This article traces the evolution of GPUs from early graphics chips to modern parallel processors, explains their internal pipeline, compares CPU and GPU architectures, and introduces key acceleration frameworks like CUDA and OpenCL for general‑purpose computing.
This article explains what a GPU (Graphics Processing Unit) is, why it differs from a CPU, how it is used through graphics and compute APIs, and explores GPU virtualization techniques such as virtual GPUs, passthrough, and vGPU architectures.
The article surveys how NVIDIA's GPU ecosystem and the Intersect360 study reveal that among the 50 most common high‑performance computing applications, 34 already support GPU acceleration, highlighting the growing importance of GPU computing across scientific, engineering, and business fields.
This article provides a comprehensive, hands‑on tutorial for setting up a CentOS 6.8 server with NVIDIA GPUs, installing Java 8, GCC 4.9.4, Python 2.7, NVIDIA drivers, CUDA 8.0, cuDNN 6.0, Bazel, and finally building and verifying TensorFlow 1.3.0 from source, while documenting common pitfalls and fixes.
This article reviews how GPU memory bandwidth, architecture, and price affect deep‑learning workloads, compares popular NVIDIA models and alternatives, discusses multi‑GPU scaling, and provides practical guidance for selecting the most suitable GPU for various budgets and use cases.
