// SPDX-License-Identifier: GPL-2.0-or-later // Copyright (C) 2013 Andrea Mazzoleni #include "internal.h" #include "gf.h" /* * Cauchy Matrix Construction for MDS RAID with Arbitrary Parity Levels in GF(2^8) * * This is a RAID implementation operating in the Galois Field GF(2^8) with * the primitive polynomial x^8 + x^4 + x^3 + x^2 + 1 (285 decimal), supporting * up to six parity levels. * * For RAID5 and RAID6, it follows the method described in H. Peter Anvin's * paper "The mathematics of RAID-6" [1]. Please refer to this paper for a * complete explanation. * * Triple parity was first evaluated using an extension of the same approach, * with additional parity coefficients set as powers of 2^-1, defined as: * * P = sum(Di) * Q = sum(2^i * Di) * R = sum(2^-i * Di) for 0 <= i < N * * This approach works efficiently for triple parity because multiplications * and divisions by 2 in GF(2^8) are very fast. It is similar to ZFS RAIDZ3, * which uses powers of 4 instead of 2^-1. * * Unfortunately, this method does not extend reliably beyond triple parity. * Vandermonde matrices do not guarantee that all square submatrices are * nonsingular, which is required to recover every possible combination of * missing disks [2, Chap. 11, Problem 7][2, Chap. 11, Theorem 8]. * * To overcome this limitation, a Cauchy matrix [3][4] is used for parity * computation. A Cauchy matrix ensures that all square submatrices are * nonsingular, so the linear system is always solvable for any combination * of missing disks. This guarantees an MDS code. * * The Cauchy construction below uses a primitive generator g. * * RAID_MODE_CAUCHY_RAID uses: * polynomial 0x11d * generator g = 2 * * RAID_MODE_CAUCHY_AES uses: * polynomial 0x11b * generator g = 3 * * The numerical examples below refer to RAID_MODE_CAUCHY_RAID, * using polynomial 0x11d and generator g = 2. * * How the matrix is obtained: * * The matrix is constructed to match Linux RAID coefficients for the * first two rows, while ensuring that all square submatrices are nonsingular. * * 1. Start by forming a Cauchy matrix with elements defined as 1/(x_i + y_j), * where all x_i and y_j are distinct elements (the textbook definition * of a Cauchy matrix). * * 2. For the first row (j=0), set x_i = g^-i and y_0 = 0, resulting in: * * row j=0 -> 1/(x_i + y_0) = 1/(g^-i + 0) = g^i * * which reproduces the RAID-6 coefficients. * * 3. For subsequent rows (j>0), set y_j = g^j, yielding: * * rows j>0 -> 1/(x_i + y_j) = 1/(g^-i + g^j) * * Since g is primitive and has multiplicative order 255, * g^-i != g^j for the supported indexes where i + j < 255. * * 4. Place a row filled with 1 at the top of the matrix, transforming it * into an Extended Cauchy Matrix. This preserves the nonsingularity * of all square submatrices. * * This first row reproduces the RAID-5 coefficients. * * 5. Adjust each row with a factor so that the first column contains all 1s. * This transformation also preserves the nonsingularity property, * maintaining the MDS code property. * * Concrete example in GF(256) for k=6, m=4: * * First, create a 3x6 Cauchy matrix using x_i = g^-i and y_0 = 0, y_j = g^j for j>0. * For RAID_MODE_CAUCHY_RAID, where g=2, this gives: * * x = { 1, 142, 71, 173, 216, 108 } * y = { 0, 2, 4 } * * The Cauchy matrix is: * * 1 2 4 8 16 32 * 244 83 78 183 118 47 * 167 39 213 59 153 82 * * Divide row 2 by 244 and row 3 by 167. Then extend it with a row of ones * on top, and it remains MDS. This forms the code for m=4, with RAID-6 as * a subset: * * 1 1 1 1 1 1 * 1 2 4 8 16 32 * 1 245 210 196 154 113 * 1 187 166 215 199 7 * * Extending the same computation to k=251 and m=6 gives: * * This results in the matrix A[row, col] defined as: * * 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01... * 01 02 04 08 10 20 40 80 1d 3a 74 e8 cd 87 13 26 4c 98 2d 5a b4 75... * 01 f5 d2 c4 9a 71 f1 7f fc 87 c1 c6 19 2f 40 55 3d ba 53 04 9c 61... * 01 bb a6 d7 c7 07 ce 82 4a 2f a5 9b b6 60 f1 ad e7 f4 06 d2 df 2e... * 01 97 7f 9c 7c 18 bd a2 58 1a da 74 70 a3 e5 47 29 07 f5 80 23 e9... * 01 2b 3f cf 73 2c d6 ed cb 74 15 78 8a c1 17 c9 89 68 21 ab 76 3b... * * This matrix supports six levels of parity, one for each row, for up to 251 * data disks, one for each column. All the 377,342,351,231 square submatrices * are nonsingular, verified also by brute-force testing. * * The construction can be extended to additional parity rows while reducing * the maximum number of data columns accordingly. In GF(2^8), the Extended * Cauchy construction requires data + parity <= 257 * (See mktables.c for more details on how the matrix is generated.) * * The downside is that generic multiplications are required to compute the * parity, not only fast multiplications by 2^n or 2^-n, which negatively * affect performance. However, optimized parallel multiplications using SSSE3 * or AVX2 instructions [1][5] make this approach competitive with the triple * parity computation using power coefficients. * * Another advantage of the Cauchy matrix is that the first two rows can * replicate the RAID5 and RAID6 approach, resulting in a compatible extension. * SSSE3 or AVX2 instructions are only needed for triple parity or beyond. * * Parity computation is as follows: * * P = sum(Di) * Q = sum(g^i * Di) * R = sum(A[2,i] * Di) * S = sum(A[3,i] * Di) * T = sum(A[4,i] * Di) * U = sum(A[5,i] * Di) for 0 <= i < N * * where g=2 for RAID_MODE_CAUCHY_RAID and * g=3 for RAID_MODE_CAUCHY_AES. * * Recovery from six disk failures at indices x, y, z, h, v, w (0 <= x < y < z < h < v < w < N) * involves computing parity of the remaining N-6 disks: * * Pa = sum(Di) * Qa = sum(g^i * Di) * Ra = sum(A[2,i] * Di) * Sa = sum(A[3,i] * Di) * Ta = sum(A[4,i] * Di) * Ua = sum(A[5,i] * Di) for i != x,y,z,h,v,w * * Defining: * * Pd = Pa + P * Qd = Qa + Q * Rd = Ra + R * Sd = Sa + S * Td = Ta + T * Ud = Ua + U * * yields: * * Pd = Dx + Dy + Dz + Dh + Dv + Dw * Qd = g^x * Dx + g^y * Dy + g^z * Dz + g^h * Dh + g^v * Dv + g^w * Dw * Rd = A[2,x] * Dx + A[2,y] * Dy + A[2,z] * Dz + A[2,h] * Dh + A[2,v] * Dv + A[2,w] * Dw * Sd = A[3,x] * Dx + A[3,y] * Dy + A[3,z] * Dz + A[3,h] * Dh + A[3,v] * Dv + A[3,w] * Dw * Td = A[4,x] * Dx + A[4,y] * Dy + A[4,z] * Dz + A[4,h] * Dh + A[4,v] * Dv + A[4,w] * Dw * Ud = A[5,x] * Dx + A[5,y] * Dy + A[5,z] * Dz + A[5,h] * Dh + A[5,v] * Dv + A[5,w] * Dw * * with g=2 for RAID_MODE_CAUCHY_RAID and * g=3 for RAID_MODE_CAUCHY_AES. * * This linear system is always solvable since the coefficient matrix is * nonsingular due to the properties of A[]. * * Example performance on a Intel Core i7-10700 CPU @ 2.90GHz, stripe 256 KiB, 8 data disks: * * int8 int32 int64 sse2 sse2e ssse3 ssse3e avx2 avx2e * gen1 24995 46562 62683 77069 * gen2 8243 15879 25788 27292 44907 * genz 5509 10588 14485 13920 25175 * gen3 1190 13201 14307 27268 * gen4 914 10061 11006 21328 * gen5 746 7970 8828 17040 * gen6 630 6627 7516 14584 * * Values are in MiB/s of data processed by a single thread. * Results can be reproduced using "raid/test/speedtest.c". * * Triple parity with power coefficients "1, 2, 2^-1" is slightly faster than * the Cauchy matrix computation if SSSE3 or AVX2 are available: * * int8 int32 int64 sse2 ssse3 avx2 * genz 2337 2874 10920 18944 * * In conclusion, the use of power coefficients, specifically powers * of 1, 2, and 2^-1, is the best option to implement triple parity on CPUs * without SSSE3 and AVX2. * On modern CPUs with SSSE3 or AVX2, the Cauchy matrix is the best * option because it provides a fast and general approach that works for any * number of parities. * * References: * [1] Anvin, "The mathematics of RAID-6", 2004 * [2] MacWilliams, Sloane, "The Theory of Error-Correcting Codes", 1977 * [3] Blomer, "An XOR-Based Erasure-Resilient Coding Scheme", 1995 * [4] Roth, "Introduction to Coding Theory", 2006 * [5] Plank, "Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions", 2013 */ /** * Inverts the square matrix M of size nxn into V. * * This is not a general matrix inversion because we assume the matrix M * to have all the square submatrix not singular. * We use Gauss elimination to invert. * * @M Matrix to invert with @n rows and @n columns. * @V Destination matrix where the result is put. * @n Number of rows and columns of the matrix. */ void raid_invert(uint8_t *M, uint8_t *V, int n) { int i, j, k; /* set the identity matrix in V */ for (i = 0; i < n; ++i) for (j = 0; j < n; ++j) V[i * n + j] = i == j; /* for each element in the diagonal */ for (k = 0; k < n; ++k) { uint8_t f; /* * The diagonal element cannot be 0 because * we are inverting matrices with all the square * submatrices not singular */ BUG_ON(M[k * n + k] == 0); /* make the diagonal element to be 1 */ f = inv(M[k * n + k]); for (j = 0; j < n; ++j) { M[k * n + j] = mul(f, M[k * n + j]); V[k * n + j] = mul(f, V[k * n + j]); } /* * Make all the elements over and under the diagonal * to be zero */ for (i = 0; i < n; ++i) { if (i == k) continue; f = M[i * n + k]; for (j = 0; j < n; ++j) { M[i * n + j] ^= mul(f, M[k * n + j]); V[i * n + j] ^= mul(f, V[k * n + j]); } } } } /** * Recover failure of one data block for PAR1. * * Starting from the equation: * * Pd = Dx * * and solving we get: * * Dx = Pd */ void raid_rec1of1(int *id, int nd, size_t size, void **v) { void *p; void *pa; /* * For PAR1 we can directly compute the missing block * and we don't need to use the zero buffer */ p = v[nd]; pa = v[id[0]]; /* use the parity as missing data block */ v[id[0]] = p; /* compute the parity over the missing data block */ v[nd] = pa; /* compute */ raid_gen(nd, 1, size, v, 0); /* restore as before */ v[id[0]] = pa; v[nd] = p; }