NumPy & SciPy for GPU

Python interface to GPU-powered libraries

CUDA matrix multiplication benchmarking on Jetson Orin Nano. Four implementations, three power modes, five matrix sizes. 99.5% mathematical validation. C++/CUDA and Python.

Tools to run and parse MKL verbose mode

Classification of Hyperspectral Images ( HSIs ) with Principal Component Analysis ( PCA ) in CUDA ( cuBLAS ).

bilibili视频【CUDA 12.x 并行编程入门(Python版)】配套代码

Enhanced GPU throttle diagnostic for DGX Spark (GB10): NVML direct telemetry, throttle cause decoder, PCIe link monitoring, baseline drift detection, timeline capture.

🔍 Analyze CUDA matrix multiplication performance and power consumption on NVIDIA Jetson Orin Nano across multiple implementations and settings.

High-performance CUDA implementation of Muon optimizer for LLM training. Features Newton-Schulz polar decomposition, cuBLAS acceleration, and transpose optimization for 8x FLOP savings on transformer FFN layers. Benchmarked on NVIDIA A100 with Llama 3.1 8B architectures (4096×11008 weights).

GPU profiling suite & CUDA kernels on A100 80GB — ResNet-50 benchmarks, Nsight Systems profiling, tiled matrix multiplication with shared memory

NumPy-like API accelerated with CUDA

NVIDIA GPU validation: PCIe transport, Unified Memory prefetch, SGEMM compute, drift detection.