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Old 2019-10-22, 12:02   #12
jasonp
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To be clear, I've read the paper and didn't see anything actionable. The problem with GPU SpMV is not that we don't know how to do one but that NFS matrices are so sparse that the GPU spends most of its time waiting for memory. Maybe 90% of the memory accesses are random and that is independent of block format.

You can implement the SpMV in GF(2) as a prefix sum with a little postprocessing; GPUs are awesome at prefix sums but try it on an NFS matrix and you get 5% of the documented prefix sum performance.

As a performance datapoint: back in the day we compared a K20 running the msieve-lacuda branch to the Ivy Bridge it was plugged into, for a moderately large problem; the K20 finished in half the time, which is great, but a K20 should be able to add a zero to the Ivy Bridge throughput.

Last fiddled with by jasonp on 2019-10-22 at 12:15
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