mojo

# Mojo can run Python code directly and call Python libraries.
# Start with Python, optimize hot paths in Mojo.

from python import Python

fn main() raises:
    # Import any Python library
    let np = Python.import_module("numpy")
    let plt = Python.import_module("matplotlib.pyplot")

    # Use NumPy arrays as usual
    let data = np.random.randn(1000)
    let mean = np.mean(data)
    let std = np.std(data)
    print("Mean:", mean, "Std:", std)

    # Plot with matplotlib
    plt.hist(data, bins=30)
    plt.title("Distribution")
    plt.savefig("plot.png")

# matrix.mojo — Custom matrix type with SIMD operations
# This runs 10-100x faster than equivalent NumPy for small matrices.

from math import sqrt
from algorithm import vectorize, parallelize

struct Matrix:
    var data: DTypePointer[DType.float64]
    var rows: Int
    var cols: Int

    fn __init__(inout self, rows: Int, cols: Int):
        self.rows = rows
        self.cols = cols
        self.data = DTypePointer[DType.float64].alloc(rows * cols)
        memset_zero(self.data, rows * cols)

    fn __getitem__(self, row: Int, col: Int) -> Float64:
        return self.data.load(row * self.cols + col)

    fn __setitem__(inout self, row: Int, col: Int, val: Float64):
        self.data.store(row * self.cols + col, val)

    fn __del__(owned self):
        self.data.free()

    fn matmul(self, other: Matrix) -> Matrix:
        """Matrix multiplication using SIMD vectorization.

        Processes multiple floating-point operations per CPU instruction.
        On a modern CPU with 256-bit SIMD, this processes 4 float64 ops
        at once — a 4x speedup over scalar code.
        """
        var result = Matrix(self.rows, other.cols)
        let simd_width = simdwidthof[DType.float64]()

        @parameter
        fn calc_row(row: Int):
            for k in range(self.cols):
                @parameter
                fn dot[simd_width: Int](col: Int):
                    result.data.store[width=simd_width](
                        row * other.cols + col,
                        result.data.load[width=simd_width](row * other.cols + col)
                        + self[row, k] * other.data.load[width=simd_width](k * other.cols + col)
                    )
                vectorize[dot, simd_width](other.cols)

        parallelize[calc_row](self.rows)    # Parallelize across CPU cores
        return result

    fn frobenius_norm(self) -> Float64:
        """Compute the Frobenius norm using SIMD reduction."""
        var sum: Float64 = 0.0
        let simd_width = simdwidthof[DType.float64]()

        @parameter
        fn accumulate[width: Int](idx: Int):
            let vals = self.data.load[width=width](idx)
            sum += (vals * vals).reduce_add()

        vectorize[accumulate, simd_width](self.rows * self.cols)
        return sqrt(sum)

# simd_example.mojo — Process data in parallel lanes
from algorithm import vectorize
from sys.info import simdwidthof

fn relu_activation(inout data: DTypePointer[DType.float32], size: Int):
    """Apply ReLU activation using SIMD.

    Processes 8 float32 values simultaneously on AVX2 CPUs,
    or 16 on AVX-512. The @parameter decorator ensures the
    SIMD width is resolved at compile time.
    """
    let simd_width = simdwidthof[DType.float32]()  # 8 on AVX2

    @parameter
    fn apply_relu[width: Int](idx: Int):
        let values = data.load[width=width](idx)
        let zeros = SIMD[DType.float32, width](0)
        data.store[width=width](idx, values.max(zeros))

    vectorize[apply_relu, simd_width](size)


fn softmax(inout data: DTypePointer[DType.float32], size: Int):
    """Numerically stable softmax with SIMD operations."""
    # Find max for numerical stability
    var max_val: Float32 = data.load(0)
    for i in range(1, size):
        let val = data.load(i)
        if val > max_val:
            max_val = val

    # Compute exp(x - max) and sum
    var sum: Float32 = 0.0
    for i in range(size):
        let exp_val = math.exp(data.load(i) - max_val)
        data.store(i, exp_val)
        sum += exp_val

    # Normalize
    let inv_sum = 1.0 / sum
    let simd_width = simdwidthof[DType.float32]()

    @parameter
    fn normalize[width: Int](idx: Int):
        data.store[width=width](idx, data.load[width=width](idx) * inv_sum)

    vectorize[normalize, simd_width](size)

# parallel.mojo — Multi-core parallel processing
from algorithm import parallelize, vectorize
from time import now

fn parallel_image_processing(
    inout pixels: DTypePointer[DType.uint8],
    width: Int,
    height: Int,
):
    """Apply grayscale conversion in parallel across CPU cores.

    Each row is processed by a different core, and within each row,
    SIMD processes multiple pixels simultaneously.
    """
    @parameter
    fn process_row(row: Int):
        let row_offset = row * width * 3    # 3 channels (RGB)

        for col in range(width):
            let idx = row_offset + col * 3
            let r = pixels.load(idx).cast[DType.float32]()
            let g = pixels.load(idx + 1).cast[DType.float32]()
            let b = pixels.load(idx + 2).cast[DType.float32]()

            # Luminance formula: 0.299R + 0.587G + 0.114B
            let gray = (0.299 * r + 0.587 * g + 0.114 * b).cast[DType.uint8]()
            pixels.store(idx, gray)
            pixels.store(idx + 1, gray)
            pixels.store(idx + 2, gray)

    parallelize[process_row](height)    # Each core handles a subset of rows

# Install Modular CLI
curl -s https://get.modular.com | sh -

# Install Mojo
modular install mojo

# Verify
mojo --version

# Run a Mojo file
mojo run my_program.mojo

# Build a binary
mojo build my_program.mojo -o my_program

Add a real-time collaborative python compatibility to my React app using Mojo.

I have a basic high-performance structs built with custom code. Migrate it to use Mojo for better high-performance structs support.