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Date: Thu, 25 Dec 2014 11:44:55 -0500
From: Bill Cox <>
To: "" <>
Subject: [OT] Memory hard proof of work with constant time verification

I am not sure if posting off-topic stuff like this here is still OK, but I
read about the John Tromp's Cuckoo Cycle here, and frankly this list has
provide amazing feedback in the past, so I'll keep milking this expert
channel so long as it's not annoying...

Here's an algorithm I'm thinking about on Christmas.  I hope you enjoy it:

This algorithm implements a memory hard proof of work scheme with a
constant time verificatoin capability.  This idea is motivated from:

      Cuckoo Cycle: a memory-bound graph-theoretic proof-of-work system, by
John Tromp

The point is to require enough memory to make it difficult to compute more
cheaply up  using GPUs, FPGAs or ASICs.  This is the goal of various
crypto-coins, such as LiteCoin  and GlobalBoost-Y.  However, these schemes
are forced to use low memory to enable rapid  block-chain verification.

Like Cuckoo Cycle, much larger memory sizes can be supported because
verification of the  proof of work is fast and does not require significant
memory.  In contrast to Cuckoo  Cycle, this algorithm is simpler and
verification is done in constant time rather than  O(log(N)).


For a given initial value, compute H(0 | value), H(1 | value),  H(2 |
value) ... until we have at least a computation cost, c_cost, initial bits
of the latest hash value being equal to the initial c_cost bits of some
previous hash value.  To enable a memory cost,
m_cost, to be independent of c_cost, require that the prior index be within
2^m_cost of the current index.  Verification is done with two calls to H.

There are various ways to remember if a prefix has been seen before, such
as hash tables and Bloom filters.  You can read about Bloom Filters here:

As an example, consider when for m_cost == 25, and c_cost == 60.  We can
implement a Bloom filter with 32 bits per entry, and 2^26 entries in 256
MiB, without significant recomputations.  However, this Bloom filter would
access 32 independent locations in
  DRAM, causing 32 L3 cache misses for every hash value, making the Bloom
filter too slow.  Instead, a hash table of the 60-bit prefixes with their
25 bit indicies should fit nicely in a 128 bit field, and a half full hash
table would take 2^26*16 = 1GiB.  As an additional benefit, the hash table
supports constant time deletion, simplifying implementation of the m_cost

Time/memory trade-offs are possible.  Lower memory can be used by simply
imposing a smaller m_cost, with a proportionally higher computation
requirement. However, the memory*computation time cost does not change,
making this algorithm memory hard.

I'm likely missing some important aspect of what is required for PoW, but
if no major issues are reported, I'll go ahead and code a reference version
just for fun.  Should only take a few hours.... famous last words :-)


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