I like this writeup as it summarizes my journey with optimizing some cuda code I wrote for an LHC experiment trigger. But there are few comments on some details.
There are 65536 registers per SM not thread block and while you can indirectly control that by making your block takes all the SM but this presents its own problems.
NVIDIA hardware limits the threads max number to 1024 (2048) and shared memory to 48 KB (64 KB) per SM. So if you consume all of that in one thread block or near the maximum then you are using one thread block per SM. You don't usually want to do that because it will lower your occupancy. Additionaly , If the kernel you’re running is not compute-bound and does not need all the registers or shared memory allocated to it, having fewer blocks on the SM could leave some compute resources idle. GPUs are designed to thrive on parallelism, and limiting the number of active blocks could cause underutilization of the SM’s cores, leading to poor performance. Finally, If each thread block occupies an entire SM, you limit the scalability of your kernel to the number of SMs on the GPU. For example, if your GPU has 60 SMs, and each block uses one SM, you can only run 60 blocks in parallel, even if the problem you’re solving could benefit from more parallelism. This can reduce the efficiency of the GPU for very large problem sizes.
There are 65536 registers per SM not thread block and while you can indirectly control that by making your block takes all the SM but this presents its own problems.
NVIDIA hardware limits the threads max number to 1024 (2048) and shared memory to 48 KB (64 KB) per SM. So if you consume all of that in one thread block or near the maximum then you are using one thread block per SM. You don't usually want to do that because it will lower your occupancy. Additionaly , If the kernel you’re running is not compute-bound and does not need all the registers or shared memory allocated to it, having fewer blocks on the SM could leave some compute resources idle. GPUs are designed to thrive on parallelism, and limiting the number of active blocks could cause underutilization of the SM’s cores, leading to poor performance. Finally, If each thread block occupies an entire SM, you limit the scalability of your kernel to the number of SMs on the GPU. For example, if your GPU has 60 SMs, and each block uses one SM, you can only run 60 blocks in parallel, even if the problem you’re solving could benefit from more parallelism. This can reduce the efficiency of the GPU for very large problem sizes.