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Everything about CuPy
Numerical programming library for the Python programming language
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: CuPyA 5-minute orientation
- Skim a recent paper: CPDDA: A Python Package for Discrete Dipole Approximation Accelerated by CuPyInfluential work since 2022
- Pick up a classic book: Mlle. Camargo.The most-reprinted book on the subject
- Read the landmark paper: Thermal dissociation processes for clathrates [CuPy4(No3)2]·2g (G=tetrahydrofurane, chloroform)Cited 38 times
- Go sideways: CUDAA closely related topic
- Go sideways: NumPyA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Thermal dissociation processes for clathrates [CuPy4(No3)2]·2g (G=tetrahydrofurane, chloroform)
Solid-State Synthesis of 1,3-Selenazoles Employing CuPy2Cl2as a Lewis Acid Catalyst
CuPy2Cl2: A Novel and Efficient Catalyst for Synthesis of Propargylamines Under the Conventional Method and Microwave Irradiation
Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport
2D Simplified Wildfire Spreading Model in Python: From NumPy to CuPy
Parallel computing on GPU with CuPy and vectorized SpMV for large-scale topology optimization
ASTRA kernelkit: GPU-accelerated projectors for computed tomography using cupy
Efficient Coupled-Cluster Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture
🆕 Influential since 2022
CPDDA: A Python Package for Discrete Dipole Approximation Accelerated by CuPy
Evaluating multi-GPU computing capabilities of Numba and CuPy
Benchmarking CUDA, CuPy, and Triton Kernel Optimizations for 3D Point Cloud Segmentation: An Empirical Comparison of Latency, Memory Efficiency, and GPU Utilization
📚 Books worth reading
- 📕Mlle. Camargo.M. Montagu-Nathan · 1932
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- ROCm ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains, including general-purpose…
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