When you first look at CUDA, the terminology may be a little confusing perhaps for the beginners something like, what’s a Block? What’s a Grid? How does any of it relate to the actual hardware? But here’s the simplest way I found to think about it.

First query your GPU hardware

Before writing any CUDA code, run this to see exactly what your card supports.

#include <stdio.h>

void printGpuInfo() {
    cudaDeviceProp prop;
    cudaGetDeviceProperties(&prop, 0);

    printf("\n========== GPU HARDWARE INFO ==========\n");
    printf("GPU Name                    : %s\n",  prop.name);
    printf("Compute Capability          : %d.%d\n", prop.major, prop.minor);
    printf("Total Global Memory         : %zu MB\n", prop.totalGlobalMem / 1024 / 1024);
    printf("---------------------------------------\n");
    printf("Max Threads per Block       : %d\n",  prop.maxThreadsPerBlock);
    printf("Max Block Dim X             : %d\n",  prop.maxThreadsDim[0]);
    printf("Max Block Dim Y             : %d\n",  prop.maxThreadsDim[1]);
    printf("Max Block Dim Z             : %d\n",  prop.maxThreadsDim[2]);
    printf("---------------------------------------\n");
    printf("Max Grid Dim X              : %d\n",  prop.maxGridSize[0]);
    printf("Max Grid Dim Y              : %d\n",  prop.maxGridSize[1]);
    printf("Max Grid Dim Z              : %d\n",  prop.maxGridSize[2]);
    printf("---------------------------------------\n");
    printf("Warp Size                   : %d\n",  prop.warpSize);
    printf("Multiprocessors (SM count)  : %d\n",  prop.multiProcessorCount);
    printf("Max Threads per SM          : %d\n",  prop.maxThreadsPerMultiProcessor);
    printf("=======================================\n\n");
}

int main() {
    printGpuInfo();
    return 0;
}

Compile and run:

nvcc device_info.cu -o device_info && ./device_info

Output you get:

dp@dp-Katana-GF76-11UG:~/Desktop$ nvcc GpuInfo.cu -o device_info && ./device_info

========== GPU HARDWARE INFO ==========
GPU Name                    : NVIDIA GeForce RTX 3070 Laptop GPU
Compute Capability          : 8.6
Total Global Memory         : 7973 MB
---------------------------------------
Max Threads per Block       : 1024
Max Block Dim X             : 1024
Max Block Dim Y             : 1024
Max Block Dim Z             : 64
---------------------------------------
Max Grid Dim X              : 2147483647
Max Grid Dim Y              : 65535
Max Grid Dim Z              : 65535
---------------------------------------
Warp Size                   : 32
Multiprocessors (SM count)  : 40
Max Threads per SM          : 1536
=======================================

dp@dp-Katana-GF76-11UG:~/Desktop$ 

Understand the physical hardware

Your GPU is not one giant processor. It’s a collection of smaller units nested inside each other.

GPU  (For example: RTX 3070 Laptop)
│
├── SM 0
├── SM 1
├── SM 2       ← 40 Streaming Multiprocessors total
│   ...           "Multiprocessors (SM count) : 40"
└── SM 39
      │
      ├── Warp 0
      ├── Warp 1
      │   ...   ← 1536 ÷ 32 = 48 warp units per SM
      └── Warp 47   "Max Threads per SM : 1536"
            │        "Warp Size : 32"
            │
            ├── Thread 0
            ├── Thread 1
            │   ...        ← always exactly 32 per warp
            └── Thread 31
                  │
                  └── executes kernel → data[idx] = 1.0 / data[idx]
40 SMs  ×  48 warps  ×  32 threads  =  61,440 simultaneous threads

Relate the above with the software layer (CUDA Concepts)

Grid, Block, and Warp are not new hardware. They are names for regions of the hardware tree above.

┌──────────────────────────────────────────────┐
│  GRID  — your full kernel launch             │
│          "Max Grid Dim X : 2,147,483,647"    │
│                                              │
│   ┌──────────────────────────────────────┐   │
│   │  BLOCK  — work assigned to one SM    │   │
│   │  "Max Threads per Block : 1024"      │   │
│   │                                      │   │
│   │   ┌──────────────────────────────┐   │   │
│   │   │  WARP  — 32 threads          │   │   │
│   │   │  "Warp Size : 32"            │   │   │
│   │   │                              │   │   │
│   │   │  T0  T1  T2  T3  ...  T31    │   │   │
│   │   └──────────────────────────────┘   │   │
│   │   Warp 1 ... Warp 47                 │   │
│   └──────────────────────────────────────┘   │
│   Block 1 ... Block 255                      │
└──────────────────────────────────────────────┘
conceptwhat it isyour number
Gridentire launch — all blocks together256 blocks
Blockgroup of threads on one SM256 threads
Warp32 threads the SM runs as one unit8 per block
Threadone element, one computation65,536 total

Step 4 — What Are Dim X, Y, Z?

Your block and grid can be shaped in 1D, 2D or 3D to match the shape of your data.

1D — flat array (your program)
──────────────────────────────
data:  [0.5][1.0][1.5][2.0] ...

Block Dim X = 256,  Y = 1,  Z = 1
Grid  Dim X = 256,  Y = 1,  Z = 1

only X matters — one row of elements


2D — an image
─────────────
image: 1920 columns × 1080 rows

Block Dim X = 32,  Y = 32,  Z = 1  →  32×32 = 1024 threads per block
Grid  Dim X = 60,  Y = 34,  Z = 1

X = columns,  Y = rows,  each thread handles one pixel


3D — a volume
─────────────
scan: 128 × 128 × 64 voxels

Block Dim X = 8,  Y = 8,  Z = 8   →  8×8×8 = 512 threads per block
Grid  Dim X = 16, Y = 16, Z = 8

X = width,  Y = height,  Z = depth,  each thread handles one voxel

The limits from my spec:

dimensionBlock maxGrid maxmaps to
X10242,147,483,647columns / width
Y102465,535rows / height
Z6465,535depth / layers

** Block X × Y × Z must never exceed Max Threads per Block (1024).

Summary

hardware (fixed silicon)             software (you define this)
────────────────────────             ──────────────────────────
GPU chip                        →    Device
40 SMs                          →    runs your Blocks
48 warp units per SM            →    Warp  (always 32 threads)
                                →    Grid  (all your blocks)
                                →    Block (your BLOCK_SIZE)
Thread                          →    Thread
Reference: https://docs.nvidia.com/cuda/cuda-programming-guide/
Reference: https://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf