115 Commits

Author SHA1 Message Date
adrien
7d28de2028 Removed comptime for str UnitParser 0.2.1 2026-05-04 23:55:00 +02:00
adrien
eb3b0d4de3 Update .zon 2026-05-04 22:59:54 +02:00
adrien
5bdc78c065 Simplified pow 2026-05-04 22:57:53 +02:00
adrien
18830c8b45 Fixed benchmark 2026-05-04 22:25:18 +02:00
adrien
4595397e70 Removed the feature where you can use comptime int or float ar rhs for operation 2026-05-04 22:10:55 +02:00
adrien
7844aacfce Added a UnitParser to get Dimensions and Scales from a str 2026-05-04 19:10:06 +02:00
adrien
9b6cd4b377 Removed release fast 2026-05-04 14:34:31 +02:00
adrien
4d275dca2d Renamed Tensor to TensorStatic to later introduce TensorAlloc and TensorGPU 2026-04-29 18:07:13 +02:00
adrien
9635cfb481 Changed self: Self to self: *const SElf in tensor for performance 2026-04-28 16:06:13 +02:00
adrien
f0029449f0 tmp 2026-04-28 14:50:08 +02:00
adrien
8816a65518 Now pass all test with new *const way
I am not quite sure about it yet, but it is faster sooo idk.
Let's see long term
2026-04-28 13:51:10 +02:00
adrien
26ff02c50f Changed TEnsor to use *const 2026-04-28 13:10:14 +02:00
adrien
bb6dd59b9a Removed more 2026-04-28 01:02:30 +02:00
adrien
acb908a448 Removed some char 2026-04-28 01:01:40 +02:00
adrien
d4a1054fdc Removed deploy.yaml 2026-04-28 00:58:24 +02:00
adrien
fb2a6f4806 Removed docs/ 2026-04-28 00:57:50 +02:00
adrien
8c77ab86f8 Readded it but also in gitignore 2026-04-28 00:56:42 +02:00
adrien
8565869919 Removed mkdocs.yaml 2026-04-28 00:56:02 +02:00
adrien
1a69b3dbf2 Removed old .md 2026-04-28 00:55:32 +02:00
adrien
cd3632210d Updated README
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0.2.0
2026-04-28 00:45:12 +02:00
adrien
de2e9cce68 Merge branch 'tensor'
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2026-04-27 22:10:02 +02:00
adrien
0b2469d321 . 2026-04-27 22:07:45 +02:00
adrien
c6f613a787 I guess I can't do better. Scalar still suck, but at least it is builtin SIDM so ok I guess 2026-04-27 22:07:39 +02:00
adrien
a0961e7571 Start to optimize the shit out of it, still a long way to go
After that GPPPPPUUUUUU baby!
2026-04-27 21:24:03 +02:00
adrien
168312b78e Removed lots of usless inline and comptime in Scales and Dimensions 2026-04-27 19:09:55 +02:00
AdrienBouvais
698e968ef8 Added a high dimension benchmark 2026-04-27 16:18:31 +02:00
AdrienBouvais
44aaa8a8b2 Removed all inline for (0..total) for either builtin or for loop without inline
This is to prevent giant binary for Tensor with a lot of Scalar
2026-04-27 16:11:46 +02:00
AdrienBouvais
cd954b379b Added cross to Tensor + fix benchmark 2026-04-27 15:13:15 +02:00
AdrienBouvais
16d25e7e7e Added shape comptime check for Tensor add/sub/div/mul 2026-04-27 14:45:41 +02:00
AdrienBouvais
f37a196b15 Fixed new Tensor to be everything (Scalar, Vector, Matrix and above) 2026-04-27 09:11:24 +02:00
Adrien Bouvais
934a40fe1a Basic untested tensor 2026-04-26 22:16:25 +02:00
adrien
1dcfd58189 .
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2026-04-26 01:37:00 +02:00
adrien
0914ea0a60 .
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2026-04-26 01:35:37 +02:00
adrien
37a95607b3 .
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2026-04-26 01:33:37 +02:00
adrien
fe7ee54601 Moved mkdocs.yml to /docs
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2026-04-26 01:31:45 +02:00
81effc9815 Merge pull request 'Replace Scalar and Vector to a single Quantity that use @Vector' (#1) from simd into main
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Reviewed-on: #1
2026-04-25 23:29:30 +00:00
adrien
4a2d45b384 Fix benchmarks to work with new Scalar and Vector 2026-04-26 01:28:33 +02:00
adrien
d32de3fe82 Converted Base test to new Scalar 2026-04-26 01:18:41 +02:00
adrien
58fd4d5797 Fix
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2026-04-26 01:11:51 +02:00
adrien
ae6b069da1 Added simd ref to action
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2026-04-26 01:09:20 +02:00
adrien
4b01dfe412 Replaced Vector with Quantity
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2026-04-26 01:07:12 +02:00
adrien
c350ffd1d6 Working vector initiate
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2026-04-26 00:40:10 +02:00
adrien
a591736b19 Totally replaced Scalar with Quantity
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2026-04-26 00:29:00 +02:00
adrien
89e46f4cd4 .
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2026-04-25 23:29:51 +02:00
adrien
db6aaf433d Added mkdocs 2026-04-25 23:29:06 +02:00
adrien
0257ee5d21 Added Quantity that will be both Scalar and Vector, using only @Vector
I did some benchmark, and @Vector(1, type) is just as fast as type for
float and around 40% slower for int.

But the traide off is ok for me, because for @Vector(>1, type), we are
between 2 and 32x faster! And I dont think single value int will be use
for high performance computing, so I am ok with how simple it make
everything.

Because now I dont have to one struct that use @Vector and one that use
just type, I can just use one. Making it even easier to integrate GPU
later. I will even be able to have a single place in my code with a + -
* and / basically. A single function for all, so I only need to optimize
that one.
2026-04-25 22:36:48 +02:00
adrien
7fc7193206 FLush 2026-04-25 18:57:38 +02:00
adrien
3a4783cfa1 Benchmark SIMD vs Native 2026-04-25 18:47:13 +02:00
adrien
80cdcd134e Added some function to benchmark 2026-04-24 23:50:16 +02:00
adrien
c2675f5117 Added imperial to README 0.1.1 2026-04-23 00:28:49 +02:00