CuPy

CuPy is a GPU-accelerated array library for Python that provides a NumPy-compatible interface for numerical computing. The library allows users to create and manipulate multi-dimensional arrays and perform mathematical operations using syntax that closely matches the NumPy API. Instead of executing computations on the CPU, CuPy runs operations on the GPU using NVIDIA CUDA, enabling highly parallel execution for numerical workloads.

The main benefit of CuPy is performance. GPUs contain thousands of processing cores designed for parallel workloads, which makes the hardware particularly effective for array operations, matrix calculations, and scientific computing tasks. By replacing NumPy with CuPy in existing Python code, many numerical workflows can be accelerated with minimal code changes while maintaining a familiar programming model.

CuPy also integrates well with the Python scientific computing ecosystem. The library supports common numerical capabilities such as linear algebra, random number generation, Fourier transforms, sparse matrices, and other scientific computing routines. Because the API mirrors NumPy and parts of SciPy, existing CPU-based code can often be migrated to GPU execution by switching the array library.

SwissGPU workstations provide dedicated NVIDIA GPUs running directly on bare-metal hardware, which makes the infrastructure well suited for libraries such as CuPy. Users can install CuPy and run GPU-accelerated numerical workloads directly on the workstation. Because each machine provides exclusive access to the GPU and system resources, CuPy can use the full compute capability of the hardware to accelerate array processing, simulations, and large-scale numerical workloads.

Resources

SwissGPU