/rust/registry/src/index.crates.io-6f17d22bba15001f/adler-1.0.2/src/algo.rs
| Line | Count | Source (jump to first uncovered line) | 
| 1 |  | use crate::Adler32; | 
| 2 |  | use std::ops::{AddAssign, MulAssign, RemAssign}; | 
| 3 |  |  | 
| 4 |  | impl Adler32 { | 
| 5 | 0 |     pub(crate) fn compute(&mut self, bytes: &[u8]) { | 
| 6 | 0 |         // The basic algorithm is, for every byte: | 
| 7 | 0 |         //   a = (a + byte) % MOD | 
| 8 | 0 |         //   b = (b + a) % MOD | 
| 9 | 0 |         // where MOD = 65521. | 
| 10 | 0 |         // | 
| 11 | 0 |         // For efficiency, we can defer the `% MOD` operations as long as neither a nor b overflows: | 
| 12 | 0 |         // - Between calls to `write`, we ensure that a and b are always in range 0..MOD. | 
| 13 | 0 |         // - We use 32-bit arithmetic in this function. | 
| 14 | 0 |         // - Therefore, a and b must not increase by more than 2^32-MOD without performing a `% MOD` | 
| 15 | 0 |         //   operation. | 
| 16 | 0 |         // | 
| 17 | 0 |         // According to Wikipedia, b is calculated as follows for non-incremental checksumming: | 
| 18 | 0 |         //   b = n×D1 + (n−1)×D2 + (n−2)×D3 + ... + Dn + n*1 (mod 65521) | 
| 19 | 0 |         // Where n is the number of bytes and Di is the i-th Byte. We need to change this to account | 
| 20 | 0 |         // for the previous values of a and b, as well as treat every input Byte as being 255: | 
| 21 | 0 |         //   b_inc = n×255 + (n-1)×255 + ... + 255 + n*65520 | 
| 22 | 0 |         // Or in other words: | 
| 23 | 0 |         //   b_inc = n*65520 + n(n+1)/2*255 | 
| 24 | 0 |         // The max chunk size is thus the largest value of n so that b_inc <= 2^32-65521. | 
| 25 | 0 |         //   2^32-65521 = n*65520 + n(n+1)/2*255 | 
| 26 | 0 |         // Plugging this into an equation solver since I can't math gives n = 5552.18..., so 5552. | 
| 27 | 0 |         // | 
| 28 | 0 |         // On top of the optimization outlined above, the algorithm can also be parallelized with a | 
| 29 | 0 |         // bit more work: | 
| 30 | 0 |         // | 
| 31 | 0 |         // Note that b is a linear combination of a vector of input bytes (D1, ..., Dn). | 
| 32 | 0 |         // | 
| 33 | 0 |         // If we fix some value k<N and rewrite indices 1, ..., N as | 
| 34 | 0 |         // | 
| 35 | 0 |         //   1_1, 1_2, ..., 1_k, 2_1, ..., 2_k, ..., (N/k)_k, | 
| 36 | 0 |         // | 
| 37 | 0 |         // then we can express a and b in terms of sums of smaller sequences kb and ka: | 
| 38 | 0 |         // | 
| 39 | 0 |         //   ka(j) := D1_j + D2_j + ... + D(N/k)_j where j <= k | 
| 40 | 0 |         //   kb(j) := (N/k)*D1_j + (N/k-1)*D2_j + ... + D(N/k)_j where j <= k | 
| 41 | 0 |         // | 
| 42 | 0 |         //  a = ka(1) + ka(2) + ... + ka(k) + 1 | 
| 43 | 0 |         //  b = k*(kb(1) + kb(2) + ... + kb(k)) - 1*ka(2) - ...  - (k-1)*ka(k) + N | 
| 44 | 0 |         // | 
| 45 | 0 |         // We use this insight to unroll the main loop and process k=4 bytes at a time. | 
| 46 | 0 |         // The resulting code is highly amenable to SIMD acceleration, although the immediate speedups | 
| 47 | 0 |         // stem from increased pipeline parallelism rather than auto-vectorization. | 
| 48 | 0 |         // | 
| 49 | 0 |         // This technique is described in-depth (here:)[https://software.intel.com/content/www/us/\ | 
| 50 | 0 |         // en/develop/articles/fast-computation-of-fletcher-checksums.html] | 
| 51 | 0 | 
 | 
| 52 | 0 |         const MOD: u32 = 65521; | 
| 53 | 0 |         const CHUNK_SIZE: usize = 5552 * 4; | 
| 54 | 0 | 
 | 
| 55 | 0 |         let mut a = u32::from(self.a); | 
| 56 | 0 |         let mut b = u32::from(self.b); | 
| 57 | 0 |         let mut a_vec = U32X4([0; 4]); | 
| 58 | 0 |         let mut b_vec = a_vec; | 
| 59 | 0 | 
 | 
| 60 | 0 |         let (bytes, remainder) = bytes.split_at(bytes.len() - bytes.len() % 4); | 
| 61 | 0 | 
 | 
| 62 | 0 |         // iterate over 4 bytes at a time | 
| 63 | 0 |         let chunk_iter = bytes.chunks_exact(CHUNK_SIZE); | 
| 64 | 0 |         let remainder_chunk = chunk_iter.remainder(); | 
| 65 | 0 |         for chunk in chunk_iter { | 
| 66 | 0 |             for byte_vec in chunk.chunks_exact(4) { | 
| 67 | 0 |                 let val = U32X4::from(byte_vec); | 
| 68 | 0 |                 a_vec += val; | 
| 69 | 0 |                 b_vec += a_vec; | 
| 70 | 0 |             } | 
| 71 | 0 |             b += CHUNK_SIZE as u32 * a; | 
| 72 | 0 |             a_vec %= MOD; | 
| 73 | 0 |             b_vec %= MOD; | 
| 74 | 0 |             b %= MOD; | 
| 75 |  |         } | 
| 76 |  |         // special-case the final chunk because it may be shorter than the rest | 
| 77 | 0 |         for byte_vec in remainder_chunk.chunks_exact(4) { | 
| 78 | 0 |             let val = U32X4::from(byte_vec); | 
| 79 | 0 |             a_vec += val; | 
| 80 | 0 |             b_vec += a_vec; | 
| 81 | 0 |         } | 
| 82 | 0 |         b += remainder_chunk.len() as u32 * a; | 
| 83 | 0 |         a_vec %= MOD; | 
| 84 | 0 |         b_vec %= MOD; | 
| 85 | 0 |         b %= MOD; | 
| 86 | 0 | 
 | 
| 87 | 0 |         // combine the sub-sum results into the main sum | 
| 88 | 0 |         b_vec *= 4; | 
| 89 | 0 |         b_vec.0[1] += MOD - a_vec.0[1]; | 
| 90 | 0 |         b_vec.0[2] += (MOD - a_vec.0[2]) * 2; | 
| 91 | 0 |         b_vec.0[3] += (MOD - a_vec.0[3]) * 3; | 
| 92 | 0 |         for &av in a_vec.0.iter() { | 
| 93 | 0 |             a += av; | 
| 94 | 0 |         } | 
| 95 | 0 |         for &bv in b_vec.0.iter() { | 
| 96 | 0 |             b += bv; | 
| 97 | 0 |         } | 
| 98 |  |  | 
| 99 |  |         // iterate over the remaining few bytes in serial | 
| 100 | 0 |         for &byte in remainder.iter() { | 
| 101 | 0 |             a += u32::from(byte); | 
| 102 | 0 |             b += a; | 
| 103 | 0 |         } | 
| 104 |  |  | 
| 105 | 0 |         self.a = (a % MOD) as u16; | 
| 106 | 0 |         self.b = (b % MOD) as u16; | 
| 107 | 0 |     } | 
| 108 |  | } | 
| 109 |  |  | 
| 110 |  | #[derive(Copy, Clone)] | 
| 111 |  | struct U32X4([u32; 4]); | 
| 112 |  |  | 
| 113 |  | impl U32X4 { | 
| 114 | 0 |     fn from(bytes: &[u8]) -> Self { | 
| 115 | 0 |         U32X4([ | 
| 116 | 0 |             u32::from(bytes[0]), | 
| 117 | 0 |             u32::from(bytes[1]), | 
| 118 | 0 |             u32::from(bytes[2]), | 
| 119 | 0 |             u32::from(bytes[3]), | 
| 120 | 0 |         ]) | 
| 121 | 0 |     } | 
| 122 |  | } | 
| 123 |  |  | 
| 124 |  | impl AddAssign<Self> for U32X4 { | 
| 125 | 0 |     fn add_assign(&mut self, other: Self) { | 
| 126 | 0 |         for (s, o) in self.0.iter_mut().zip(other.0.iter()) { | 
| 127 | 0 |             *s += o; | 
| 128 | 0 |         } | 
| 129 | 0 |     } | 
| 130 |  | } | 
| 131 |  |  | 
| 132 |  | impl RemAssign<u32> for U32X4 { | 
| 133 | 0 |     fn rem_assign(&mut self, quotient: u32) { | 
| 134 | 0 |         for s in self.0.iter_mut() { | 
| 135 | 0 |             *s %= quotient; | 
| 136 | 0 |         } | 
| 137 | 0 |     } | 
| 138 |  | } | 
| 139 |  |  | 
| 140 |  | impl MulAssign<u32> for U32X4 { | 
| 141 | 0 |     fn mul_assign(&mut self, rhs: u32) { | 
| 142 | 0 |         for s in self.0.iter_mut() { | 
| 143 | 0 |             *s *= rhs; | 
| 144 | 0 |         } | 
| 145 | 0 |     } | 
| 146 |  | } |