GPUs are designed to handle graphics-related tasks, perform general-purpose computations, and accelerate machine learning algorithms.

CUDA, OpenCL, and DirectCompute are the three main programming models used for GPU computing.

GPUs have more cores than CPUs, are designed for parallel processing, and have a higher clock speed than CPUs.

CUDA cores are specialized processing units designed to perform floating-point operations efficiently.

The GPU's memory hierarchy is designed to store data and instructions for the GPU, provide fast access to frequently used data, and reduce memory latency.

GDDR5, GDDR6, and HBM2 are all types of memory commonly used in GPUs.

GPU's compute units are responsible for executing instructions, performing arithmetic and logical operations, and handling data movement.

GPU's streaming multiprocessors group compute units together, improve resource utilization, and increase parallelism.

GPU's warp scheduler assigns threads to compute units, manages the execution of threads, and resolves data dependencies.

GPU's shared memory stores data shared by threads in a warp, reduces memory latency, and improves communication between threads.

GPU's constant memory stores data that is read-only, reduces memory bandwidth usage, and improves performance for frequently accessed data.

GPU's texture memory stores textures for graphics rendering, improves the performance of texture-intensive applications, and reduces memory bandwidth usage.

GPU's register file stores frequently used data, reduces memory latency, and improves performance for arithmetic and logical operations.

GPU's instruction cache stores frequently executed instructions, reduces memory latency, and improves performance for control flow instructions.

GPU's data cache stores frequently accessed data, reduces memory latency, and improves performance for data-intensive applications.