Blog article explaining Cuckoo Cycle
Cuckoo Cycle is the first graph-theoretic proof-of-work, and the most memory bound, yet with instant verification. Unlike Hashcash, Cuckoo Cycle is immune from quantum speedup by Grover's search algorithm.
With a 42-line complete specification, Cuckoo Cycle is less than half the size of either SHA256, Blake2b, or SHA3 (Keccak) as used in Bitcoin, Equihash and ethash. Simplicity matters.
Proofs take the form of a length 42 cycle in a bipartite graph with 2^N nodes and 2^(N-1) edges, with N ranging from 10 up to 64.
The graph is defined by the siphash-2-4 keyed hash function mapping an edge index and partition side (0 or 1) to the edge endpoint on that side. This makes verification trivial: hash the header to derive the siphash key, then compute the 42x2 edge endpoints with 84 siphashes, check that each endpoint occurs twice, and that you come back to the starting point only after traversing 42 edges.
While trivially verifiable, finding a 42-cycle, on the other hand, is far from trivial, requiring considerable resources, and some luck (the odds of a random cuckoo graph having an L-cycle are approximately 1 in L).
The memory efficient miner uses 3 bits per edge and is bottlenecked by accessing random 2-bit counters, making it memory latency bound. The core of this miner, where over 99% of time is spent, is also relatively simple.
The roughly 4x faster latency avoiding miner uses 33 bits per edge and is bottlenecked by bucket sorting, making it memory bandwidth bound.
Instead of fixing N, a proof-of-work system could allow miners to work on any graph size they like, above a certain minimum. Cycles in larger graphs are more valued as they take more effort to find. We propose to scale the difficulty for a graph on 2^N nodes by 2^N * (N-1), which is the number of bits of siphash output that define the graph.
Dynamic Sizing allows for increasing the memory required for mining simply by raising the minimum allowed graph size. Since this makes new valid blocks a subset of old valid blocks, it is not a hard fork but a soft fork, and thus immensely easier to deploy. Miner manufacturers are incentivized to support larger sizes as being more future proof.
A raise in minimum graph size could be triggered automatically if over the last so many blocks (on the scale of months), less than a certain fraction (a minority like 1/3 or 1/4) were solved at that minimum size. This locks in a new minimum which then activates within so many blocks (on the scale of weeks).
The goal of a memory bound PoW is to make any ASICs face the same memory bottleneck that CPUs and GPUs do. Just as these processing units must go off-chip to access global memory, Cuckoo Cycle aims to deter single-chip ASICs in favor of simpler memory-controller like ASICs that connect commodity memory chips together. Such ASICs only need to run efficient enough to saturate the limited memory bandwidth/latency. With power consumption and cost dominated by that of the memory chips, and the roles of ASIC design skills and fab access diminished, miner manufacturers can hopefully compete on more equal terms.
Adding tiny cuckoo ASICs to devices already equipped with sufficient memory could make for a cost effective mining platform. Integration of a Cuckoo Cycle accelerator on future CPUs/GPUs, obviating the need for a separate chip, would yield the ultimate form of decentralization.
Large scale Cuckoo Cycle mining would drive up demand for low-latency SRAM memory, ultimately making it more affordable as production increases in scale. This could benefit many applications beyond mining. It could also provide a market for faulty memory chips (which need to be indelibly marked as such), which would merely result in a slight degradation of a miner's solution rate.
The most efficient known way to find cycles in random bipartite graphs is to start by eliminating edges that are not part of any cycle (over 99.9% of edges). This preprocessing phase is called edge trimming and actually takes the vast majority of runtime.
The algorithm implemented in lean_miner.hpp runs in time linear in 2^N. (Note that running in sub-linear time is out of the question, as you could only compute a fraction of all edges, and the odds of all 42 edges of a cycle occurring in this fraction are astronomically small). Memory-wise, it uses 2^(N-1) bits to maintain a subset of all edges (potential cycle edges) and 2^N additional bits to trim the subset in a series of edge trimming rounds.
The bandwidth bound algorithm implemented in mean_miner.hpp uses (1+Ε) × 2^(N-1) words to maintain bins of edges instead of a bitmap which it sorts to do trimming.
After edge trimming, an algorithm inspired by Cuckoo Hashing is used to recognise all cycles, and recover those of the right length.
The runtime of a single proof attempt for a 2^30 node graph on a 4GHz i7-4790K is 10.5 seconds with the single-threaded mean solver, using 2200MB (or 3200MB with faster solution recovery). This reduces to 3.5 seconds with 4 threads (3x speedup).
Using an order of magnitude less memory (just under 200MB), the lean solver takes 32.8 seconds per proof attempt. Its multi-threading performance is less impressive though, with 2 threads still taking 25.6 seconds and 4 taking 20.5 seconds.
I claim that siphash-2-4 is a safe choice of underlying hash function, that these implementations are reasonably optimal, that trading off (less) memory for (more) running time, incurs at least one order of magnitude extra slowdown, and finally, that meaner_miner.cu is a reasonably optimal GPU miner. The latter runs about 10x faster on an NVIDA 1080Ti than mean_miner on an Intel Core-i7 CPU. In support of these claims, I offer the following bounties:
$10000 for an open source implementation that finds 42-cycles twice as fast as lean_miner, using no more than 1 byte per edge.
$10000 for an open source implementation that finds 42-cycles twice as fast as mean_miner, regardless of memory use.
$10000 for an open source implementation that uses at most N/k bits while finding 42-cycles up to 10 k times slower, for any k>=2.
All of these bounties require N ranging over {2^28,2^30,2^32} and #threads ranging over {1,2,4,8}, and further assume a high-end Intel Core i7 or Xeon and recent gcc compiler with regular flags as in my Makefile.
$5000 for an open source implementation for a consumer GPU that finds 42-cycles twice as fast as meaner_miner.cu on 2^30 node graphs on comparable hardware.
The Makefile defines corresponding targets leancpubounty, meancpubounty, tmtobounty, and gpubounty.
Improvements by a factor of 4 will be rewarded with double the regular bounty.
In order to minimize the risk of missing out on less drastic improvements, I further offer a fraction FRAC of the regular CPU/GPU-speedup bounty, payable in bitcoin cash, for improvements by a factor of 2^FRAC, where FRAC is at least one-tenth. Note that 2^0.1 is about a 7% improvement.
While both siphash-2-4 and siphash-1-3 pass the smhasher test suite for non-cryptographic hash functions, siphash-1-2, with 1 compression round and only 2 finalization rounds, fails quite badly in the Avalanche department. We invite attacks on Cuckoo Cycle's dependence on its underlying hash function by offering
$5000 for an open source implementation that finds 42-cycles in graphs defined by siphash-1-2 twice as fast as lean_miner on graphs defined by siphash-2-4, using no more than 1 byte per edge.
$5000 for an open source implementation that finds 42-cycles in graphs defined by siphash-1-2 twice as fast as mean_miner on graphs defined by siphash-2-4, regardless of memory use.
These bounties are not subject to double and/or fractional payouts.
Happy bounty hunting!
cd src export LD_LIBRARY_PATH="$PWD:$LD_LIBRARY_PATH" make
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