec
inserting 10000000 random vectors... 29.000 sec
80r = range query returned 2680049 items in 1.02700 sec
40r = range query returned 334901 items in 0.16200 sec
20r = range query returned 41902 items in 0.02000 sec
10r = range query returned 5175 items in 0.00300 sec
cleaned up in 2.32100 sec
Create Kdtree: 12.735sec
80r = Search: 2680049, 0.512sec
40r = Search: 334901, 0.076sec ! crazy, similar speed between 20 and 40 unit radius !
20r = Search: 41902, 0.07sec
10r = Search: 5175, 0.001sec
On the creation of the neighborhood. Reduce the 'inverse' to a single array instead
of one per dimension. This shortens create time from 16.5s to 12.0s. Reduces memory
Create neighborhood: 12.069 sec
Search block 639983: 0.116 sec
Cleanup: 0.011 sec
Area of a unit sphere is ~.52. Validated by the number of items found
in the rectangular search compared to the spherical search of kdtree and nanoflann
Minimize the code inside the search loop. Gives about 20% performance.
Not digging in the right place, but gets the 40radius test to .1 second.
Create neighborhood: 12.076 sec
Search block 639983: 0.09 sec
Cleanup: 0.010 sec
Switched the code to work in 'chunks'. This breaks the array of point numbers
into small sequential ranges. The algorithm wins with these smaller sections.
The other win is that all the array offsets now only index the chunk. By making
the chunk size smaller we can fit the indices into uint8 or a uint16. Cuts a
good 40% off of runtime and really drops memory. We could use still significantly
less memory by using some form of stack to store the results in, currently makes
a size_t array of numPoints size, which means a good 40MB.
Notice the sizes of results is varying here, so there is some problem which could
strike these results as irrelevent. I am highly dubious since these are getting
about half of the results as the unchunked.
Wow, look at the time needed to build the hood. Super fast, and that is (?) valid time.
(Hulk Hogan eat your heart out!)
-- with 10k chunks in size_t index
Create neighborhood: 3.332 sec
Search complete 640842: 0.066 sec
Cleanup: 0.008 sec
-- with 10k chunks in uint16 index
Create neighborhood: 2.942 sec
Search complete 592354: 0.054 sec
Cleanup: 0.010 sec
-- with 255 chunks in uint8 index
Create neighborhood: 0.887 sec
Search complete 332549: 0.053 sec
Cleanup: 0.008 sec
Memory needed to create the 10mill array of points (baseline). So hood and results
are amount of memory over this.
Fixed the incorrectness issues. Create time for uint8 is not <1sec. But I think
it can still be reduced. Want to profile differnt sections first.
-- with 255 chunks in uint8
Create neighborhood: 1.978 sec
Search complete 638857: 0.080 sec
Cleanup: 0.009 sec
-- with 65k chunks in uint16
Create neighborhood: 4.037 sec
Search complete 638857: 0.056 sec
Cleanup: 0.015 sec
-- with 100k chunks in uint32
Create neighborhood: 4.370 sec
Search complete 638857: 0.062 sec
Cleanup: 0.022 sec
The init could still be threaded to cut it down more.
Actually, with the chunking the search and create could be easily threaded by chunk.
With the uint16 indices (and 32k chunks)
10r = Search complete 10034: 0.010 sec
20r = Search complete 79836: 0.025 sec
40r = Search complete 638857: 0.055 sec
80r = Search complete 5118520: 0.100 sec
Next up, speed up the create. Is it the filling of the initial numbered array? If so that may
be sped up using memcpy to duplicate each chunk?
If I break the data into chunks AFTER sorting, could that help things? PERHAPS!
Currently all chunks are finding 20k points on the min axis and resulting in about 4k.
That is from the 65k chunks of uint16.
Perhaps optimizing the uint8 case by finding quicky early outs for scanning sections?
Moving uint16 from 65k chunks to 32k gave speedup to larger search radius, but had no
effect on the 10r and 20r searches.
97% of the search time is spent in the main search loop.
No need to focus on optimizing the bisects.
Searching 2 out of 3 dimensions still takes up most of the time.
This is good, because it confirms intersecting the first two axis
takes the majority of time.
Single axis finds: 3976488 .017 seconds
Second axis finds: 1594896 .042 seconds
All three finds: 638857 .054 seconds
with uint8 and 255 chunks, .026 time is bisecting and .048 is spent in searching (64%)
with uint8 and 64 chunks, .052 time is bisecting and .048 is spent searching (48%)
Note that there are NONE (0) chunks that end up with 0 points in any axis. LAMEO! (40 radius)