When you launch a CUDA kernel, you typically launch many threads, not just one. These threads are organized in a hierarchical structure ⤴️
CUDA hierarchy: A grid contains blocks, and each block contains threads.
Each kernel launch, creates one Grid, which contains many Blocks, which in turn contains many Threads. ⤴️
This maps to <<<3, 4>>> in CUDA, which means launching 3 blocks, each containing 4 threads — totaling 12 threads. The two parameters inside the kernel launch syntax <<>> represent the number of blocks in the grid (gridDim) and the number of threads in each block(blockDim). ⤴️
each thread must know exactly which piece of data it’s responsible for. That’s where the global thread index comes in: it gives every thread a unique ID across the entire grid so it can access the correct memory location. ⤴️
to get the global thread index, we can use the blockIdx, blockDim and threadIdx. CUDA gives us built-in variables to retrieve this information:
blockIdx.x → which block (floor) you’re in threadIdx.x → which thread (room) inside the block blockDim.x → how many threads per block (rooms per floor)
Using these, every thread can compute its global ID using:
int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;``` [⤴️](https://omni.dariustan.me/me/https-sanket-pixel-github-io-blog-2025-that-first-cuda-blog-2-197caf76ede#b214488f-684f-4e66-9aad-177a961ba171) ^b214488f
In CUDA, both threads and blocks can be organized in 1D, 2D, or 3D layouts. That means:
Each block can have threads arranged like a line (1D), a grid (2D), or a cube (3D).
Similarly, the grid of blocks itself can follow any of these layouts. ⤴️
Each thread and block has 3 coordinate components:
threadIdx.x, threadIdx.y, threadIdx.z tell you the thread’s position within its block.
blockIdx.x, blockIdx.y, blockIdx.z tell you the block’s position within the grid
blockDim.x, blockDim.y, blockDim.z tell you how many threads are there per block in each direction ⤴️
int x = blockIdx.x * blockDim.x + threadIdx.x; int y = blockIdx.y * blockDim.y + threadIdx.y; ⤴️
int idx = y * total_width + x; // where total_width is the width of the full grid ⤴️
int x = blockIdx.x * blockDim.x + threadIdx.x; int y = blockIdx.y * blockDim.y + threadIdx.y; int z = blockIdx.z * blockDim.z + threadIdx.z; ⤴️
int idx = z * (height * width) + y * width + x; ⤴️
TL;DR: Think of .x, .y, and .z as the coordinates in a virtual 3D thread universe. You use them to uniquely identify and assign work to each thread, especially in problems where your data naturally lives in 2D or 3D. ⤴️
how does this then get assigned to SMs to run compute?
In the earlier 1D example, we used plain integers like <<>>. In this 2D case, we use dim3 to pass 2D configurations — a CUDA struct that lets us naturally map threads to 1D, 2D, or 3D data layouts. ⤴️
Whether 1D or 2D, it’s all about aligning the thread layout to your problem. ⤴️
it’s important to understand a key idea —the CPU (host) and GPU (device) have separate memory spaces. They do not share memory by default and cannot directly access each other’s data. If you want the GPU to work on data from the CPU (or send results back), you must explicitly transfer that data between the two.⤴️
Simply defining an array on the CPU doesn’t make it visible to the GPU. ⤴️
The GPU cannot allocate or manage host memory directly. ⤴️
Data must be copied from host to device before the kernel runs, and back afterward if needed. ⤴️
Memory transfers are relatively expensive compared to computation, so minimizing them is often important for performance. ⤴️