About

⚡ Data manipulation and analysis library in C++ | CUDA GPU + SIMD (AVX2/AVX512/AMX) CPU CPU

Requirements

C++20-compatible compiler:

• gcc 13 or higher
• clang 14 or higher
• Visual Studio 2019 or higher
• CUDA development environment (NVIDIA CUDA Toolkit, and compatible NVIDIA drivers installed) to use CUDA optimizations (nvcc 12 or higher)

Repo

git clone https://github.com/mgorshkov/pd.git

Build library and unit tests

./scripts/build.sh

Build docs

cmake --build . --target doc

Open scipy/build/doc/html/index.html in your browser.

Install

cmake .. -DCMAKE_INSTALL_PREFIX:PATH=~/pd_install
cmake --build . --target install

Usage example (samples/read_csv)

#include <iostream>

#include <pd/read_csv.hpp>

int main(int, char **) {
    using namespace pd;

    auto df = read_csv("https://raw.githubusercontent.com/adityakumar529/Coursera_Capstone/master/diabetes.csv");
    std::cout << "df.shape=" << df.shape() << std::endl;
    const char *non_zero[] = {"Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI"};
    for (const auto &column: non_zero) {
        df[column] = df[column].replace(0L, np::NaN);
        auto mean = df[column].mean(true);
        df[column] = df[column].replace(np::NaN, mean);
    }

    auto X = df.iloc(":", "0:8");
    auto y = df.iloc(":", "8");

    std::cout << "X=" << X << std::endl;
    std::cout << "y=" << y << std::endl;

    return 0;
}

How to build the sample

1. Clone the repo

git clone https://github.com/mgorshkov/pd.git

2. Build the library

./scripts/build.sh

3. cd samples/read_csv

cd samples/read_csv

4. Make build dir

mkdir -p build && cd build

5. Configure cmake

cmake -DCMAKE_BUILD_TYPE=Release ..

6. Build

Linux/MacOS

cmake --build .

Windows

cmake --build . --config Release

6. Run the app

$./read_csv

Links

• ⚡ NumPy-style arrays in C++ | CUDA GPU + SIMD (AVX2/AVX512/AMX) CPU CPU: https://github.com/mgorshkov/np
• ⚡ SciPy methods in C++ | CUDA GPU + SIMD (AVX2/AVX512/AMX) CPU CPU: https://github.com/mgorshkov/scipy
• ⚡ ML methods in C++ | CUDA GPU + SIMD (AVX2/AVX512/AMX) CPU CPU: https://github.com/mgorshkov/sklearn