In CUDA, a thread block is the basic building unit of execution. You always launch your kernels as a collection of thread blocks.

Remember:
𝙂𝙧𝙞𝙙 → 𝘽𝙡𝙤𝙘𝙠 → 𝙒𝙖𝙧𝙥 → 𝙏𝙝𝙧𝙚𝙖𝙙

🌟 A thread block is a group of threads that:

• Execute together on the same Streaming Multiprocessor (SM)
• Share fast on-chip shared memory
• Can synchronize with each other during execution

Although it feels like a software concept, it’s deeply tied to the hardware — shared memory lives inside the SM, and its limits define how many blocks can execute on a SM.

👉 When launching a kernel, you choose the block size — how many threads it contains and in what shape: 1D, 2D, or 3D (to easily map your data layout).

𝙙𝙞𝙢3 𝙗𝙡𝙤𝙘𝙠𝘿𝙞𝙢(8, 8, 8);

The total number of threads = x × y × z and cannot exceed 1024 threads per block on modern GPUs.

🔥 But here’s the catch: block size affects SM occupancy — how many warps (groups of 32 threads) can run in parallel.

• Too big (1024 threads) → fewer blocks fit per SM → lower occupancy
• Too small (32 threads) → you hit the SM’s block limit before using all its available warps → lower occupancy

🧩 Finding the sweet spot is like solving a puzzle — you need to balance threads per block, number of blocks per SM, and resource usage per block to get the best performance.

💡 A good starting point: 128–256 threads per block, then fine-tune with profiling.