This project integrates a custom CUDA-based matrix multiplication kernel into a PyTorch deep learning model, leveraging GPU acceleration for matrix operations. The goal is to compare the performance of this custom kernel with PyTorch's built-in matrix multiplication and demonstrate how custom CUDA kernels can optimize compute-intensive operations.
Raspberry Pi Pico (RP2040) and Adafruit Metro M7 (NXP IMXRT10XX) benchmark
The provided code is a Python script that uses the CuPy library to perform optimized GPU operations, specifically matrix multiplication. The script includes a custom CUDA kernel that is optimized for performance and energy consumption. The kernel uses half-precision floating-point numbers (float16) for improved performance and warp utilization.
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