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GPUs have high latency, high throughput memory. Random access is a killer if your calculations are at all serialized



The SLIDE algorithm is not serialized. The only issue is the sparsity.


GPUs would still be faster than CPUs. You describe them as high-latency but their memory latency is comparable to CPUs. That's why ethash mining or equihash mining (workloads bottlenecked by short ≤32-byte random memory reads) is still faster on GPUs than on CPUs. Also see https://news.ycombinator.com/item?id=22505029


32-bytes accesses are not short. 8 bytes (double precision floating point) are shorter and that's makes sparse matrix multiplication hard on GPU.

Also, SHA256(d?) employed by ethash is, actually, quite long - 80 cycles, at the very least (cycle per round). In mining you can interleave mining computation for one header with loading required by computation of mining of another header and, from what I know, this is what CUDA on GPU will do.

The sheer amount of compute power makes ethash mining faster on GPU.


Reads shorter than 64 bytes on a CPU all cost you the same: a packet of 64 bytes on the memory bus, because that's the atom size of modern CPU's DDR4 memory controllers...

On GPUs the atom size is 32/64 bytes. So GPUs are always better than or equal to CPUs when it comes to small reads/writes.

It's true that the compute power of ethash is not negligible, but to give you one more data point: on equihash there is even less compute spent on hashing, and GPUs still dominate CPUs




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