Modular•2y ago•

4 replies

NuMojo’s got NDArrays! 🥳

NuMojo has just released v0.1 adding many new features the largest of which is an N dimensional array type completely independent of

Tensor

Tensor

. Our

NDArray

NDArray

has many features including
* Native Vectorization of arithmetic dunder operations

+,-.*./

+,-.*./

etc.
* Indexing with both integers and Slices (though mixed requires using

Slice

Slice

explicitly due to Mojo parser limitations).
*

sum

sum

mean

mean

stdev

stdev

both on axis and cumulative.
* A few different

matmul

matmul

implementations.
* Basic integration

trapz

trapz

.
* A few of the array creation routines from numpy:

arange

arange

ones

ones

zeros

zeros

identity

identity

.
* Some Sorting functions:

bubble_sort

bubble_sort

quick_sort

quick_sort

,and

quick_sort_inplace

quick_sort_inplace

.

Our math functions were all migrated to NDArray, and a new

Backend

Backend

was added.

VectorizedParallelizedNWorkers

VectorizedParallelizedNWorkers

which allows the number of workers to be set as a parameter. I have found that setting the number of workers to between half, and three-quarters of the total number of cores provides a speed-up where default parallelization caused a slowdown (on WSL).

For those of you who are new to NuMojo

Backend

Backend

is a trait that defines how calculations get carried out for many of the functions in our math module. The default is

Vectorized

Vectorized

which uses SIMD vectorization. In the future, all numojo functions and data types will implement backends to enable easy switching between computational methods and eventually even hardware accelerators such as GPUs.

Example: The following calculates the square root of an array from 0 to 99, reshaped to 10x10, vectorized, and parallelized with 8 workers.

import numojo as nm
alias backend = nm.VectorizedParallelizedNWorkers[8]
def main():
    var array = nm.arange[nm.f64](0,100)
    array.reshape(10,10)
    var res = nm.sqrt[nm.f64,backend=backend](array)
    print(res)

import numojo as nm
alias backend = nm.VectorizedParallelizedNWorkers[8]
def main():
    var array = nm.arange[nm.f64](0,100)
    array.reshape(10,10)
    var res = nm.sqrt[nm.f64,backend=backend](array)
    print(res)

GitHub

GitHub - Mojo-Numerics-and-Algorithms-group/NuMojo: NuMojo is a lib...

NuMojo is a library for numerical computing in Mojo

similar to numpy in Python. - Mojo-Numerics-and-Algorithms-group/NuMojo

GitHub - Mojo-Numerics-and-Algorithms-group/NuMojo: NuMojo is a lib...

Modular•2y ago•

4 replies

mad alex 1997

NuMojo’s got NDArrays! 🥳

NuMojo has just released v0.1 adding many new features the largest of which is an N dimensional array type completely independent of

Tensor

Tensor

. Our

NDArray

NDArray

has many features including
* Native Vectorization of arithmetic dunder operations

+,-.*./

+,-.*./

etc.
* Indexing with both integers and Slices (though mixed requires using

Slice

Slice

explicitly due to Mojo parser limitations).
*

sum

sum

mean

mean

stdev

stdev

both on axis and cumulative.
* A few different

matmul

matmul

implementations.
* Basic integration

trapz

trapz

.
* A few of the array creation routines from numpy:

arange

arange

ones

ones

zeros

zeros

identity

identity

.
* Some Sorting functions:

bubble_sort

bubble_sort

quick_sort

quick_sort

,and

quick_sort_inplace

quick_sort_inplace

.

Our math functions were all migrated to NDArray, and a new

Backend

Backend

was added.

VectorizedParallelizedNWorkers

VectorizedParallelizedNWorkers

Backend

Backend

is a trait that defines how calculations get carried out for many of the functions in our math module. The default is

Vectorized

Vectorized

import numojo as nm
alias backend = nm.VectorizedParallelizedNWorkers[8]
def main():
    var array = nm.arange[nm.f64](0,100)
    array.reshape(10,10)
    var res = nm.sqrt[nm.f64,backend=backend](array)
    print(res)

import numojo as nm
alias backend = nm.VectorizedParallelizedNWorkers[8]
def main():
    var array = nm.arange[nm.f64](0,100)
    array.reshape(10,10)
    var res = nm.sqrt[nm.f64,backend=backend](array)
    print(res)

GitHub

GitHub - Mojo-Numerics-and-Algorithms-group/NuMojo: NuMojo is a lib...

NuMojo is a library for numerical computing in Mojo

similar to numpy in Python. - Mojo-Numerics-and-Algorithms-group/NuMojo

NuMojo’s got NDArrays! 🥳

NuMojo’s got NDArrays! 🥳

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