CuPy
CuPy is an open-source Python library providing a NumPy-compatible interface for GPU-accelerated numerical computing.
Fast Python: High performance techniques for large datasets
- How it is used
- Deploys NumPy-like array analysis on a GPU and provides a case for profiling GPU performance.
- How deep it goes
- Works through array residency, transfers, GPU-aware timing, and Mandelbrot kernels; leaves CUDA C details largely unexplored.
- Examples
- Benchmarks doubling a 5000 × 5000 matrix and generating a Mandelbrot image with an embedded CUDA C kernel.
- What you can do after
- Apply it via CuPy and cupyx.time.repeat to run GPU array operations and measure GPU execution time.
Read how Fast Python: High performance techniques for large datasets explains CuPy →
Starting Data Analytics with Generative AI and Python
CuPy is a Python array-computing library that provides GPU-accelerated operations with an interface modeled on NumPy.
- How it is used
- Accelerates bootstrap confidence-interval calculations by moving compatible array operations from the CPU to a CUDA-enabled GPU.
- How deep it goes
- Demonstrates installation and code conversion, but defers detailed CPU–GPU architecture and compatibility mechanics.
- Examples
- Uses a CUDA bootstrap_CI function, pip installation, CuPy percentile operations, and execution-time comparisons against optimized CPU code.
- What you can do after
- Apply it via CuPy by adapting NumPy-based array code for CUDA and returning results to NumPy when needed.
Read how Starting Data Analytics with Generative AI and Python explains CuPy →
Related concepts
- Numbauses · Numba is a Python compiler that uses just-in-time compilation to generate optimized machine code for numerical code, with support for CPU and GPU targets.
- NumPycontrasts with · NumPy is an open-source Python library for numerical computing that provides multidimensional arrays and operations on them.
- GPGPUuses · GPGPU is the use of graphics processing units to perform general-purpose computations, often by exploiting their parallel processing capabilities.
- GPUuses · A graphics processing unit (GPU) is a processor designed for highly parallel computation, especially graphics and data-parallel workloads.
- CUDAuses · CUDA is NVIDIA’s parallel computing platform and programming model for performing general-purpose computations on NVIDIA GPUs.
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