Add cache generation and FNV hashing functions
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# Copyright (c) 2018 Status Research & Development GmbH
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# Distributed under the Apache v2 License (license terms are at http://www.apache.org/licenses/LICENSE-2.0).
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import math, number_theory # https://github.com/numforge/number-theory
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import math, sequtils,
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number_theory, # Not on nimble yet: https://github.com/numforge/number-theory
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keccak_tiny,
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zero_functional
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import ./private/primes
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import ./private/[primes, bytes]
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# TODO: consider switching from default int to uint64
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# TODO: Switching from default int to uint64
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# Note: array/seq indexing requires an Ordinal, uint64 are not.
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# So to index arrays/seq we would need to cast uint64 to int anyway ...
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@ -45,6 +48,61 @@ proc get_full_size(block_number: Natural): int {.noSideEffect.}=
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dm.rem == 0 and dm.quot.isPrime):
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result -= 2 * MIX_BYTES
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# ###############################################################################
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# Cache generation
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proc mkcache(cache_size, seed: int): seq[Hash[512]] {.noSideEffect.}=
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let n = cache_size div HASH_BYTES
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# Sequentially produce the initial dataset
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result = newSeq[Hash[512]](n)
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result[0] = sha3_512 seed.asByteArray # TODO: spec is unclear if we interpret integers as array of bytes
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for i in 1 ..< n:
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result[i] = sha3_512 result[i-1].asByteArray
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# Use a low-round version of randmemohash
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for _ in 0 ..< CACHE_ROUNDS:
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for i in 0 ..< n:
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let
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v = asByteArray(result[i])[0].int mod n
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a = result[(i-1+n) mod n].asByteArray
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b = result[v].asByteArray
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result[i] = sha3_512 zip(a, b)-->map(it[0] xor it[1])
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# ###############################################################################
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# Data aggregation function
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const FNV_PRIME = 0x01000193
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proc fnv[T: SomeUnsignedInt or Natural](v1, v2: T): T =
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# Original formula is ((v1 * FNV_PRIME) xor v2) mod 2^32
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# However contrary to Python and depending on the type T,
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# in Nim (v1 * FNV_PRIME) can overflow
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# We can't do 2^32 with an int (only 2^32-1)
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# and in general (a xor b) mod c != (a mod c) xor (b mod c)
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#
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# Thankfully
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# We know that:
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# - (a xor b) and c == (a and c) xor (b and c)
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# - for powers of 2: a mod 2^p == a and (2^p - 1)
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# - 2^32 - 1 == high(uint32)
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const mask: T = 2^32 - 1
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mulmod(v1 and mask, FNV_PRIME.T, (2^32).T) xor (v2 and mask)
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# ###############################################################################
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when isMainModule:
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echo get_full_size(100000)
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echo get_full_size(100000)
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let a = sha3_512 1234.asByteArray
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echo a
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echo zip([0, 1, 2, 3], [10, 20, 30, 40]) --> map(it[0] * it[1]) # [0, 20, 60, 120]
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echo zip(a.asByteArray, a.asByteArray) --> map(it[0] xor it[1])
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@ -0,0 +1,13 @@
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# Copyright (c) 2018 Status Research & Development GmbH
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# Distributed under the Apache v2 License (license terms are at http://www.apache.org/licenses/LICENSE-2.0).
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import keccak_tiny
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proc asByteArray*[T: not (ref|ptr|string)](data: T): array[sizeof(T), byte] =
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## Cast stack allocated types to an array of byte
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cast[type result](data)
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proc asByteArray*(data: Hash[512]): array[64, byte] =
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## Workaround: Nim cannot evaluate size of arrays
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## https://github.com/nim-lang/Nim/issues/5802
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cast[type result](data)
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@ -4,7 +4,7 @@
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# Primality testing. TODO: a scalable implementation (i.e. Miller-Rabin)
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# See https://github.com/mratsim/nim-projecteuler/blob/master/src/lib/primes.nim
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import number_theory # https://github.com/numforge/number-theory
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import number_theory # Not on Nimble yet: https://github.com/numforge/number-theory
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proc isPrime*(x: SomeUnsignedInt): bool {.noSideEffect.}=
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