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Date: Thu, 27 Feb 2014 09:13:52 -0500
From: Bill Cox <>
Subject: Re: [PHC] GPU multiplication speed?

On Thu, Feb 27, 2014 at 8:41 AM,  <> wrote:
> I think newer Nvidia chips could run 16 instructions in parallel in a
> multicore, for a latency down to about 11 cycles, but I have not tried it.

Thanks for the great description!  That's very helpful.  If I
understand correctly, you are suggesting that a modern Nvidia chip on
a single thread can do a multiply that depends on the result of the
previous multiply every 11-ish cycles (possibly 24x24 rather than
32x32), and that all instructions take this long.  So computing:

    (a*b ^ c)*d ^e

should have a latency of 4*11 == 44-ish cycles, but each core can do
32 of these in parallel.  Is that right?

> If you want to make things awkward and difficult for GPU, good methods
> include using a very large state (the number of free registers in a GPU is
> large but not infinite), or to force memory usage (e.g. with accesses at
> irregular addresses, like in RC4 encryption: these cannot be easily mapped
> to registers; actual RAM must be used). Each multicore has access to some
> "local RAM" (like 4 kB) that supports parallel access to some extent, but
> all intertwined computations must share that amount: if you need to run
> 192 parallel instances on a multicore, then each instance only has 20 or
> 30 bytes to play with efficiently; everything beyond will incur accesses
> to external RAM, and these won't be parallel.

Given this description, I would guess that Script is sped up on GPUs
only for small memories, like the 128KiB used in LiteCoin.  Large
memory hashes, such as 1GiB would be limited by memory space on the
card, and the fact that instructions take something like 11-ish ns
each.  If that's the case, GPUs should be very bad at large memory
sized Script hashing.  Is this right?

Also, on a GPU, there's nothing special about multiply (maybe 32 bit
vs 24 is helpful by).  Any memory hashing algorithm will slow down a
GPU so long as there are enough sequential operations that depend on
the previous result.  However, SHA-256, Blake2, and other hash
functions have a lot of parallelism, so a GPU can interleave
instructions that don't act on results from the prior few
instructions, reducing the latency impact.

If this is correct, wouldn't an effective strategy be to have a long
chain of sequential operations that depend on the prior result, and to
hash a lot of memory?  It wouldn't have to be multiply - anything
would do, such as a chain of addition, rotation, and XOR.  If I did
this, causing an 11-ish cycle effective computation time, and hashed 1
GiB, then a modern Nvidia GPU with 12GiB would only be able to compute
12 hashes in parallel due to memory limitations, and his 11-cycle
latency vs my 1-ish cycles for simple operations should limit his
advantage to maybe 2x-ish?  If I run 2 threads, than would it be a
1x-ish non-advantage?


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