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hashcat/OpenCL/m02710_a3-optimized.cl

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Initial commit
2015-12-04 14:47:52 +00:00
/**
Added docs/credits.txt Added docs/team.txt
2016-09-11 20:20:15 +00:00
* Author......: See docs/credits.txt
Initial commit
2015-12-04 14:47:52 +00:00
* License.....: MIT
*/
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
#define NEW_SIMD_CODE
Move files from include/ to OpenCL/ if they are used within kernels Rename includes in OpenCL so that it's easier to recognize them as such
2016-05-25 21:04:26 +00:00
#include "inc_vendor.cl"
Move macros DGST_R0 - DGST_R3 to host, define dgst_size for opencl kernel from host; both at runtime
2016-06-26 21:39:42 +00:00
#include "inc_hash_constants.h"
Move files from include/ to OpenCL/ if they are used within kernels Rename includes in OpenCL so that it's easier to recognize them as such
2016-05-25 21:04:26 +00:00
#include "inc_hash_functions.cl"
#include "inc_types.cl"
#include "inc_common.cl"
#include "inc_simd.cl"
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +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])
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
#endif
Initial commit
2015-12-04 14:47:52 +00:00
Get rid of comb_t which can be safely replace with pw_t now
2017-06-24 22:56:25 +00:00
void m02710m (u32 w0[4], u32 w1[4], u32 w2[4], u32 w3[4], const u32 pw_len, __global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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, __local u32 *l_bin2asc)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* modifier
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* salt
*/
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
u32 salt_buf0[4];
u32 salt_buf1[4];
u32 salt_buf2[4];
u32 salt_buf3[4];
salt_buf0[0] = salt_bufs[salt_pos].salt_buf[0];
salt_buf0[1] = salt_bufs[salt_pos].salt_buf[1];
salt_buf0[2] = salt_bufs[salt_pos].salt_buf[2];
salt_buf0[3] = salt_bufs[salt_pos].salt_buf[3];
salt_buf1[0] = salt_bufs[salt_pos].salt_buf[4];
salt_buf1[1] = salt_bufs[salt_pos].salt_buf[5];
salt_buf1[2] = salt_bufs[salt_pos].salt_buf[6];
salt_buf1[3] = salt_bufs[salt_pos].salt_buf[7];
salt_buf2[0] = 0;
salt_buf2[1] = 0;
salt_buf2[2] = 0;
salt_buf2[3] = 0;
salt_buf3[0] = 0;
salt_buf3[1] = 0;
salt_buf3[2] = 0;
salt_buf3[3] = 0;
Initial commit
2015-12-04 14:47:52 +00:00
const u32 salt_len = salt_bufs[salt_pos].salt_len;
/**
* loop
*/
- 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 w0l = w0[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
for (u32 il_pos = 0; il_pos < il_cnt; il_pos += VECT_SIZE)
Initial commit
2015-12-04 14:47:52 +00:00
{
Prepare NEW_SIMD_MODE for -a 1 kernels
2016-02-20 15:13:06 +00:00
const u32x w0r = ix_create_bft (bfs_buf, il_pos);
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
const u32x w0lr = w0l | w0r;
u32x w0_t[4];
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
u32x w1_t[4];
u32x w2_t[4];
u32x w3_t[4];
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
w0_t[0] = w0lr;
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] = w3[2];
w3_t[3] = w3[3];
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
/**
* md5
*/
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
u32x a = MD5M_A;
u32x b = MD5M_B;
u32x c = MD5M_C;
u32x 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);
Initial commit
2015-12-04 14:47:52 +00:00
a += MD5M_A;
b += MD5M_B;
c += MD5M_C;
d += MD5M_D;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
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;
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
w2_t[0] = salt_buf0[0];
w2_t[1] = salt_buf0[1];
w2_t[2] = salt_buf0[2];
w2_t[3] = salt_buf0[3];
w3_t[0] = salt_buf1[0];
w3_t[1] = salt_buf1[1];
w3_t[2] = salt_buf1[2];
w3_t[3] = salt_buf1[3];
Initial commit
2015-12-04 14:47:52 +00:00
a = MD5M_A;
b = MD5M_B;
c = MD5M_C;
d = MD5M_D;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +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);
const u32x r_a = a + MD5M_A;
const u32x r_b = b + MD5M_B;
const u32x r_c = c + MD5M_C;
const u32x r_d = d + MD5M_D;
Initial commit
2015-12-04 14:47:52 +00:00
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
const u32x r_14 = (32 + salt_len) * 8;
Initial commit
2015-12-04 14:47:52 +00:00
a = r_a;
b = r_b;
c = r_c;
d = r_d;
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C00, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C01, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C02, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C03, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C04, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C05, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C06, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C07, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C08, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C09, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C0a, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C0b, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C0c, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, r_14, MD5C0e, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C0f, MD5S03);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C10, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C11, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C12, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C13, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C14, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C15, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C16, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C17, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, r_14, MD5C19, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C1a, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C1b, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C1c, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C1d, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C1e, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C1f, MD5S13);
MD5_STEP0(MD5_H , a, b, c, d, MD5C20, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C21, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, r_14, MD5C23, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C24, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C25, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C26, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C27, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C28, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C29, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C2a, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C2b, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C2c, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C2d, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C2e, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C2f, MD5S23);
MD5_STEP0(MD5_I , a, b, c, d, MD5C30, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, r_14, MD5C32, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C33, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C34, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C35, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C36, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C37, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C38, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C39, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C3a, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C3b, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C3c, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C3d, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C3e, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C3f, MD5S33);
a += r_a;
b += r_b;
c += r_c;
d += r_d;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
COMPARE_M_SIMD (a, d, c, b);
Initial commit
2015-12-04 14:47:52 +00:00
}
}
Get rid of comb_t which can be safely replace with pw_t now
2017-06-24 22:56:25 +00:00
void m02710s (u32 w0[4], u32 w1[4], u32 w2[4], u32 w3[4], const u32 pw_len, __global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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, __local u32 *l_bin2asc)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* modifier
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
Initial commit
2015-12-04 14:47:52 +00:00
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
/**
* salt
*/
u32 salt_buf0[4];
u32 salt_buf1[4];
u32 salt_buf2[4];
u32 salt_buf3[4];
salt_buf0[0] = salt_bufs[salt_pos].salt_buf[0];
salt_buf0[1] = salt_bufs[salt_pos].salt_buf[1];
salt_buf0[2] = salt_bufs[salt_pos].salt_buf[2];
salt_buf0[3] = salt_bufs[salt_pos].salt_buf[3];
salt_buf1[0] = salt_bufs[salt_pos].salt_buf[4];
salt_buf1[1] = salt_bufs[salt_pos].salt_buf[5];
salt_buf1[2] = salt_bufs[salt_pos].salt_buf[6];
salt_buf1[3] = salt_bufs[salt_pos].salt_buf[7];
salt_buf2[0] = 0;
salt_buf2[1] = 0;
salt_buf2[2] = 0;
salt_buf2[3] = 0;
salt_buf3[0] = 0;
salt_buf3[1] = 0;
salt_buf3[2] = 0;
salt_buf3[3] = 0;
const u32 salt_len = salt_bufs[salt_pos].salt_len;
Initial commit
2015-12-04 14:47:52 +00:00
/**
* 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
*/
- 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 w0l = w0[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
for (u32 il_pos = 0; il_pos < il_cnt; il_pos += VECT_SIZE)
Initial commit
2015-12-04 14:47:52 +00:00
{
Prepare NEW_SIMD_MODE for -a 1 kernels
2016-02-20 15:13:06 +00:00
const u32x w0r = ix_create_bft (bfs_buf, il_pos);
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
const u32x w0lr = w0l | w0r;
u32x w0_t[4];
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
u32x w1_t[4];
u32x w2_t[4];
u32x w3_t[4];
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
w0_t[0] = w0lr;
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] = w3[2];
w3_t[3] = w3[3];
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
/**
* md5
*/
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
u32x a = MD5M_A;
u32x b = MD5M_B;
u32x c = MD5M_C;
u32x 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);
Initial commit
2015-12-04 14:47:52 +00:00
a += MD5M_A;
b += MD5M_B;
c += MD5M_C;
d += MD5M_D;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
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;
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
w2_t[0] = salt_buf0[0];
w2_t[1] = salt_buf0[1];
w2_t[2] = salt_buf0[2];
w2_t[3] = salt_buf0[3];
w3_t[0] = salt_buf1[0];
w3_t[1] = salt_buf1[1];
w3_t[2] = salt_buf1[2];
w3_t[3] = salt_buf1[3];
Initial commit
2015-12-04 14:47:52 +00:00
a = MD5M_A;
b = MD5M_B;
c = MD5M_C;
d = MD5M_D;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +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);
const u32x r_a = a + MD5M_A;
const u32x r_b = b + MD5M_B;
const u32x r_c = c + MD5M_C;
const u32x r_d = d + MD5M_D;
Initial commit
2015-12-04 14:47:52 +00:00
Cleanup -m 27xx kernels to latest standard
2016-03-19 16:07:28 +00:00
const u32x r_14 = (32 + salt_len) * 8;
Initial commit
2015-12-04 14:47:52 +00:00
a = r_a;
b = r_b;
c = r_c;
d = r_d;
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C00, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C01, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C02, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C03, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C04, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C05, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C06, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C07, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C08, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C09, MD5S01);
MD5_STEP0(MD5_Fo, c, d, a, b, MD5C0a, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C0b, MD5S03);
MD5_STEP0(MD5_Fo, a, b, c, d, MD5C0c, MD5S00);
MD5_STEP0(MD5_Fo, d, a, b, c, MD5C0d, MD5S01);
MD5_STEP (MD5_Fo, c, d, a, b, r_14, MD5C0e, MD5S02);
MD5_STEP0(MD5_Fo, b, c, d, a, MD5C0f, MD5S03);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C10, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C11, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C12, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C13, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C14, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C15, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C16, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C17, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C18, MD5S10);
MD5_STEP (MD5_Go, d, a, b, c, r_14, MD5C19, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C1a, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C1b, MD5S13);
MD5_STEP0(MD5_Go, a, b, c, d, MD5C1c, MD5S10);
MD5_STEP0(MD5_Go, d, a, b, c, MD5C1d, MD5S11);
MD5_STEP0(MD5_Go, c, d, a, b, MD5C1e, MD5S12);
MD5_STEP0(MD5_Go, b, c, d, a, MD5C1f, MD5S13);
MD5_STEP0(MD5_H , a, b, c, d, MD5C20, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C21, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C22, MD5S22);
MD5_STEP (MD5_H , b, c, d, a, r_14, MD5C23, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C24, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C25, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C26, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C27, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C28, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C29, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C2a, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C2b, MD5S23);
MD5_STEP0(MD5_H , a, b, c, d, MD5C2c, MD5S20);
MD5_STEP0(MD5_H , d, a, b, c, MD5C2d, MD5S21);
MD5_STEP0(MD5_H , c, d, a, b, MD5C2e, MD5S22);
MD5_STEP0(MD5_H , b, c, d, a, MD5C2f, MD5S23);
MD5_STEP0(MD5_I , a, b, c, d, MD5C30, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C31, MD5S31);
MD5_STEP (MD5_I , c, d, a, b, r_14, MD5C32, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C33, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C34, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C35, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C36, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C37, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C38, MD5S30);
MD5_STEP0(MD5_I , d, a, b, c, MD5C39, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C3a, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C3b, MD5S33);
MD5_STEP0(MD5_I , a, b, c, d, MD5C3c, MD5S30);
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
if (MATCHES_NONE_VS ((a + r_a), search[0])) continue;
Initial commit
2015-12-04 14:47:52 +00:00
MD5_STEP0(MD5_I , d, a, b, c, MD5C3d, MD5S31);
MD5_STEP0(MD5_I , c, d, a, b, MD5C3e, MD5S32);
MD5_STEP0(MD5_I , b, c, d, a, MD5C3f, MD5S33);
a += r_a;
b += r_b;
c += r_c;
d += r_d;
Converted _a3 kernels, use SIMD for CPU and GPU
2016-01-23 14:32:31 +00:00
COMPARE_S_SIMD (a, d, c, b);
Initial commit
2015-12-04 14:47:52 +00:00
}
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_m04 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = 0;
w1[1] = 0;
w1[2] = 0;
w1[3] = 0;
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = 0;
w2[1] = 0;
w2[2] = 0;
w2[3] = 0;
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = 0;
w3[1] = 0;
w3[2] = pws[gid].i[14];
w3[3] = 0;
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710m (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_m08 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = pws[gid].i[ 4];
w1[1] = pws[gid].i[ 5];
w1[2] = pws[gid].i[ 6];
w1[3] = pws[gid].i[ 7];
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = 0;
w2[1] = 0;
w2[2] = 0;
w2[3] = 0;
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = 0;
w3[1] = 0;
w3[2] = pws[gid].i[14];
w3[3] = 0;
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710m (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_m16 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = pws[gid].i[ 4];
w1[1] = pws[gid].i[ 5];
w1[2] = pws[gid].i[ 6];
w1[3] = pws[gid].i[ 7];
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = pws[gid].i[ 8];
w2[1] = pws[gid].i[ 9];
w2[2] = pws[gid].i[10];
w2[3] = pws[gid].i[11];
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = pws[gid].i[12];
w3[1] = pws[gid].i[13];
w3[2] = pws[gid].i[14];
w3[3] = pws[gid].i[15];
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710m (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_s04 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = 0;
w1[1] = 0;
w1[2] = 0;
w1[3] = 0;
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = 0;
w2[1] = 0;
w2[2] = 0;
w2[3] = 0;
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = 0;
w3[1] = 0;
w3[2] = pws[gid].i[14];
w3[3] = 0;
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710s (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_s08 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = pws[gid].i[ 4];
w1[1] = pws[gid].i[ 5];
w1[2] = pws[gid].i[ 6];
w1[3] = pws[gid].i[ 7];
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = 0;
w2[1] = 0;
w2[2] = 0;
w2[3] = 0;
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = 0;
w3[1] = 0;
w3[2] = pws[gid].i[14];
w3[3] = 0;
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710s (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
__kernel void m02710_s16 (__global pw_t *pws, __global const kernel_rule_t *rules_buf, __global const pw_t *combs_buf, __global const bf_t *bfs_buf, __global void *tmps, __global void *hooks, __global const u32 *bitmaps_buf_s1_a, __global const u32 *bitmaps_buf_s1_b, __global const u32 *bitmaps_buf_s1_c, __global const u32 *bitmaps_buf_s1_d, __global const u32 *bitmaps_buf_s2_a, __global const u32 *bitmaps_buf_s2_b, __global const u32 *bitmaps_buf_s2_c, __global const u32 *bitmaps_buf_s2_d, __global plain_t *plains_buf, __global const digest_t *digests_buf, __global u32 *hashes_shown, __global const salt_t *salt_bufs, __global const void *esalt_bufs, __global u32 *d_return_buf, __global u32 *d_scryptV0_buf, __global u32 *d_scryptV1_buf, __global u32 *d_scryptV2_buf, __global u32 *d_scryptV3_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 u64 gid_max)
Initial commit
2015-12-04 14:47:52 +00:00
{
/**
* base
*/
Switch the datatypes of the variables responsible for work-item count and work-item size from u32 to u64
2017-08-19 14:39:22 +00:00
const u64 gid = get_global_id (0);
const u64 lid = get_local_id (0);
const u64 lsz = get_local_size (0);
Initial commit
2015-12-04 14:47:52 +00:00
/**
* modifier
*/
- 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 w0[4];
Initial commit
2015-12-04 14:47:52 +00:00
w0[0] = pws[gid].i[ 0];
w0[1] = pws[gid].i[ 1];
w0[2] = pws[gid].i[ 2];
w0[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 w1[4];
Initial commit
2015-12-04 14:47:52 +00:00
w1[0] = pws[gid].i[ 4];
w1[1] = pws[gid].i[ 5];
w1[2] = pws[gid].i[ 6];
w1[3] = pws[gid].i[ 7];
- 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 w2[4];
Initial commit
2015-12-04 14:47:52 +00:00
w2[0] = pws[gid].i[ 8];
w2[1] = pws[gid].i[ 9];
w2[2] = pws[gid].i[10];
w2[3] = pws[gid].i[11];
- 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 w3[4];
Initial commit
2015-12-04 14:47:52 +00:00
w3[0] = pws[gid].i[12];
w3[1] = pws[gid].i[13];
w3[2] = pws[gid].i[14];
w3[3] = pws[gid].i[15];
const u32 pw_len = pws[gid].pw_len;
/**
* bin2asc table
*/
__local u32 l_bin2asc[256];
Upgrade kernel to support dynamic local work sizes
2016-01-19 15:06:03 +00:00
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;
}
Initial commit
2015-12-04 14:47:52 +00:00
barrier (CLK_LOCAL_MEM_FENCE);
if (gid >= gid_max) return;
/**
* main
*/
Workaround memory allocation limit from OpenCL by using multiple buffers for scrypt
2016-06-28 09:03:04 +00:00
m02710s (w0, w1, w2, w3, pw_len, pws, rules_buf, combs_buf, bfs_buf, tmps, hooks, bitmaps_buf_s1_a, bitmaps_buf_s1_b, bitmaps_buf_s1_c, bitmaps_buf_s1_d, bitmaps_buf_s2_a, bitmaps_buf_s2_b, bitmaps_buf_s2_c, bitmaps_buf_s2_d, plains_buf, digests_buf, hashes_shown, salt_bufs, esalt_bufs, d_return_buf, d_scryptV0_buf, d_scryptV1_buf, d_scryptV2_buf, d_scryptV3_buf, bitmap_mask, bitmap_shift1, bitmap_shift2, salt_pos, loop_pos, loop_cnt, il_cnt, digests_cnt, digests_offset, l_bin2asc);
Initial commit
2015-12-04 14:47:52 +00:00
}
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