Theory

GPGPU computing refers to using GPUs to perform computations traditionally handled by CPUs. CUDA, developed by NVIDIA, is a parallel computing platform and programming model that allows developers to harness NVIDIA GPUs for general-purpose computing tasks. CUDA enables massive parallelism, making it suitable for high-performance computing applications such as machine learning, deep learning, and scientific simulations.

GPGPUs are well-suited for parallelizable workloads, providing significant speedups compared to CPU-based computing, especially for tasks like matrix operations, vector calculations, and deep learning. Testing GPGPU computing involves ensuring that hardware and software systems are optimized for specific tasks and can efficiently execute required computations. This includes benchmarking the processing power, bandwidth, and efficiency of GPGPU implementations for workloads like matrix multiplications, deep learning models, and data transformations.