Introduction
Nvidia’s CUDA programming platform and software ecosystem has given the company a monopoly in general purpose GPU computing, especially for accelerating machine learning workloads. This article provides an in-depth analysis on how Nvidia achieved its dominant position, the implications of its stranglehold on the market, emerging threats, and the future outlook for greater competition in the space.
The Rise of CUDA
Launched in 2007, CUDA (Compute Unified Device Architecture) allows developers to leverage the parallel processing capabilities of Nvidia GPUs for non-graphics workloads using C/C++. CUDA provides low-level hardware access while abstracting away complex driver details through a straightforward API.
Nvidia’s early mover advantage with a mature GPU programming platform like CUDA perfectly positioned the company to dominate as deep learning started booming years later. AI researchers quickly gravitated to Nvidia GPUs given their order of magnitude speedup over CPUs for training neural networks. The massive parallelism of GPUs was a perfect match for accelerating the math-intensive matrix computations involved in deep learning.
Unlike niche academic languages like OpenCL that targeted high performance…
