The AI Observer • 3 implied HN points • 14 Mar 24
- GPUs are essential for modern compute, especially with the rise of AI workloads like large language models that heavily rely on tensor operations like matrix addition and multiplication.
- When working with GPUs, programmers use CUDA to define functions called kernels that can be launched on the GPU. Parallelism is explicitly defined and optimized, unlike in CPUs where loops iterate serially over data sets.
- Execution on GPUs differs from CPUs due to the minimal cost of GPU hardware threads, efficient thread scheduling at the hardware layer, and the use of warps to execute parallel instructions. GPUs optimize for high throughput with many hardware threads, while CPUs focus on low latency for individual instructions.