Adding new built-in backend for RS Vandermonde
This is meant to be used in cases where ISA-L and Jerasure cannot be used.
This commit is contained in:
Kevin Greenan
parent
f3a99e81e9
commit
bf91980e29
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@ -195,6 +195,7 @@ AM_CONDITIONAL(ENABLE_DOXYGEN, test x$enable_doxygen = xyes)
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AC_CONFIG_FILES([\
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src/builtin/null_code/Makefile \
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src/builtin/xor_codes/Makefile \
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src/builtin/rs_vand/Makefile \
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src/Makefile \
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test/Makefile \
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doc/Makefile \
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@ -0,0 +1,45 @@
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/*
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* Copyright 2015 Kevin M Greenan
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* Redistributions in binary form must reproduce the above copyright notice, this
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* list of conditions and the following disclaimer in the documentation and/or
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* other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY
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* THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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* EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
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* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
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* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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* vi: set noai tw=79 ts=4 sw=4:
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*/
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// DISCLAIMER: This is a totally basic implementation of RS used if a user does not
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// want to install one of the supported backends, such as Jerasure and ISA-L.
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// This is not expected to perform as well as the other supported backends,
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// but does not make any assumptions about the host system. Using a library
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// like Jerasure with GF-Complete will give users the ability to tune to their
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// architecture (Intel or ARM), CPU and memory (lots of options).
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// We are only implementing w=16 here. If you want to use something
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// else, then use Jerasure with GF-Complete or ISA-L.
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#define PRIM_POLY 0x1100b
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#define FIELD_SIZE (1 << 16)
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#define GROUP_SIZE (FIELD_SIZE - 1)
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void rs_galois_init_tables();
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void rs_galois_deinit_tables();
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int rs_galois_mult(int x, int y);
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int rs_galois_div(int x, int y);
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int rs_galois_inverse(int x);
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@ -0,0 +1,44 @@
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/*
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* Copyright 2015 Kevin M Greenan
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* Redistributions in binary form must reproduce the above copyright notice, this
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* list of conditions and the following disclaimer in the documentation and/or
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* other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY
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* THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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* EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
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* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
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* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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* vi: set noai tw=79 ts=4 sw=4:
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*/
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int* create_non_systematic_vand_matrix(int k, int m);
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void free_systematic_matrix(int *matrix);
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int* make_systematic_matrix(int k, int m);
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int is_missing(int *missing_idxs, int index_to_check);
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int gaussj_inversion(int *matrix, int *inverse, int n);
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int get_non_zero_diagonal(int *matrix, int row, int num_rows, int num_cols);
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int rs_galois_div(int x, int y);
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int rs_galois_inverse(int x);
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int rs_galois_mult(int x, int y);
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void init_rs_vand(int k, int m);
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void deinit_rs_vand();
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void print_matrix(int *matrix, int rows, int cols);
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void square_matrix_multiply(int *m1, int *m2, int *prod, int n);
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int create_decoding_matrix(int *gen_matrix, int *dec_matrix, int *missing_idxs, int k, int m);
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int is_identity_matrix(int *matrix, int n);
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int internal_rs_vand_encode(int *generator_matrix, char **data, char **parity, int k, int m, int blocksize);
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int internal_rs_vand_decode(int *generator_matrix, char **data, char **parity, int k, int m, int *missing, int blocksize, int rebuild_parity);
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int internal_rs_vand_reconstruct(int *generator_matrix, char **data, char **parity, int k, int m, int *missing, int destination_idx, int blocksize);
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@ -1,10 +1,11 @@
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SUBDIRS = builtin/xor_codes builtin/null_code
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SUBDIRS = builtin/xor_codes builtin/null_code builtin/rs_vand
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lib_LTLIBRARIES = liberasurecode.la
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INCLUDES = \
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-I$(top_srcdir)/include/erasurecode \
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-I$(top_srcdir)/include/xor_codes \
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-I$(top_srcdir)/include/rs_vand \
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-I$(top_srcdir)/include/shss
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# liberasurecode params
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@ -30,7 +31,8 @@ liberasurecodeinclude_HEADERS = \
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liberasurecode_la_CPPFLAGS = -Werror @GCOV_FLAGS@
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liberasurecode_la_LIBADD = \
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builtin/xor_codes/libXorcode.la -lpthread -lm @GCOV_LDFLAGS@
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builtin/xor_codes/libXorcode.la -lpthread -lm @GCOV_LDFLAGS@ \
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builtin/rs_vand/liberasurecode_rsvand.la -lpthread -lm @GCOV_LDFLAGS@
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# Version format (C - A).(A).(R) for C:R:A input
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liberasurecode_la_LDFLAGS = -rpath '$(libdir)' -version-info 1:7:0
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@ -0,0 +1,10 @@
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lib_LTLIBRARIES = liberasurecode_rsvand.la
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# liberasurecode_rsvand params
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liberasurecode_rsvand_la_SOURCES = rs_galois.c rs_vand_internal.c
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liberasurecode_rsvand_la_CPPFLAGS = -I$(top_srcdir)/include/rs_vand @GCOV_FLAGS@
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# Version format (C - A).(A).(R) for C:R:A input
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liberasurecode_rsvand_la_LDFLAGS = @GCOV_LDFLAGS@ -rpath '$(libdir)' -version-info 1:1:0
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MOSTLYCLEANFILES = *.gcda *.gcno *.gcov
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@ -0,0 +1,101 @@
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/*
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* Copyright 2015 Kevin M Greenan
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* Redistributions of source code must retain the above copyright notice, this
|
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* list of conditions and the following disclaimer.
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*
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* Redistributions in binary form must reproduce the above copyright notice, this
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* list of conditions and the following disclaimer in the documentation and/or
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* other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY
|
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* THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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* EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
|
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* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
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* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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* vi: set noai tw=79 ts=4 sw=4:
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*/
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// DISCLAIMER: This is a totally basic implementation of RS used if a user does not
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// want to install one of the supported backends, such as Jerasure and ISA-L.
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// This is not expected to perform as well as the other supported backends,
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// but does not make any assumptions about the host system. Using a library
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// like Jerasure with GF-Complete will give users the ability to tune to their
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// architecture (Intel or ARM), CPU and memory (lots of options).
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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// We are only implementing w=16 here. If you want to use something
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// else, then use Jerasure with GF-Complete or ISA-L.
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#define PRIM_POLY 0x1100b
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#define FIELD_SIZE (1 << 16)
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#define GROUP_SIZE (FIELD_SIZE - 1)
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int *log_table = NULL;
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int *ilog_table = NULL;
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int *ilog_table_begin = NULL;
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void rs_galois_init_tables()
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{
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log_table = (int*)malloc(sizeof(int)*FIELD_SIZE);
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ilog_table_begin = (int*)malloc(sizeof(int)*FIELD_SIZE*3);
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int i = 0;
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int x = 1;
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for (i = 0; i < GROUP_SIZE; i++) {
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log_table[x] = i;
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ilog_table_begin[i] = x;
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ilog_table_begin[i + GROUP_SIZE] = x;
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ilog_table_begin[i + (GROUP_SIZE*2)] = x;
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x = x << 1;
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if (x & FIELD_SIZE) {
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x ^= PRIM_POLY;
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}
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}
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ilog_table = &ilog_table_begin[GROUP_SIZE];
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}
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void rs_galois_deinit_tables()
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{
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free(log_table);
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free(ilog_table_begin);
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}
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int rs_galois_mult(int x, int y)
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{
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int sum;
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if (x == 0 || y == 0) return 0;
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// This can 'overflow' beyond 255. This is
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// handled by positive overflow of ilog_table
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sum = log_table[x] + log_table[y];
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return ilog_table[sum];
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}
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int rs_galois_div(int x, int y)
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{
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int diff;
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if (x == 0) return 0;
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if (y == 0) return -1;
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// This can 'underflow'. This is handled
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// by negative overflow of ilog_table
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diff = log_table[x] - log_table[y];
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return ilog_table[diff];
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}
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int rs_galois_inverse(int x)
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{
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return rs_galois_div(1, x);
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}
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@ -0,0 +1,553 @@
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/*
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* Copyright 2015 Kevin M Greenan
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* Redistributions in binary form must reproduce the above copyright notice, this
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* list of conditions and the following disclaimer in the documentation and/or
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* other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY
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* THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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* EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
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* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
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* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
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* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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* vi: set noai tw=79 ts=4 sw=4:
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*/
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// DISCLAIMER: This is a totally basic implementation of RS used if a user does not
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// want to install one of the supported backends, such as Jerasure and ISA-L.
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// This is not expected to perform as well as the other supported backends,
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// but does not make any assumptions about the host system. Using a library
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// like Jerasure with GF-Complete will give users the ability to tune to their
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// architecture (Intel or ARM), CPU and memory (lots of options).
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <stdint.h>
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#include <rs_galois.h>
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#include <rs_vand_internal.h>
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#include <unistd.h>
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#include <fcntl.h>
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void print_matrix(int *matrix, int rows, int cols)
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{
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int i, j;
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printf("\n");
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for (i = 0; i < rows; i++) {
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for (j = 0; j < cols; j++) {
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printf("%d ", matrix[(i * cols) + j]);
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}
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printf("\n");
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}
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printf("\n");
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}
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void square_matrix_multiply(int *m1, int *m2, int *prod, int n)
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{
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int i, j, k;
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for (i = 0; i < n; i++) {
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for (j = 0; j < n; j++) {
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int p = 0;
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for (k = 0; k < n; k++) {
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p ^= rs_galois_mult(m1[(j*n)+k], m2[(k*n)+i]);
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}
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prod[(j*n)+i] = p;
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}
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}
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}
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int is_identity_matrix(int *matrix, int n)
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{
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int i, j;
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for (i = 0; i < n; i++) {
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for (j = 0; j < n; j++) {
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int val = matrix[(i*n) + j];
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if (i != j) {
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if (val != 0) {
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return 0;
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}
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} else {
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if (val != 1) {
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return 0;
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}
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}
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}
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}
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return 1;
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}
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int* get_matrix_row(int *matrix, int row_idx, int num_cols)
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{
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return &matrix[row_idx * num_cols];
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}
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void copy_row(int *from_matrix, int *to_matrix, int from_row_idx, int to_row_idx, int num_cols)
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{
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int *from_row = get_matrix_row(from_matrix, from_row_idx, num_cols);
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int *to_row = get_matrix_row(to_matrix, to_row_idx, num_cols);
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memcpy(to_row, from_row, sizeof(int)*num_cols);
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}
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int is_missing(int *missing_idxs, int index_to_check)
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{
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int i = 0;
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while (missing_idxs[i] > -1) {
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if (missing_idxs[i] == index_to_check) {
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return 1;
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}
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i++;
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}
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return 0;
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}
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int create_decoding_matrix(int *gen_matrix, int *dec_matrix, int *missing_idxs, int k, int m)
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{
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int i, j;
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int n = k+m;
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for (i = 0, j = 0; i < n && j < k; i++) {
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if (!is_missing(missing_idxs, i)) {
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copy_row(gen_matrix, dec_matrix, i, j, k);
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j++;
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}
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}
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return j == k;
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}
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void init_rs_vand(int k, int m)
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{
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rs_galois_init_tables();
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}
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void deinit_rs_vand(int k, int m)
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{
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rs_galois_deinit_tables();
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}
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int * create_non_systematic_vand_matrix(int k, int m)
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{
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int rows = k + m;
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int cols = k;
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int i, j, acc;
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int *matrix = (int*)malloc(sizeof(int)*rows*cols);
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if (NULL == matrix) return NULL;
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// First row is 1, 0, 0, ..., 0
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matrix[0] = 1;
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for (i = 1; i < cols; i++) matrix[i] = 0;
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// Other rows are:
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// i^0 (=1), i^1, i^2, ..., i^(cols-1)
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for (i = 1; i < rows; i++) {
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acc = 1;
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for (j = 0; j < cols; j++) {
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matrix[i * cols + j] = acc;
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acc = rs_galois_mult(acc, i);
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}
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}
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return matrix;
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}
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// Swap the entries of two rows in a matrix
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void swap_matrix_rows(int *r1, int *r2, int num_cols)
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{
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int i;
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int tmp;
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for (i = 0; i < num_cols; i++) {
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tmp = r1[i];
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r1[i] = r2[i];
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r2[i] = tmp;
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}
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}
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void col_mult(int *matrix, int elem, int col_idx, int num_rows, int num_cols)
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{
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int i;
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for (i = 0; i < num_rows; i++) {
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matrix[col_idx] = rs_galois_mult(matrix[col_idx], elem);
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col_idx += num_cols;
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}
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}
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void row_mult(int *matrix, int elem, int row_idx, int num_rows, int num_cols)
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{
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int i, to_row = row_idx * num_cols;
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for (i = 0; i < num_cols; i++) {
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matrix[to_row] = rs_galois_mult(matrix[to_row], elem);
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to_row++;
|
||||
}
|
||||
}
|
||||
|
||||
void col_mult_and_add(int *matrix, int elem, int from_col, int to_col, int num_rows, int num_cols)
|
||||
{
|
||||
int i;
|
||||
|
||||
for (i = 0; i < num_rows; i++) {
|
||||
matrix[to_col] = matrix[to_col] ^ rs_galois_mult(matrix[from_col], elem);
|
||||
from_col += num_cols;
|
||||
to_col += num_cols;
|
||||
}
|
||||
}
|
||||
|
||||
void row_mult_and_add(int *matrix, int elem, int from_row, int to_row, int num_rows, int num_cols)
|
||||
{
|
||||
int i;
|
||||
from_row = from_row * num_cols;
|
||||
to_row = to_row * num_cols;
|
||||
for (i = 0; i < num_cols; i++) {
|
||||
matrix[to_row] = matrix[to_row] ^ rs_galois_mult(matrix[from_row], elem);
|
||||
to_row++;
|
||||
from_row++;
|
||||
}
|
||||
}
|
||||
|
||||
int get_non_zero_diagonal(int *matrix, int row, int num_rows, int num_cols)
|
||||
{
|
||||
int i, row_idx;
|
||||
|
||||
row_idx = (num_cols * row) + row;
|
||||
for (i = row; i < num_rows; i++) {
|
||||
if (matrix[row_idx] != 0) {
|
||||
return i;
|
||||
}
|
||||
row_idx += num_cols;
|
||||
}
|
||||
|
||||
return -1;
|
||||
}
|
||||
|
||||
int * make_systematic_matrix(int k, int m)
|
||||
{
|
||||
int rows = k + m;
|
||||
int cols = k;
|
||||
int i, j;
|
||||
int *matrix = create_non_systematic_vand_matrix(k, m);
|
||||
|
||||
if (NULL == matrix) return NULL;
|
||||
|
||||
// The first row is already 1, 0, 0, ..., 0
|
||||
for (i = 1; i < cols; i++) {
|
||||
int diag_idx = ((cols*i) + i);
|
||||
// Get next row candidate, whose diagonal entry @ i,i != 0
|
||||
int next_row = get_non_zero_diagonal(matrix, i, rows, cols);
|
||||
|
||||
// Swap candidate row with row i, if needed
|
||||
if (next_row != i) {
|
||||
swap_matrix_rows(&matrix[next_row*cols], &matrix[i*cols], cols);
|
||||
}
|
||||
|
||||
// Ensure the leading entry of row i is 1 by multiplying the
|
||||
// column by the inverse of matrix[diag_idx]
|
||||
if (matrix[diag_idx] != 1) {
|
||||
col_mult(matrix, rs_galois_inverse(matrix[diag_idx]), i, rows, cols);
|
||||
}
|
||||
|
||||
// Zero-out all non-zero, non-diagonal entries in row i
|
||||
// by multiplying the corresponding columns by col-i*<row_value>
|
||||
for (j = 0; j < cols; j++) {
|
||||
int row_val = matrix[(i * cols) + j];
|
||||
if (i != j && row_val != 0) {
|
||||
col_mult_and_add(matrix, row_val, i, j, rows, cols);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Create all-XOR parity as first row of parity submatrix
|
||||
for (i = 0; i < cols; i++) {
|
||||
int row_val = matrix[(cols * cols) + i];
|
||||
if (row_val != 1) {
|
||||
// Multiply the parity sub-column by the inverse of row_val
|
||||
// We then implicitly multuply row i by the inverse of row_val
|
||||
// (not explicitly necessary, since all other entries are 0)
|
||||
col_mult(&matrix[cols*cols], rs_galois_inverse(row_val), i, rows - cols, cols);
|
||||
}
|
||||
}
|
||||
|
||||
return matrix;
|
||||
}
|
||||
|
||||
void free_systematic_matrix(int *matrix)
|
||||
{
|
||||
free(matrix);
|
||||
}
|
||||
|
||||
int gaussj_inversion(int *matrix, int *inverse, int n)
|
||||
{
|
||||
int i, j;
|
||||
|
||||
// Zero out the inverse matrix
|
||||
memset(inverse, 0, sizeof(int)*n*n);
|
||||
|
||||
// Make the inverse matrix an identity matrix
|
||||
for (i = 0; i < n; i++) {
|
||||
int diag_idx = ((n*i) + i);
|
||||
inverse[diag_idx] = 1;
|
||||
}
|
||||
|
||||
for (i = 0; i < n; i++) {
|
||||
int diag_idx = ((n*i) + i);
|
||||
// Get next row candidate, whose diagonal entry @ i,i != 0
|
||||
int next_row = get_non_zero_diagonal(matrix, i, n, n);
|
||||
|
||||
// Swap candidate row with row i, if needed
|
||||
if (next_row != i) {
|
||||
swap_matrix_rows(&matrix[next_row*n], &matrix[i*n], n);
|
||||
swap_matrix_rows(&inverse[next_row*n], &inverse[i*n], n);
|
||||
}
|
||||
|
||||
// Make the leading entry a '1'
|
||||
if (matrix[diag_idx] != 1) {
|
||||
int leading_val_inv = rs_galois_inverse(matrix[diag_idx]);
|
||||
row_mult(matrix, leading_val_inv, i, n, n);
|
||||
row_mult(inverse, leading_val_inv, i, n, n);
|
||||
}
|
||||
|
||||
// Zero-out all other entries in column i
|
||||
for (j = 0; j < n; j++) {
|
||||
if (i != j) {
|
||||
int val = matrix[(j * n) + i];
|
||||
row_mult_and_add(matrix, val, i, j, n, n);
|
||||
row_mult_and_add(inverse, val, i, j, n, n);
|
||||
}
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
void region_xor(char *from_buf, char *to_buf, int blocksize)
|
||||
{
|
||||
int i;
|
||||
|
||||
uint32_t *_from_buf = (uint32_t*)from_buf;
|
||||
uint32_t *_to_buf = (uint32_t*)to_buf;
|
||||
int adj_blocksize = blocksize / 4;
|
||||
int trailing_bytes = blocksize % 4;
|
||||
|
||||
for (i = 0; i < adj_blocksize; i++) {
|
||||
_to_buf[i] = _to_buf[i] ^ _from_buf[i];
|
||||
}
|
||||
|
||||
for (i = blocksize-trailing_bytes; i < blocksize; i++) {
|
||||
to_buf[i] = to_buf[i] ^ from_buf[i];
|
||||
}
|
||||
}
|
||||
|
||||
void region_multiply(char *from_buf, char *to_buf, int mult, int xor, int blocksize)
|
||||
{
|
||||
int i;
|
||||
uint16_t *_from_buf = (uint16_t*)from_buf;
|
||||
uint16_t *_to_buf = (uint16_t*)to_buf;
|
||||
int adj_blocksize = blocksize / 2;
|
||||
int trailing_bytes = blocksize % 2;
|
||||
|
||||
if (xor) {
|
||||
for (i = 0; i < adj_blocksize; i++) {
|
||||
_to_buf[i] = _to_buf[i] ^ (uint16_t)rs_galois_mult(_from_buf[i], mult);
|
||||
}
|
||||
|
||||
if (trailing_bytes == 1) {
|
||||
i = blocksize - 1;
|
||||
to_buf[i] = to_buf[i] ^ (char)rs_galois_mult(from_buf[i], mult);
|
||||
}
|
||||
} else {
|
||||
for (i = 0; i < adj_blocksize; i++) {
|
||||
_to_buf[i] = (uint16_t)rs_galois_mult(_from_buf[i], mult);
|
||||
}
|
||||
|
||||
if (trailing_bytes == 1) {
|
||||
i = blocksize - 1;
|
||||
to_buf[i] = (char)rs_galois_mult(from_buf[i], mult);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void region_dot_product(char **from_bufs, char *to_buf, int *matrix_row, int num_entries, int blocksize)
|
||||
{
|
||||
int i;
|
||||
|
||||
for (i = 0; i < num_entries; i++) {
|
||||
int mult = matrix_row[i];
|
||||
if (mult == 1) {
|
||||
region_xor(from_bufs[i], to_buf, blocksize);
|
||||
} else {
|
||||
region_multiply(from_bufs[i], to_buf, mult, 1, blocksize);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int internal_rs_vand_encode(int *generator_matrix, char **data, char **parity, int k, int m, int blocksize)
|
||||
{
|
||||
int i;
|
||||
int n = k + m;
|
||||
|
||||
for (i = k; i < n; i++) {
|
||||
memset(parity[i - k], 0, blocksize);
|
||||
region_dot_product(data, parity[i - k], &generator_matrix[(i * k)], k, blocksize);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
char **get_first_k_available(char **data, char **parity, int *missing, int k)
|
||||
{
|
||||
int i, j;
|
||||
char **first_k_available = (char**)malloc(sizeof(char*)*k);
|
||||
|
||||
for (i = 0, j = 0; j < k; i++) {
|
||||
if (!missing[i]) {
|
||||
first_k_available[j] = i < k ? data[i] : parity[i - k];
|
||||
j++;
|
||||
}
|
||||
}
|
||||
return first_k_available;
|
||||
}
|
||||
|
||||
int internal_rs_vand_decode(int *generator_matrix, char **data, char **parity, int k, int m, int *missing, int blocksize, int rebuild_parity)
|
||||
{
|
||||
int *decoding_matrix = NULL;
|
||||
int *inverse_decoding_matrix = NULL;
|
||||
char **first_k_available = NULL;
|
||||
int n = m + k;
|
||||
int *_missing = (int*)malloc(sizeof(int)*n);
|
||||
int i = 0;
|
||||
int num_missing = 0;
|
||||
|
||||
memset(_missing, 0, sizeof(int)*n);
|
||||
|
||||
while (missing[num_missing] > -1) {
|
||||
_missing[missing[num_missing]] = 1;
|
||||
num_missing++;
|
||||
}
|
||||
|
||||
if (num_missing > m) {
|
||||
free(_missing);
|
||||
return -1;
|
||||
}
|
||||
|
||||
decoding_matrix = (int*)malloc(sizeof(int)*k*k);
|
||||
inverse_decoding_matrix = (int*)malloc(sizeof(int)*k*k);
|
||||
first_k_available = get_first_k_available(data, parity, _missing, k);
|
||||
|
||||
create_decoding_matrix(generator_matrix, decoding_matrix, missing, k, m);
|
||||
gaussj_inversion(decoding_matrix, inverse_decoding_matrix, k);
|
||||
|
||||
// Rebuild data fragments
|
||||
for (i = 0; i < k; i++) {
|
||||
// Data fragment i is missing, recover it
|
||||
if (_missing[i]) {
|
||||
region_dot_product(first_k_available, data[i], &inverse_decoding_matrix[(i * k)], k, blocksize);
|
||||
}
|
||||
}
|
||||
|
||||
// Rebuild parity fragments
|
||||
if (rebuild_parity) {
|
||||
for (i = k; i < n; i++) {
|
||||
// Parity fragment i is missing, recover it
|
||||
if (_missing[i]) {
|
||||
region_dot_product(data, parity[i - k], &generator_matrix[(i * k)], k, blocksize);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
free(decoding_matrix);
|
||||
free(inverse_decoding_matrix);
|
||||
free(first_k_available);
|
||||
free(_missing);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
int internal_rs_vand_reconstruct(int *generator_matrix, char **data, char **parity, int k, int m, int *missing, int destination_idx, int blocksize)
|
||||
{
|
||||
int *decoding_matrix = NULL;
|
||||
int *inverse_decoding_matrix = NULL;
|
||||
char **first_k_available = NULL;
|
||||
int *parity_row = NULL;
|
||||
int n = k + m;
|
||||
int *_missing = (int*)malloc(sizeof(int)*n);
|
||||
int i, j;
|
||||
int num_missing = 0;
|
||||
|
||||
memset(_missing, 0, sizeof(int)*n);
|
||||
|
||||
while (missing[num_missing] > -1) {
|
||||
_missing[missing[num_missing]] = 1;
|
||||
num_missing++;
|
||||
}
|
||||
|
||||
if (num_missing > m) {
|
||||
free(_missing);
|
||||
return -1;
|
||||
}
|
||||
|
||||
decoding_matrix = (int*)malloc(sizeof(int)*k*k);
|
||||
inverse_decoding_matrix = (int*)malloc(sizeof(int)*k*k);
|
||||
first_k_available = get_first_k_available(data, parity, _missing, k);
|
||||
|
||||
create_decoding_matrix(generator_matrix, decoding_matrix, missing, k, m);
|
||||
gaussj_inversion(decoding_matrix, inverse_decoding_matrix, k);
|
||||
|
||||
// Rebuilding data is easy, just do a dot product using the inverted decoding
|
||||
// matrix
|
||||
if (destination_idx < k) {
|
||||
region_dot_product(first_k_available, data[destination_idx], &inverse_decoding_matrix[(destination_idx * k)], k, blocksize);
|
||||
} else {
|
||||
// Rebuilding parity is a little tricker, we first copy the corresp. parity row
|
||||
// and update it to reconstruct the parity with the first k available elements
|
||||
|
||||
// Copy the parity entries for available data elements
|
||||
// from the original generator matrix
|
||||
parity_row = (int*)malloc(sizeof(int)*k);
|
||||
memset(parity_row, 0, sizeof(int)*k);
|
||||
j = 0;
|
||||
for (i = 0; i < k; i++) {
|
||||
if (!_missing[i]) {
|
||||
parity_row[j] = generator_matrix[(destination_idx * k) + i];
|
||||
j++;
|
||||
}
|
||||
}
|
||||
|
||||
i = 0;
|
||||
// For each missing data element, we substitute in the row (equation) for the data element into the
|
||||
// the parity row.
|
||||
while (missing[i] > -1) {
|
||||
if (missing[i] < k) {
|
||||
for (j = 0; j < k; j++) {
|
||||
parity_row[j] ^= rs_galois_mult(generator_matrix[(destination_idx * k) + missing[i]], inverse_decoding_matrix[(missing[i] * k) + j]);
|
||||
}
|
||||
}
|
||||
i++;
|
||||
}
|
||||
region_dot_product(first_k_available, parity[destination_idx - k], parity_row, k, blocksize);
|
||||
}
|
||||
|
||||
free(decoding_matrix);
|
||||
free(inverse_decoding_matrix);
|
||||
free(first_k_available);
|
||||
free(_missing);
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue