This project demonstrates GPU-accelerated matrix multiplication using CUDA, implementing both a basic version and an optimized version using shared memory.

A C++/CUDA project implementing basic matrix operations to demonstrate GPU computing fundamentals. This project serves as a learning ground for CUDA programming concepts and optimization techniques.

NVIDIA is the market leader in AI-focused GPUs, with the Tesla (now A100 and H100) and RTX series optimised for machine ...

However, both the 440 and 426 Hemi units were short-lived in the Cuda. At the end of the 1971 model year, only two years after the third-gen Barracuda's debut, Chrysler discontinued its high ...

The Nvidia GeForce RTX 5080 boasts fantastic DLSS 4 abilities, and the new Founders Edition design makes it one of the nicest ...

As of September 2024, AMD had $4.5 billion in cash and cash equivalents against total debt of $1.7 billion. AMD took on debt to acquire Xilinx, but Xilinx generates healthy cash flow, and now that AMD ...

Explore the incredible AI performance of Llama.cpp when paired with the GeForce RTX 5090. Discover the speed, capabilities, ...

The Nvidia GeForce RTX 5090 is a powerful upgrade to an already overpowered enthusiast-grade GPU, and in a lot of ways, this ...

The AI enigma. The direction in which some AEC software developers are heading raises important questions about workstation investments ...

The NVIDIA RTX 3070 outperforms AMD's RX 6700 XT in most games across all resolutions, but has less VRAM, affecting its performance in VRAM-intensive scenarios. RTX 3070 excels in productivity tasks ...