CUDA

GPU Computing for AI: Parallel Processing and Performance Graphics processing units (GPUs) deliver massive parallel power for AI. Instead of one fast CPU core, a modern GPU runs thousands of threads that work on different parts of a workload at the same time. For AI, most tasks are matrix multiplications and tensor operations, which GPUs handle very efficiently. Two main forms of parallelism drive AI systems: data parallelism and model parallelism. Data parallelism splits a batch across devices, so each GPU computes gradients on its slice and then averages results. Model parallelism divides the model itself across GPUs when a single device cannot fit all layers. Many setups combine both to scale training. ...

GPU Computing for Accelerated AI and Visualization Graphics processing units (GPUs) are built to handle many tasks at once. In AI, this parallel power lets you train large neural networks faster and run more experiments with the same time. In visualization, GPUs render scenes, process volume data, and display interactive results in real time. Both AI and visualization benefit from higher throughput and better memory bandwidth. Key advantages include higher throughput for matrix operations, specialized tensor cores in many GPUs, and efficient memory paths. A common rule: keep data on the GPU as much as possible to avoid slow transfers over the PCIe bus. That often means using GPU-accelerated libraries and keeping models and data resident on video memory during training and inference. ...

GPU-Accelerated Computing for Data Science GPU-accelerated computing has become a cornerstone for modern data science. Today’s GPUs offer thousands of cores and wide memory bandwidth, letting you run large matrix operations, train models faster, and explore data with interactive speed. This shift changes workflows from long, fixed runs to quick, iterative experiments. With thoughtful setup, a single GPU can unlock performance that previously required a cluster. Data science workloads shine on GPUs in a few areas: deep learning, large-scale linear algebra, and data preprocessing. Training neural networks benefits from parallel tensor operations; simulations and Monte Carlo methods gain from parallel sampling; data transformations like filtering and normalization can be accelerated with GPU libraries. The key is to keep data on the GPU as much as possible to minimize transfers. ...