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.

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