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hashcat/OpenCL/m11100_a0.cl

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Initial commit
2015-12-04 14:47:52 +00:00
/**
Allow and support vector-width 16, which is current maximum for OpenCL. Closes #226.
2016-02-16 15:42:08 +00:00
* Authors.....: Jens Steube <jens.steube@gmail.com>
* magnum <john.magnum@hushmail.com>
*
Initial commit
2015-12-04 14:47:52 +00:00
* License.....: MIT
*/
#define _MD5_
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
#define NEW_SIMD_CODE
Initial commit
2015-12-04 14:47:52 +00:00
#include "include/constants.h"
#include "include/kernel_vendor.h"
#define DGST_R0 0
#define DGST_R1 3
#define DGST_R2 2
#define DGST_R3 1
#include "include/kernel_functions.c"
Fix path to includes
2016-01-03 00:48:05 +00:00
#include "OpenCL/types_ocl.c"
#include "OpenCL/common.c"
Replace the substring GPU to a more appropriate "device" or "kernel" substring depending on the context
2016-01-05 07:26:44 +00:00
#include "include/rp_kernel.h"
Fix path to includes
2016-01-03 00:56:41 +00:00
#include "OpenCL/rp.c"
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
#include "OpenCL/simd.c"
Initial commit
2015-12-04 14:47:52 +00:00
Prepare _a0 kernel for SIMD
2016-02-01 20:06:56 +00:00
#if VECT_SIZE == 1
#define uint_to_hex_lower8(i) (u32x) (l_bin2asc[(i)])
#elif VECT_SIZE == 2
#define uint_to_hex_lower8(i) (u32x) (l_bin2asc[(i).s0], l_bin2asc[(i).s1])
#elif VECT_SIZE == 4
#define uint_to_hex_lower8(i) (u32x) (l_bin2asc[(i).s0], l_bin2asc[(i).s1], l_bin2asc[(i).s2], l_bin2asc[(i).s3])
#elif VECT_SIZE == 8
#define uint_to_hex_lower8(i) (u32x) (l_bin2asc[(i).s0], l_bin2asc[(i).s1], l_bin2asc[(i).s2], l_bin2asc[(i).s3], l_bin2asc[(i).s4], l_bin2asc[(i).s5], l_bin2asc[(i).s6], l_bin2asc[(i).s7])
Allow and support vector-width 16, which is current maximum for OpenCL. Closes #226.
2016-02-16 15:42:08 +00:00
#elif VECT_SIZE == 16
#define uint_to_hex_lower8(i) (u32x) (l_bin2asc[(i).s0], l_bin2asc[(i).s1], l_bin2asc[(i).s2], l_bin2asc[(i).s3], l_bin2asc[(i).s4], l_bin2asc[(i).s5], l_bin2asc[(i).s6], l_bin2asc[(i).s7], l_bin2asc[(i).s8], l_bin2asc[(i).s9], l_bin2asc[(i).sa], l_bin2asc[(i).sb], l_bin2asc[(i).sc], l_bin2asc[(i).sd], l_bin2asc[(i).se], l_bin2asc[(i).sf])
Prepare _a0 kernel for SIMD
2016-02-01 20:06:56 +00:00
#endif
Initial commit
2015-12-04 14:47:52 +00:00
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_m04 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
* base
Initial commit
2015-12-04 14:47:52 +00:00
*/
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
const u32 gid = get_global_id (0);
Initial commit
2015-12-04 14:47:52 +00:00
const u32 lid = get_local_id (0);
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
const u32 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
* bin2asc table
Initial commit
2015-12-04 14:47:52 +00:00
*/
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
__local u32 l_bin2asc[256];
for (u32 i = lid; i < 256; i += lsz)
{
const u32 i0 = (i >> 0) & 15;
const u32 i1 = (i >> 4) & 15;
l_bin2asc[i] = ((i0 < 10) ? '0' + i0 : 'a' - 10 + i0) << 8
| ((i1 < 10) ? '0' + i1 : 'a' - 10 + i1) << 0;
}
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* base
*/
Initial commit
2015-12-04 14:47:52 +00:00
- Dropped all vector code since new GPU's are all scalar, makes the code much easier - Some performance on low-end GPU may drop because of that, but only for a few hash-modes - Dropped scalar code (aka warp) since we do not have any vector datatypes anymore - Renamed C++ overloading functions memcat32_9 -> memcat_c32_w4x4_a3x4 - Still need to fix kernels to new function names, needs to be done manually - Temperature Management needs to be rewritten partially because of conflicting datatypes names - Added code to create different codepaths for NV on AMD in runtime in host (see data.vendor_id) - Added code to create different codepaths for NV on AMD in runtime in kernels (see IS_NV and IS_AMD) - First tests working for -m 0, for example - Great performance increases in general for NV so far - Tested amp_* and markov_* kernel - Migrated special NV optimizations for rule processor
2015-12-15 11:04:22 +00:00
u32 pw_buf0[4];
Initial commit
2015-12-04 14:47:52 +00:00
pw_buf0[0] = pws[gid].i[ 0];
pw_buf0[1] = pws[gid].i[ 1];
pw_buf0[2] = pws[gid].i[ 2];
pw_buf0[3] = pws[gid].i[ 3];
- Dropped all vector code since new GPU's are all scalar, makes the code much easier - Some performance on low-end GPU may drop because of that, but only for a few hash-modes - Dropped scalar code (aka warp) since we do not have any vector datatypes anymore - Renamed C++ overloading functions memcat32_9 -> memcat_c32_w4x4_a3x4 - Still need to fix kernels to new function names, needs to be done manually - Temperature Management needs to be rewritten partially because of conflicting datatypes names - Added code to create different codepaths for NV on AMD in runtime in host (see data.vendor_id) - Added code to create different codepaths for NV on AMD in runtime in kernels (see IS_NV and IS_AMD) - First tests working for -m 0, for example - Great performance increases in general for NV so far - Tested amp_* and markov_* kernel - Migrated special NV optimizations for rule processor
2015-12-15 11:04:22 +00:00
u32 pw_buf1[4];
Initial commit
2015-12-04 14:47:52 +00:00
pw_buf1[0] = pws[gid].i[ 4];
pw_buf1[1] = pws[gid].i[ 5];
pw_buf1[2] = pws[gid].i[ 6];
pw_buf1[3] = pws[gid].i[ 7];
const u32 pw_len = pws[gid].pw_len;
/**
* challenge
*/
u32 challenge;
challenge = salt_bufs[salt_pos].salt_buf[0];
/**
* salt
*/
u32 salt_buf0[4];
salt_buf0[0] = salt_bufs[salt_pos].salt_buf[ 1]; // not a bug
salt_buf0[1] = salt_bufs[salt_pos].salt_buf[ 2];
salt_buf0[2] = salt_bufs[salt_pos].salt_buf[ 3];
salt_buf0[3] = salt_bufs[salt_pos].salt_buf[ 4];
u32 salt_buf1[4];
salt_buf1[0] = salt_bufs[salt_pos].salt_buf[ 5];
salt_buf1[1] = salt_bufs[salt_pos].salt_buf[ 6];
salt_buf1[2] = salt_bufs[salt_pos].salt_buf[ 7];
salt_buf1[3] = salt_bufs[salt_pos].salt_buf[ 8];
const u32 salt_len = salt_bufs[salt_pos].salt_len - 4;
/**
* loop
*/
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
for (u32 il_pos = 0; il_pos < il_cnt; il_pos += VECT_SIZE)
Initial commit
2015-12-04 14:47:52 +00:00
{
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w0[4] = { 0 };
u32x w1[4] = { 0 };
u32x w2[4] = { 0 };
u32x w3[4] = { 0 };
Initial commit
2015-12-04 14:47:52 +00:00
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
const u32x out_len = apply_rules_vect (pw_buf0, pw_buf1, pw_len, rules_buf, il_pos, w0, w1);
Initial commit
2015-12-04 14:47:52 +00:00
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w0_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0_t[0] = salt_buf0[0];
w0_t[1] = salt_buf0[1];
w0_t[2] = salt_buf0[2];
w0_t[3] = salt_buf0[3];
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w1_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1_t[0] = salt_buf1[0];
w1_t[1] = salt_buf1[1];
w1_t[2] = salt_buf1[2];
w1_t[3] = salt_buf1[3];
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w2_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2_t[0] = 0;
w2_t[1] = 0;
w2_t[2] = 0;
w2_t[3] = 0;
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w3_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3_t[0] = 0;
w3_t[1] = 0;
w3_t[2] = 0;
w3_t[3] = 0;
/*
* append the salt
*/
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
switch_buffer_by_offset_le_VV (w0_t, w1_t, w2_t, w3_t, out_len);
Initial commit
2015-12-04 14:47:52 +00:00
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
const u32x pw_salt_len = out_len + salt_len;
Initial commit
2015-12-04 14:47:52 +00:00
w0_t[0] |= w0[0];
w0_t[1] |= w0[1];
w0_t[2] |= w0[2];
w0_t[3] |= w0[3];
w1_t[0] |= w1[0];
w1_t[1] |= w1[1];
w1_t[2] |= w1[2];
w1_t[3] |= w1[3];
w2_t[0] |= w2[0];
w2_t[1] |= w2[1];
w2_t[2] |= w2[2];
w2_t[3] |= w2[3];
w3_t[0] |= w3[0];
w3_t[1] |= w3[1];
w3_t[2] = pw_salt_len * 8;
w3_t[3] = 0;
/*
* md5 ($pass.$salt)
*/
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x a = MD5M_A;
u32x b = MD5M_B;
u32x c = MD5M_C;
u32x d = MD5M_D;
Initial commit
2015-12-04 14:47:52 +00:00
MD5_STEP (MD5_Fo, a, b, c, d, w0_t[0], MD5C00, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w0_t[1], MD5C01, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w0_t[2], MD5C02, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w0_t[3], MD5C03, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w1_t[0], MD5C04, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w1_t[1], MD5C05, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w1_t[2], MD5C06, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w1_t[3], MD5C07, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w2_t[0], MD5C08, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w2_t[1], MD5C09, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w2_t[2], MD5C0a, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w2_t[3], MD5C0b, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w3_t[0], MD5C0c, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w3_t[1], MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w3_t[2], MD5C0e, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w3_t[3], MD5C0f, MD5S03);
MD5_STEP (MD5_Go, a, b, c, d, w0_t[1], MD5C10, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w1_t[2], MD5C11, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w2_t[3], MD5C12, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w0_t[0], MD5C13, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w1_t[1], MD5C14, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w2_t[2], MD5C15, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w3_t[3], MD5C16, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w1_t[0], MD5C17, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w2_t[1], MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w3_t[2], MD5C19, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w0_t[3], MD5C1a, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w2_t[0], MD5C1b, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w3_t[1], MD5C1c, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w0_t[2], MD5C1d, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w1_t[3], MD5C1e, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w3_t[0], MD5C1f, MD5S13);
MD5_STEP (MD5_H , a, b, c, d, w1_t[1], MD5C20, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w2_t[0], MD5C21, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w2_t[3], MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w3_t[2], MD5C23, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w0_t[1], MD5C24, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w1_t[0], MD5C25, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w1_t[3], MD5C26, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w2_t[2], MD5C27, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w3_t[1], MD5C28, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w0_t[0], MD5C29, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w0_t[3], MD5C2a, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w1_t[2], MD5C2b, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w2_t[1], MD5C2c, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w3_t[0], MD5C2d, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w3_t[3], MD5C2e, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w0_t[2], MD5C2f, MD5S23);
MD5_STEP (MD5_I , a, b, c, d, w0_t[0], MD5C30, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w1_t[3], MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w3_t[2], MD5C32, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w1_t[1], MD5C33, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w3_t[0], MD5C34, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w0_t[3], MD5C35, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w2_t[2], MD5C36, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w0_t[1], MD5C37, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w2_t[0], MD5C38, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w3_t[3], MD5C39, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w1_t[2], MD5C3a, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w3_t[1], MD5C3b, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w1_t[0], MD5C3c, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w2_t[3], MD5C3d, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w0_t[2], MD5C3e, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w2_t[1], MD5C3f, MD5S33);
a += MD5M_A;
b += MD5M_B;
c += MD5M_C;
d += MD5M_D;
w0_t[0] = uint_to_hex_lower8 ((a >> 0) & 255) << 0
| uint_to_hex_lower8 ((a >> 8) & 255) << 16;
w0_t[1] = uint_to_hex_lower8 ((a >> 16) & 255) << 0
| uint_to_hex_lower8 ((a >> 24) & 255) << 16;
w0_t[2] = uint_to_hex_lower8 ((b >> 0) & 255) << 0
| uint_to_hex_lower8 ((b >> 8) & 255) << 16;
w0_t[3] = uint_to_hex_lower8 ((b >> 16) & 255) << 0
| uint_to_hex_lower8 ((b >> 24) & 255) << 16;
w1_t[0] = uint_to_hex_lower8 ((c >> 0) & 255) << 0
| uint_to_hex_lower8 ((c >> 8) & 255) << 16;
w1_t[1] = uint_to_hex_lower8 ((c >> 16) & 255) << 0
| uint_to_hex_lower8 ((c >> 24) & 255) << 16;
w1_t[2] = uint_to_hex_lower8 ((d >> 0) & 255) << 0
| uint_to_hex_lower8 ((d >> 8) & 255) << 16;
w1_t[3] = uint_to_hex_lower8 ((d >> 16) & 255) << 0
| uint_to_hex_lower8 ((d >> 24) & 255) << 16;
// add the 4 byte challenge here
w2_t[0] = challenge;
w2_t[1] = 0x00000080;
w2_t[2] = 0;
w2_t[3] = 0;
w3_t[0] = 0;
w3_t[1] = 0;
w3_t[2] = (32 + 4) * 8;
w3_t[3] = 0;
/**
* md5 ($hash.$challenge)
*/
a = MD5M_A;
b = MD5M_B;
c = MD5M_C;
d = MD5M_D;
MD5_STEP (MD5_Fo, a, b, c, d, w0_t[0], MD5C00, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w0_t[1], MD5C01, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w0_t[2], MD5C02, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w0_t[3], MD5C03, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w1_t[0], MD5C04, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w1_t[1], MD5C05, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w1_t[2], MD5C06, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w1_t[3], MD5C07, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w2_t[0], MD5C08, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w2_t[1], MD5C09, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w2_t[2], MD5C0a, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w2_t[3], MD5C0b, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w3_t[0], MD5C0c, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w3_t[1], MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w3_t[2], MD5C0e, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w3_t[3], MD5C0f, MD5S03);
MD5_STEP (MD5_Go, a, b, c, d, w0_t[1], MD5C10, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w1_t[2], MD5C11, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w2_t[3], MD5C12, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w0_t[0], MD5C13, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w1_t[1], MD5C14, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w2_t[2], MD5C15, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w3_t[3], MD5C16, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w1_t[0], MD5C17, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w2_t[1], MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w3_t[2], MD5C19, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w0_t[3], MD5C1a, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w2_t[0], MD5C1b, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w3_t[1], MD5C1c, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w0_t[2], MD5C1d, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w1_t[3], MD5C1e, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w3_t[0], MD5C1f, MD5S13);
MD5_STEP (MD5_H , a, b, c, d, w1_t[1], MD5C20, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w2_t[0], MD5C21, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w2_t[3], MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w3_t[2], MD5C23, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w0_t[1], MD5C24, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w1_t[0], MD5C25, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w1_t[3], MD5C26, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w2_t[2], MD5C27, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w3_t[1], MD5C28, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w0_t[0], MD5C29, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w0_t[3], MD5C2a, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w1_t[2], MD5C2b, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w2_t[1], MD5C2c, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w3_t[0], MD5C2d, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w3_t[3], MD5C2e, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w0_t[2], MD5C2f, MD5S23);
MD5_STEP (MD5_I , a, b, c, d, w0_t[0], MD5C30, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w1_t[3], MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w3_t[2], MD5C32, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w1_t[1], MD5C33, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w3_t[0], MD5C34, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w0_t[3], MD5C35, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w2_t[2], MD5C36, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w0_t[1], MD5C37, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w2_t[0], MD5C38, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w3_t[3], MD5C39, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w1_t[2], MD5C3a, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w3_t[1], MD5C3b, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w1_t[0], MD5C3c, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w2_t[3], MD5C3d, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w0_t[2], MD5C3e, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w2_t[1], MD5C3f, MD5S33);
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
COMPARE_M_SIMD (a, d, c, b);
Initial commit
2015-12-04 14:47:52 +00:00
}
}
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_m08 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
}
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_m16 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
}
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_s04 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
* base
Initial commit
2015-12-04 14:47:52 +00:00
*/
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
const u32 gid = get_global_id (0);
Initial commit
2015-12-04 14:47:52 +00:00
const u32 lid = get_local_id (0);
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
const u32 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
* bin2asc table
Initial commit
2015-12-04 14:47:52 +00:00
*/
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
__local u32 l_bin2asc[256];
for (u32 i = lid; i < 256; i += lsz)
{
const u32 i0 = (i >> 0) & 15;
const u32 i1 = (i >> 4) & 15;
l_bin2asc[i] = ((i0 < 10) ? '0' + i0 : 'a' - 10 + i0) << 8
| ((i1 < 10) ? '0' + i1 : 'a' - 10 + i1) << 0;
}
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* base
*/
Initial commit
2015-12-04 14:47:52 +00:00
- Dropped all vector code since new GPU's are all scalar, makes the code much easier - Some performance on low-end GPU may drop because of that, but only for a few hash-modes - Dropped scalar code (aka warp) since we do not have any vector datatypes anymore - Renamed C++ overloading functions memcat32_9 -> memcat_c32_w4x4_a3x4 - Still need to fix kernels to new function names, needs to be done manually - Temperature Management needs to be rewritten partially because of conflicting datatypes names - Added code to create different codepaths for NV on AMD in runtime in host (see data.vendor_id) - Added code to create different codepaths for NV on AMD in runtime in kernels (see IS_NV and IS_AMD) - First tests working for -m 0, for example - Great performance increases in general for NV so far - Tested amp_* and markov_* kernel - Migrated special NV optimizations for rule processor
2015-12-15 11:04:22 +00:00
u32 pw_buf0[4];
Initial commit
2015-12-04 14:47:52 +00:00
pw_buf0[0] = pws[gid].i[ 0];
pw_buf0[1] = pws[gid].i[ 1];
pw_buf0[2] = pws[gid].i[ 2];
pw_buf0[3] = pws[gid].i[ 3];
- Dropped all vector code since new GPU's are all scalar, makes the code much easier - Some performance on low-end GPU may drop because of that, but only for a few hash-modes - Dropped scalar code (aka warp) since we do not have any vector datatypes anymore - Renamed C++ overloading functions memcat32_9 -> memcat_c32_w4x4_a3x4 - Still need to fix kernels to new function names, needs to be done manually - Temperature Management needs to be rewritten partially because of conflicting datatypes names - Added code to create different codepaths for NV on AMD in runtime in host (see data.vendor_id) - Added code to create different codepaths for NV on AMD in runtime in kernels (see IS_NV and IS_AMD) - First tests working for -m 0, for example - Great performance increases in general for NV so far - Tested amp_* and markov_* kernel - Migrated special NV optimizations for rule processor
2015-12-15 11:04:22 +00:00
u32 pw_buf1[4];
Initial commit
2015-12-04 14:47:52 +00:00
pw_buf1[0] = pws[gid].i[ 4];
pw_buf1[1] = pws[gid].i[ 5];
pw_buf1[2] = pws[gid].i[ 6];
pw_buf1[3] = pws[gid].i[ 7];
const u32 pw_len = pws[gid].pw_len;
/**
* challenge
*/
u32 challenge;
challenge = salt_bufs[salt_pos].salt_buf[0];
/**
* salt
*/
u32 salt_buf0[4];
salt_buf0[0] = salt_bufs[salt_pos].salt_buf[ 1]; // not a bug
salt_buf0[1] = salt_bufs[salt_pos].salt_buf[ 2];
salt_buf0[2] = salt_bufs[salt_pos].salt_buf[ 3];
salt_buf0[3] = salt_bufs[salt_pos].salt_buf[ 4];
u32 salt_buf1[4];
salt_buf1[0] = salt_bufs[salt_pos].salt_buf[ 5];
salt_buf1[1] = salt_bufs[salt_pos].salt_buf[ 6];
salt_buf1[2] = salt_bufs[salt_pos].salt_buf[ 7];
salt_buf1[3] = salt_bufs[salt_pos].salt_buf[ 8];
const u32 salt_len = salt_bufs[salt_pos].salt_len - 4;
/**
* digest
*/
const u32 search[4] =
{
digests_buf[digests_offset].digest_buf[DGST_R0],
digests_buf[digests_offset].digest_buf[DGST_R1],
digests_buf[digests_offset].digest_buf[DGST_R2],
digests_buf[digests_offset].digest_buf[DGST_R3]
};
/**
* loop
*/
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
for (u32 il_pos = 0; il_pos < il_cnt; il_pos += VECT_SIZE)
Initial commit
2015-12-04 14:47:52 +00:00
{
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w0[4] = { 0 };
u32x w1[4] = { 0 };
u32x w2[4] = { 0 };
u32x w3[4] = { 0 };
Initial commit
2015-12-04 14:47:52 +00:00
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
const u32x out_len = apply_rules_vect (pw_buf0, pw_buf1, pw_len, rules_buf, il_pos, w0, w1);
Initial commit
2015-12-04 14:47:52 +00:00
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w0_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0_t[0] = salt_buf0[0];
w0_t[1] = salt_buf0[1];
w0_t[2] = salt_buf0[2];
w0_t[3] = salt_buf0[3];
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w1_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1_t[0] = salt_buf1[0];
w1_t[1] = salt_buf1[1];
w1_t[2] = salt_buf1[2];
w1_t[3] = salt_buf1[3];
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w2_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2_t[0] = 0;
w2_t[1] = 0;
w2_t[2] = 0;
w2_t[3] = 0;
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x w3_t[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3_t[0] = 0;
w3_t[1] = 0;
w3_t[2] = 0;
w3_t[3] = 0;
/*
* append the salt
*/
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
switch_buffer_by_offset_le_VV (w0_t, w1_t, w2_t, w3_t, out_len);
Initial commit
2015-12-04 14:47:52 +00:00
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
const u32x pw_salt_len = out_len + salt_len;
Initial commit
2015-12-04 14:47:52 +00:00
w0_t[0] |= w0[0];
w0_t[1] |= w0[1];
w0_t[2] |= w0[2];
w0_t[3] |= w0[3];
w1_t[0] |= w1[0];
w1_t[1] |= w1[1];
w1_t[2] |= w1[2];
w1_t[3] |= w1[3];
w2_t[0] |= w2[0];
w2_t[1] |= w2[1];
w2_t[2] |= w2[2];
w2_t[3] |= w2[3];
w3_t[0] |= w3[0];
w3_t[1] |= w3[1];
w3_t[2] = pw_salt_len * 8;
w3_t[3] = 0;
/*
* md5 ($pass.$salt)
*/
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
u32x a = MD5M_A;
u32x b = MD5M_B;
u32x c = MD5M_C;
u32x d = MD5M_D;
Initial commit
2015-12-04 14:47:52 +00:00
MD5_STEP (MD5_Fo, a, b, c, d, w0_t[0], MD5C00, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w0_t[1], MD5C01, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w0_t[2], MD5C02, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w0_t[3], MD5C03, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w1_t[0], MD5C04, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w1_t[1], MD5C05, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w1_t[2], MD5C06, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w1_t[3], MD5C07, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w2_t[0], MD5C08, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w2_t[1], MD5C09, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w2_t[2], MD5C0a, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w2_t[3], MD5C0b, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w3_t[0], MD5C0c, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w3_t[1], MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w3_t[2], MD5C0e, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w3_t[3], MD5C0f, MD5S03);
MD5_STEP (MD5_Go, a, b, c, d, w0_t[1], MD5C10, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w1_t[2], MD5C11, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w2_t[3], MD5C12, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w0_t[0], MD5C13, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w1_t[1], MD5C14, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w2_t[2], MD5C15, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w3_t[3], MD5C16, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w1_t[0], MD5C17, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w2_t[1], MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w3_t[2], MD5C19, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w0_t[3], MD5C1a, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w2_t[0], MD5C1b, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w3_t[1], MD5C1c, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w0_t[2], MD5C1d, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w1_t[3], MD5C1e, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w3_t[0], MD5C1f, MD5S13);
MD5_STEP (MD5_H , a, b, c, d, w1_t[1], MD5C20, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w2_t[0], MD5C21, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w2_t[3], MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w3_t[2], MD5C23, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w0_t[1], MD5C24, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w1_t[0], MD5C25, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w1_t[3], MD5C26, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w2_t[2], MD5C27, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w3_t[1], MD5C28, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w0_t[0], MD5C29, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w0_t[3], MD5C2a, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w1_t[2], MD5C2b, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w2_t[1], MD5C2c, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w3_t[0], MD5C2d, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w3_t[3], MD5C2e, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w0_t[2], MD5C2f, MD5S23);
MD5_STEP (MD5_I , a, b, c, d, w0_t[0], MD5C30, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w1_t[3], MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w3_t[2], MD5C32, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w1_t[1], MD5C33, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w3_t[0], MD5C34, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w0_t[3], MD5C35, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w2_t[2], MD5C36, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w0_t[1], MD5C37, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w2_t[0], MD5C38, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w3_t[3], MD5C39, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w1_t[2], MD5C3a, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w3_t[1], MD5C3b, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w1_t[0], MD5C3c, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w2_t[3], MD5C3d, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w0_t[2], MD5C3e, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w2_t[1], MD5C3f, MD5S33);
a += MD5M_A;
b += MD5M_B;
c += MD5M_C;
d += MD5M_D;
w0_t[0] = uint_to_hex_lower8 ((a >> 0) & 255) << 0
| uint_to_hex_lower8 ((a >> 8) & 255) << 16;
w0_t[1] = uint_to_hex_lower8 ((a >> 16) & 255) << 0
| uint_to_hex_lower8 ((a >> 24) & 255) << 16;
w0_t[2] = uint_to_hex_lower8 ((b >> 0) & 255) << 0
| uint_to_hex_lower8 ((b >> 8) & 255) << 16;
w0_t[3] = uint_to_hex_lower8 ((b >> 16) & 255) << 0
| uint_to_hex_lower8 ((b >> 24) & 255) << 16;
w1_t[0] = uint_to_hex_lower8 ((c >> 0) & 255) << 0
| uint_to_hex_lower8 ((c >> 8) & 255) << 16;
w1_t[1] = uint_to_hex_lower8 ((c >> 16) & 255) << 0
| uint_to_hex_lower8 ((c >> 24) & 255) << 16;
w1_t[2] = uint_to_hex_lower8 ((d >> 0) & 255) << 0
| uint_to_hex_lower8 ((d >> 8) & 255) << 16;
w1_t[3] = uint_to_hex_lower8 ((d >> 16) & 255) << 0
| uint_to_hex_lower8 ((d >> 24) & 255) << 16;
// add the 4 byte challenge here
w2_t[0] = challenge;
w2_t[1] = 0x00000080;
w2_t[2] = 0;
w2_t[3] = 0;
w3_t[0] = 0;
w3_t[1] = 0;
w3_t[2] = (32 + 4) * 8;
w3_t[3] = 0;
/**
* md5 ($hash.$challenge)
*/
a = MD5M_A;
b = MD5M_B;
c = MD5M_C;
d = MD5M_D;
MD5_STEP (MD5_Fo, a, b, c, d, w0_t[0], MD5C00, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w0_t[1], MD5C01, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w0_t[2], MD5C02, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w0_t[3], MD5C03, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w1_t[0], MD5C04, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w1_t[1], MD5C05, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w1_t[2], MD5C06, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w1_t[3], MD5C07, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w2_t[0], MD5C08, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w2_t[1], MD5C09, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w2_t[2], MD5C0a, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w2_t[3], MD5C0b, MD5S03);
MD5_STEP (MD5_Fo, a, b, c, d, w3_t[0], MD5C0c, MD5S00);
MD5_STEP (MD5_Fo, d, a, b, c, w3_t[1], MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, w3_t[2], MD5C0e, MD5S02);
MD5_STEP (MD5_Fo, b, c, d, a, w3_t[3], MD5C0f, MD5S03);
MD5_STEP (MD5_Go, a, b, c, d, w0_t[1], MD5C10, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w1_t[2], MD5C11, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w2_t[3], MD5C12, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w0_t[0], MD5C13, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w1_t[1], MD5C14, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w2_t[2], MD5C15, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w3_t[3], MD5C16, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w1_t[0], MD5C17, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w2_t[1], MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w3_t[2], MD5C19, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w0_t[3], MD5C1a, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w2_t[0], MD5C1b, MD5S13);
MD5_STEP (MD5_Go, a, b, c, d, w3_t[1], MD5C1c, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, w0_t[2], MD5C1d, MD5S11);
MD5_STEP (MD5_Go, c, d, a, b, w1_t[3], MD5C1e, MD5S12);
MD5_STEP (MD5_Go, b, c, d, a, w3_t[0], MD5C1f, MD5S13);
MD5_STEP (MD5_H , a, b, c, d, w1_t[1], MD5C20, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w2_t[0], MD5C21, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w2_t[3], MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w3_t[2], MD5C23, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w0_t[1], MD5C24, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w1_t[0], MD5C25, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w1_t[3], MD5C26, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w2_t[2], MD5C27, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w3_t[1], MD5C28, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w0_t[0], MD5C29, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w0_t[3], MD5C2a, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w1_t[2], MD5C2b, MD5S23);
MD5_STEP (MD5_H , a, b, c, d, w2_t[1], MD5C2c, MD5S20);
MD5_STEP (MD5_H , d, a, b, c, w3_t[0], MD5C2d, MD5S21);
MD5_STEP (MD5_H , c, d, a, b, w3_t[3], MD5C2e, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, w0_t[2], MD5C2f, MD5S23);
MD5_STEP (MD5_I , a, b, c, d, w0_t[0], MD5C30, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w1_t[3], MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w3_t[2], MD5C32, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w1_t[1], MD5C33, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w3_t[0], MD5C34, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w0_t[3], MD5C35, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w2_t[2], MD5C36, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w0_t[1], MD5C37, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w2_t[0], MD5C38, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w3_t[3], MD5C39, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w1_t[2], MD5C3a, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w3_t[1], MD5C3b, MD5S33);
MD5_STEP (MD5_I , a, b, c, d, w1_t[0], MD5C3c, MD5S30);
MD5_STEP (MD5_I , d, a, b, c, w2_t[3], MD5C3d, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, w0_t[2], MD5C3e, MD5S32);
MD5_STEP (MD5_I , b, c, d, a, w2_t[1], MD5C3f, MD5S33);
Converted to new SIMD: -m 11100 -a 0
2016-02-04 21:38:55 +00:00
COMPARE_S_SIMD (a, d, c, b);
Initial commit
2015-12-04 14:47:52 +00:00
}
}
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_s08 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
}
Fixed two major problems 1) SIMD code for all attack-mode Macro vector_accessible() was not refactored and missing completely. Had to rename variables rules_cnt, combs_cnt and bfs_cnt into il_cnt which was a good thing anyway as with new SIMD code they all act in the same way. 2) SIMD code for attack-mode 0 With new SIMD code, apply_rules_vect() has to return u32 not u32x. This has massive impact on all *_a0 kernels. I've rewritten most of them. Deep testing using test.sh is still required. Some kernel need more fixes: - Some are kind of completely incompatible like m10400 but they still use old check_* includes, we should get rid of them as they are no longer neccessary as we have simd.c - Some have a chance but require additional effort like m11500. We can use commented out "#define NEW_SIMD_CODE" to find them This change can have negative impact on -a0 performance for device that require vectorization. That is mostly CPU devices. New GPU's are all scalar, so they wont get hurt by this. This change also proofes that there's no way to efficiently vectorize kernel rules with new SIMD code, but it enables the addition of the rule functions like @ that we were missing for some long time. This is a TODO.
2016-02-27 16:18:54 +00:00
__kernel void m11100_s16 (__global pw_t *pws, __global kernel_rule_t *rules_buf, __global comb_t *combs_buf, __global bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global u32 *bitmaps_buf_s1_a, __global u32 *bitmaps_buf_s1_b, __global u32 *bitmaps_buf_s1_c, __global u32 *bitmaps_buf_s1_d, __global u32 *bitmaps_buf_s2_a, __global u32 *bitmaps_buf_s2_b, __global u32 *bitmaps_buf_s2_c, __global u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global digest_t *digests_buf, __global u32 *hashes_shown, __global salt_t *salt_bufs, __global void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV_buf, const u32 bitmap_mask, const u32 bitmap_shift1, const u32 bitmap_shift2, const u32 salt_pos, const u32 loop_pos, const u32 loop_cnt, const u32 il_cnt, const u32 digests_cnt, const u32 digests_offset, const u32 combs_mode, const u32 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
}
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