Comments (3)
@crertel , here is the bench for matrix multiplication.
GraphMath goes second to Matrex, but deviation from the average is huge.
Surely, if you use dedicated SIMD operations for 3 & 4 element vectors you can beat up everyone!
##### With input 3x3 #####
Name ips average deviation median 99th %
Matrex 3624.36 K 0.28 μs ±35.11% 0.26 μs 0.51 μs
GraphMath 1310.16 K 0.76 μs ±247.15% 0.70 μs 1.70 μs
Matrix 372.58 K 2.68 μs ±47.31% 2.40 μs 6.20 μs
Numexy 89.72 K 11.15 μs ±128.04% 9 μs 30 μs
ExMatrix 35.76 K 27.96 μs ±47.28% 24 μs 67 μs
Comparison:
Matrex 3624.36 K
GraphMath 1310.16 K - 2.77x slower
Matrix 372.58 K - 9.73x slower
Numexy 89.72 K - 40.40x slower
ExMatrix 35.76 K - 101.35x slower
from matrex.
Hi!
Thank you.
Surely will do, I am curious also.
from matrex.
Woohoo!
Yeah, the next step in this was opaque blobs + SIMD.
I wish the BEAM folks actually acknowledged packed doubles/floats as a datatype :(
from matrex.
Related Issues (16)
- Use :nan | :inf | :neg_inf instead of NaN | Inf | NegInf
- Talking to Numpy HOT 4
- Diagonal and Exponentials HOT 4
- Rounding numbers HOT 3
- Patter matching arrays problem HOT 3
- Do not use :io.columns on inspect HOT 1
- delete boobs function HOT 1
- Compilation Error on macOS 10.15 Catalina HOT 3
- Floating point issue for larger numbers HOT 3
- call to heatmap() results in ** (CompileError) console:1 "undefined function heatmap/1" HOT 1
- Benchmark against Numpy HOT 12
- Retrieve neighbours HOT 1
- Dirty schedulers and reductions HOT 2
- Support of different data types and multiple dimensions HOT 8
- Tag the releases in github HOT 1
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from matrex.