GPUaccelerated

GPUaccelerated, often written as GPU-accelerated, refers to the use of graphics processing units (GPUs) to perform computational tasks that would traditionally be handled by the central processing unit (CPU). This approach leverages the parallel architecture of modern GPUs to increase throughput for workloads such as linear algebra, simulations, data analytics, and multimedia processing, where many operations can be executed concurrently.

GPUs contain thousands of smaller cores capable of executing thousands of threads simultaneously. Programs are executed on GPUs through specialized models and libraries, including Nvidia CUDA, OpenCL, and AMD ROCm, as well as graphics pipelines using DirectX or Vulkan compute shaders. Data typically moves between host memory and device memory over a interconnect such as PCIe, so performance depends on memory bandwidth and the efficiency of the kernels and data transfer.

Benefits of GPU acceleration include substantial speedups for parallelizable tasks, improved energy efficiency per operation, and the ability to handle large datasets in real time. Limitations include programming complexity, debugging challenges, and data-transfer overheads; not all algorithms benefit from GPU execution, and results can vary with precision requirements and numerical determinism. There is also vendor dependency and ecosystem maturity to consider.

Common domains for GPU-accelerated workloads include machine learning and deep learning, scientific computing, computer vision, video encoding/decoding, rendering, and scientific simulations. Examples of software and frameworks with GPU acceleration encompass machine learning libraries (TensorFlow, PyTorch with CUDA), image/video processing libraries, and GPU-accelerated rendering tools.