usecrate::Adler32; use std::ops::{AddAssign, MulAssign, RemAssign};
impl Adler32 { pub(crate) fn compute(&mutself, bytes: &[u8]) { // The basic algorithm is, for every byte: // a = (a + byte) % MOD // b = (b + a) % MOD // where MOD = 65521. // // For efficiency, we can defer the `% MOD` operations as long as neither a nor b overflows: // - Between calls to `write`, we ensure that a and b are always in range 0..MOD. // - We use 32-bit arithmetic in this function. // - Therefore, a and b must not increase by more than 2^32-MOD without performing a `% MOD` // operation. // // According to Wikipedia, b is calculated as follows for non-incremental checksumming: // b = n×D1 + (n−1)×D2 + (n−2)×D3 + ... + Dn + n*1 (mod 65521) // Where n is the number of bytes and Di is the i-th Byte. We need to change this to account // for the previous values of a and b, as well as treat every input Byte as being 255: // b_inc = n×255 + (n-1)×255 + ... + 255 + n*65520 // Or in other words: // b_inc = n*65520 + n(n+1)/2*255 // The max chunk size is thus the largest value of n so that b_inc <= 2^32-65521. // 2^32-65521 = n*65520 + n(n+1)/2*255 // Plugging this into an equation solver since I can't math gives n = 5552.18..., so 5552. // // On top of the optimization outlined above, the algorithm can also be parallelized with a // bit more work: // // Note that b is a linear combination of a vector of input bytes (D1, ..., Dn). // // If we fix some value k<N and rewrite indices 1, ..., N as // // 1_1, 1_2, ..., 1_k, 2_1, ..., 2_k, ..., (N/k)_k, // // then we can express a and b in terms of sums of smaller sequences kb and ka: // // ka(j) := D1_j + D2_j + ... + D(N/k)_j where j <= k // kb(j) := (N/k)*D1_j + (N/k-1)*D2_j + ... + D(N/k)_j where j <= k // // a = ka(1) + ka(2) + ... + ka(k) + 1 // b = k*(kb(1) + kb(2) + ... + kb(k)) - 1*ka(2) - ... - (k-1)*ka(k) + N // // We use this insight to unroll the main loop and process k=4 bytes at a time. // The resulting code is highly amenable to SIMD acceleration, although the immediate speedups // stem from increased pipeline parallelism rather than auto-vectorization. // // This technique is described in-depth (here:)[https://software.intel.com/content/www/us/\ // en/develop/articles/fast-computation-of-fletcher-checksums.html]
letmut a = u32::from(self.a); letmut b = u32::from(self.b); letmut a_vec = U32X4([0; 4]); letmut b_vec = a_vec;
let (bytes, remainder) = bytes.split_at(bytes.len() - bytes.len() % 4);
// iterate over 4 bytes at a time let chunk_iter = bytes.chunks_exact(CHUNK_SIZE); let remainder_chunk = chunk_iter.remainder(); for chunk in chunk_iter { for byte_vec in chunk.chunks_exact(4) { let val = U32X4::from(byte_vec);
a_vec += val;
b_vec += a_vec;
}
b += CHUNK_SIZE as u32 * a;
a_vec %= MOD;
b_vec %= MOD;
b %= MOD;
} // special-case the final chunk because it may be shorter than the rest for byte_vec in remainder_chunk.chunks_exact(4) { let val = U32X4::from(byte_vec);
a_vec += val;
b_vec += a_vec;
}
b += remainder_chunk.len() as u32 * a;
a_vec %= MOD;
b_vec %= MOD;
b %= MOD;
// combine the sub-sum results into the main sum
b_vec *= 4;
b_vec.0[1] += MOD - a_vec.0[1];
b_vec.0[2] += (MOD - a_vec.0[2]) * 2;
b_vec.0[3] += (MOD - a_vec.0[3]) * 3; for &av in a_vec.0.iter() {
a += av;
} for &bv in b_vec.0.iter() {
b += bv;
}
// iterate over the remaining few bytes in serial for &byte in remainder.iter() {
a += u32::from(byte);
b += a;
}
self.a = (a % MOD) as u16; self.b = (b % MOD) as u16;
}
}
Die Informationen auf dieser Webseite wurden
nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit,
noch Qualität der bereit gestellten Informationen zugesichert.
Bemerkung:
Die farbliche Syntaxdarstellung und die Messung sind noch experimentell.