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Thank you!

Totally agree 👍

Thanks so much!!

Thank you! I really appreciate that. Glad my video could help.

Thanks! I appreciate that. Happy i could help! 👍

That depends on your workload. If you have a lot of thread divergence, more SMs with fewer ALUs is better. If you have high utilization and less thread divergence, then fewer SMs with more ALUs would be better. As GPU tasks become more complicated, the amount of divergence increases, so making the SMs smaller is a better approach. The raw peak performance only depends on the number of ALUs and clock speed (not the SMs). However, the maximum practical peak performance will highly depend on the number of SMs depending on the workload. I.e. 1 SM with 2048 ALUs at 1 GHz might do 2 TFLOPS. But practically only be able to achieve 40% peak. Whereas 4 SMs with 256 ALUs each might peak out at 1 TFLOPs and be able to achieve 90% peak. 40% vs 90% could be explained completely via thread divergence. Then you have 2*.4=0.8 TFLOPS vs 1*0.9=0.9 TFLOPS. That's an extreme example though, because they are a lot closer in base numbers while being further apart in performance (it has to do with register file pressure, scheduling, work distribution, etc.)

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Himanshu patra Hey, sorry I don't have any more videos. This was just a final project I had to do for school.

Bradon Fredrickson thank you somuch for the quick replay. I thought of asking because some where in the video you told "I have explained CUBA in the prev class". It is such a nice video. Thank you so much. 😊.

True, but they do similar tasks. Albeit, the GPUs cores are more like lots and lots of advanced ALUs with a few special bells and whistles here and there

Thank you

Thanks!!

I think the main difference is the number of ALU's. GPU's have a lot of ALU's and a CPU only had a few.

@@bradonf333 well, they aren't the same though. GPU ALUs are a bit more advanced, and tend to have features like how to calculate shading, if I recall correctly

​@@ithaca2076 That's not quite correct, depending on what you mean. CPUs don't typically have one type of ALU anymore, they have an integer ALU (add, subtract, logic, shifts), a multiplier, a divider, and a FP ALU (which is often divided into a FP ADD/MUL ALU and a FP DIV / SQRT etc ALU). Often those ALUs are combined into common pipelines, for example, an x86 CPU may do {Add, Subtract, Logic, Shifts, and multiplication} in one ALU, and then {Add, Subtract, logic, and division} in another ALU. It also depends on the CPU, some of them have multiply-accumulate instructions, while others don't. If you ignore the addition of a MAC instruction, then a GPU "ALU" is going to be much simpler than most CPU ALUs. The degree to which that's true depends on the GPU architecture, the older ones combined an INT32 and FP32 ALU into a single "core", which may or may not have been unified (i.e. a single pipeline that could do either), or they could have been unique pipelines. The advantage to unique pipelines would be that the latency is lower (fewer cycles) at the cost of area. The current NVidia architectures have a combined FP32/INT32 pipeline, and a FP32 only pipeline. The current AMD architectures have combined FP32 and INT32 pipelines, which is true for the newer ARM Mali GPUs, as well as PowerVR, and Apple's GPUs. Going back to NVidia though, the FP32 ALUs can only do FPADD, FPSUB, FPMUL, FPMAC, FMA, and I think that's also were the Int to FP instructions are done. The INT32 ALUs do integer ADD, SUB, MUL, MAC, Logic, Shift, FPCompare, and I think is where the FP to Int instructions are done. The "Cores" / ALUs don't do anything fancier like SQRT, or Division. The GPU is actually incapable of doing either of those operations, and instead can do approximations to those operations using the Special Function Units (SFU / MFU). Those would not be considered ALUs though. TL;DR The GPU ALUs are in general much simpler than that of a modern (high end) CPU.