Actually krnlx is doing it right. Nobody pays a penny if he release the miner in public now. So it's better that he is mining himself, maybe only with few GPU's, to get his hard work pay for himself.
Everyone just wait and not destroy his profit.
Topic: [ANN] ccminer 2.3 - opensource - GPL (tpruvot) - page 44. (Read 500193 times)
Not sure what this means? There aren't developers because there is someone willing to pay more? You don't even know what 'more' is in this situation.
Sorry, some editing, stupid me.
Let me put it this way: You are not paying enough.
You don't know how much I'm paying. Despite leaving open ended amounts to those I wish to employ they don't even look to do business with you.
Not sure what this means? There aren't developers because there is someone willing to pay more? You don't even know what 'more' is in this situation.
Sorry, some editing, stupid me.
Let me put it this way: You are not paying enough.
You'd think Cryptos would have attracted a lot more talent then they have as far as programming goes.
Someone pays more. No matter how big farm you build some builds bigger.
I'm sure you have seen this, could be fake:
Not sure what this means? There aren't developers because there is someone willing to pay more? You don't even know what 'more' is in this situation.
You'd think Cryptos would have attracted a lot more talent then they have as far as programming goes.
Someone pays more. No matter how big farm you build some builds bigger.
I'm sure you have seen this, could be fake:
There are always people willing to compile something for Windows if you post it. He's currently getting his bounty out of Xevan and then can release it for the .5 bounty. Either way, once it's released it wont earn more then all the other algos at the moment. Too public, too popular, the new kids still don't know how difficulty works. Only reason it looks more profitable (and not by that much) is because one person is currently mining it.
Krlnx knows this and that's why he publicly announced it. It's no longer that profitable and he probably can scoop up quite a bit more from private offers in PMs.
Epsylon doesn't make ground up miners AFAIK. I've tried contacting multiple developers to get private miners made and most wont even talk about it despite willing to compensate them quite well. It's actually pretty ludicrous. You'd think Cryptos would have attracted a lot more talent then they have as far as programming goes.
Krlnx knows this and that's why he publicly announced it. It's no longer that profitable and he probably can scoop up quite a bit more from private offers in PMs.
You want some profitable nvidia mining to happen? Next time something new is released contact tpruvot.
Epsylon doesn't make ground up miners AFAIK. I've tried contacting multiple developers to get private miners made and most wont even talk about it despite willing to compensate them quite well. It's actually pretty ludicrous. You'd think Cryptos would have attracted a lot more talent then they have as far as programming goes.
You want some profitable nvidia mining to happen? Next time something new is released contact tpruvot.
I see a lot of people are looking for xevan algorithm to work at ccminer, and some developers sell ccminer copy for 0.5BTC.
Does these people know what xevan algo coins have very shall hashrate? 200+ pcs gpus like gtx 1070 and profit almost gone KRLX can earn 0.25-0.5 BTC in bounties if he publish his work.
They want windows version. I dont have windows computers.
Your skunk miner was so easy to compile in windows. so many users here did it, including me.
You will find here hundreds of sincere guys who can do that for you.
KRLX can earn 0.25-0.5 BTC in bounties if he publish his work.
They want windows version. I dont have windows computers.
Your skunk miner was so easy to compile in windows. so many users here did it, including me.
You will find here hundreds of sincere guys who can do that for you.
It needs complete rewrite. simd512 will be the most difficult
This code is not based on the 4way groestl in ccminer, but the opencl wolf0 groestl kernel in sgminer. I have updated the post with the more code.
I can port to windows and will do it for free. pm me.
KRLX can earn 0.25-0.5 BTC in bounties if he publish his work.
They want windows version. I dont have windows computers.
KRLX can earn 0.25-0.5 BTC in bounties if he publish his work.
They want windows version. I dont have windows computers.
Sp_ is preparing his next 0.5 BTC by asking
KRLX can earn 0.25-0.5 BTC in bounties if he publish his work. Next up is groestl 1024.
broken..
broken..
Code:
__global__
void quark_groestl512_gpu_hash_128(uint32_t threads, uint64_t *const __restrict__ g_hash)
{
uint64_t T0[256], T1[256], T2[256], T3[256];
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
const uint32_t hashPosition = thread;
uint64_t *inpHash = &g_hash[8 * hashPosition];
for (int i = 0; i < 256; i += WORKSIZE)
{
const uint64_t tmp = T0_G[i];
T0[i] = tmp;
T1[i] = ROTL64(tmp, 8UL);
T2[i] = ROTL64(tmp, 16UL);
T3[i] = ROTL64(tmp, 24UL);
}
uint64_t M[16] = { 0 }, H[16] = { 0 }, H2[16];
#pragma unroll
for (int i = 0; i < 16; ++i) M[i] = cuda_swab32(((uint32_t *)inpHash)[i]);
//M[8] = 0x80UL;
//M[15] = 0x0100000000000000UL;
H[15] = 0x0002000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
M[0] = 0x80UL;
for (int i = 1; i < 15; ++i) M[i] = 0x00UL;
M[15] = 0x0200000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
GroestlP(H2, H, T0, T1, T2, T3);
// vstore8(VSWAP8(((ulong8 *)H2)[1] ^ ((ulong8 *)H)[1]), 0, hash->h8);
#pragma unroll
for (int i = 8; i < 16; ++i)
{
H2[i] = (H2[i] ^ H[i]);
}
inpHash[0] = H2[0 + 8];
inpHash[1] = H2[1 + 8];
inpHash[2] = H2[2 + 8];
inpHash[3] = H2[3 + 8];
inpHash[4] = H2[4 + 8];
inpHash[5] = H2[5 + 8];
inpHash[6] = H2[6 + 8];
inpHash[7] = H2[7 + 8];
}
}
void quark_groestl512_gpu_hash_128(uint32_t threads, uint64_t *const __restrict__ g_hash)
{
uint64_t T0[256], T1[256], T2[256], T3[256];
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
const uint32_t hashPosition = thread;
uint64_t *inpHash = &g_hash[8 * hashPosition];
for (int i = 0; i < 256; i += WORKSIZE)
{
const uint64_t tmp = T0_G[i];
T0[i] = tmp;
T1[i] = ROTL64(tmp, 8UL);
T2[i] = ROTL64(tmp, 16UL);
T3[i] = ROTL64(tmp, 24UL);
}
uint64_t M[16] = { 0 }, H[16] = { 0 }, H2[16];
#pragma unroll
for (int i = 0; i < 16; ++i) M[i] = cuda_swab32(((uint32_t *)inpHash)[i]);
//M[8] = 0x80UL;
//M[15] = 0x0100000000000000UL;
H[15] = 0x0002000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
M[0] = 0x80UL;
for (int i = 1; i < 15; ++i) M[i] = 0x00UL;
M[15] = 0x0200000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
GroestlP(H2, H, T0, T1, T2, T3);
// vstore8(VSWAP8(((ulong8 *)H2)[1] ^ ((ulong8 *)H)[1]), 0, hash->h8);
#pragma unroll
for (int i = 8; i < 16; ++i)
{
H2[i] = (H2[i] ^ H[i]);
}
inpHash[0] = H2[0 + 8];
inpHash[1] = H2[1 + 8];
inpHash[2] = H2[2 + 8];
inpHash[3] = H2[3 + 8];
inpHash[4] = H2[4 + 8];
inpHash[5] = H2[5 + 8];
inpHash[6] = H2[6 + 8];
inpHash[7] = H2[7 + 8];
}
}
It needs complete rewrite. simd512 will be the most difficult
Sp_ is preparing his next 0.5 BTC by asking
There are several bounties on a opensource cuda xevan miner.
KRLX can earn 0.25-0.5 BTC in bounties if he publish his work.
Next up is groestl 1024.
broken..
// Macros and table for Wolf's OpenCL Groestl implementation
#define BYTE(x, y) (bfe((uint32_t)((x) >> ((y >= 32U) ? 32U : 0U)), (y) - (((y) >= 32) ? 32U : 0), 8U))
#define B64_0(x) ((uint8_t)(x))
#define B64_1(x) BYTE((x), 8U)
#define B64_2(x) BYTE((x), 16U)
#define B64_3(x) BYTE((x), 24U)
#define B64_4(x) BYTE((x), 32U)
#define B64_5(x) BYTE((x), 40U)
#define B64_6(x) BYTE((x), 48U)
#define B64_7(x) BYTE((x), 56U)
#define GROESTL_RBTT(d, Hval, b0, b1, b2, b3, b4, b5, b6, b7)\
d = (T0[B64_0(Hval[b0])] ^ T1[B64_1(Hval[b1])] ^ T2[B64_2(Hval[b2])] ^ T3[B64_3(Hval[b3])] \
^ SWAPDWORDS(T0[B64_4(Hval[b4])]) ^ SWAPDWORDS(T1[B64_5(Hval[b5])]) ^ SWAPDWORDS(T2[B64_6(Hval[b6])]) ^ SWAPDWORDS(T3[B64_7(Hval[b7])]));
#define PC64(j, r) (uint64_t)((j) | (r))
#define QC64(j, r) ROTL64(((uint64_t)(r)) ^ (~((uint64_t)(j))), 56UL)
static const __constant__ uint64_t T0_G[] =
{
0xc6a597f4a5f432c6UL, 0xf884eb9784976ff8UL, 0xee99c7b099b05eeeUL, 0xf68df78c8d8c7af6UL,
0xff0de5170d17e8ffUL, 0xd6bdb7dcbddc0ad6UL, 0xdeb1a7c8b1c816deUL, 0x915439fc54fc6d91UL,
0x6050c0f050f09060UL, 0x0203040503050702UL, 0xcea987e0a9e02eceUL, 0x567dac877d87d156UL,
0xe719d52b192bcce7UL, 0xb56271a662a613b5UL, 0x4de69a31e6317c4dUL, 0xec9ac3b59ab559ecUL,
0x8f4505cf45cf408fUL, 0x1f9d3ebc9dbca31fUL, 0x894009c040c04989UL, 0xfa87ef92879268faUL,
0xef15c53f153fd0efUL, 0xb2eb7f26eb2694b2UL, 0x8ec90740c940ce8eUL, 0xfb0bed1d0b1de6fbUL,
0x41ec822fec2f6e41UL, 0xb3677da967a91ab3UL, 0x5ffdbe1cfd1c435fUL, 0x45ea8a25ea256045UL,
0x23bf46dabfdaf923UL, 0x53f7a602f7025153UL, 0xe496d3a196a145e4UL, 0x9b5b2ded5bed769bUL,
0x75c2ea5dc25d2875UL, 0xe11cd9241c24c5e1UL, 0x3dae7ae9aee9d43dUL, 0x4c6a98be6abef24cUL,
0x6c5ad8ee5aee826cUL, 0x7e41fcc341c3bd7eUL, 0xf502f1060206f3f5UL, 0x834f1dd14fd15283UL,
0x685cd0e45ce48c68UL, 0x51f4a207f4075651UL, 0xd134b95c345c8dd1UL, 0xf908e9180818e1f9UL,
0xe293dfae93ae4ce2UL, 0xab734d9573953eabUL, 0x6253c4f553f59762UL, 0x2a3f54413f416b2aUL,
0x080c10140c141c08UL, 0x955231f652f66395UL, 0x46658caf65afe946UL, 0x9d5e21e25ee27f9dUL,
0x3028607828784830UL, 0x37a16ef8a1f8cf37UL, 0x0a0f14110f111b0aUL, 0x2fb55ec4b5c4eb2fUL,
0x0e091c1b091b150eUL, 0x2436485a365a7e24UL, 0x1b9b36b69bb6ad1bUL, 0xdf3da5473d4798dfUL,
0xcd26816a266aa7cdUL, 0x4e699cbb69bbf54eUL, 0x7fcdfe4ccd4c337fUL, 0xea9fcfba9fba50eaUL,
0x121b242d1b2d3f12UL, 0x1d9e3ab99eb9a41dUL, 0x5874b09c749cc458UL, 0x342e68722e724634UL,
0x362d6c772d774136UL, 0xdcb2a3cdb2cd11dcUL, 0xb4ee7329ee299db4UL, 0x5bfbb616fb164d5bUL,
0xa4f65301f601a5a4UL, 0x764decd74dd7a176UL, 0xb76175a361a314b7UL, 0x7dcefa49ce49347dUL,
0x527ba48d7b8ddf52UL, 0xdd3ea1423e429fddUL, 0x5e71bc937193cd5eUL, 0x139726a297a2b113UL,
0xa6f55704f504a2a6UL, 0xb96869b868b801b9UL, 0x0000000000000000UL, 0xc12c99742c74b5c1UL,
0x406080a060a0e040UL, 0xe31fdd211f21c2e3UL, 0x79c8f243c8433a79UL, 0xb6ed772ced2c9ab6UL,
0xd4beb3d9bed90dd4UL, 0x8d4601ca46ca478dUL, 0x67d9ce70d9701767UL, 0x724be4dd4bddaf72UL,
0x94de3379de79ed94UL, 0x98d42b67d467ff98UL, 0xb0e87b23e82393b0UL, 0x854a11de4ade5b85UL,
0xbb6b6dbd6bbd06bbUL, 0xc52a917e2a7ebbc5UL, 0x4fe59e34e5347b4fUL, 0xed16c13a163ad7edUL,
0x86c51754c554d286UL, 0x9ad72f62d762f89aUL, 0x6655ccff55ff9966UL, 0x119422a794a7b611UL,
0x8acf0f4acf4ac08aUL, 0xe910c9301030d9e9UL, 0x0406080a060a0e04UL, 0xfe81e798819866feUL,
0xa0f05b0bf00baba0UL, 0x7844f0cc44ccb478UL, 0x25ba4ad5bad5f025UL, 0x4be3963ee33e754bUL,
0xa2f35f0ef30eaca2UL, 0x5dfeba19fe19445dUL, 0x80c01b5bc05bdb80UL, 0x058a0a858a858005UL,
0x3fad7eecadecd33fUL, 0x21bc42dfbcdffe21UL, 0x7048e0d848d8a870UL, 0xf104f90c040cfdf1UL,
0x63dfc67adf7a1963UL, 0x77c1ee58c1582f77UL, 0xaf75459f759f30afUL, 0x426384a563a5e742UL,
0x2030405030507020UL, 0xe51ad12e1a2ecbe5UL, 0xfd0ee1120e12effdUL, 0xbf6d65b76db708bfUL,
0x814c19d44cd45581UL, 0x1814303c143c2418UL, 0x26354c5f355f7926UL, 0xc32f9d712f71b2c3UL,
0xbee16738e13886beUL, 0x35a26afda2fdc835UL, 0x88cc0b4fcc4fc788UL, 0x2e395c4b394b652eUL,
0x93573df957f96a93UL, 0x55f2aa0df20d5855UL, 0xfc82e39d829d61fcUL, 0x7a47f4c947c9b37aUL,
0xc8ac8befacef27c8UL, 0xbae76f32e73288baUL, 0x322b647d2b7d4f32UL, 0xe695d7a495a442e6UL,
0xc0a09bfba0fb3bc0UL, 0x199832b398b3aa19UL, 0x9ed12768d168f69eUL, 0xa37f5d817f8122a3UL,
0x446688aa66aaee44UL, 0x547ea8827e82d654UL, 0x3bab76e6abe6dd3bUL, 0x0b83169e839e950bUL,
0x8cca0345ca45c98cUL, 0xc729957b297bbcc7UL, 0x6bd3d66ed36e056bUL, 0x283c50443c446c28UL,
0xa779558b798b2ca7UL, 0xbce2633de23d81bcUL, 0x161d2c271d273116UL, 0xad76419a769a37adUL,
0xdb3bad4d3b4d96dbUL, 0x6456c8fa56fa9e64UL, 0x744ee8d24ed2a674UL, 0x141e28221e223614UL,
0x92db3f76db76e492UL, 0x0c0a181e0a1e120cUL, 0x486c90b46cb4fc48UL, 0xb8e46b37e4378fb8UL,
0x9f5d25e75de7789fUL, 0xbd6e61b26eb20fbdUL, 0x43ef862aef2a6943UL, 0xc4a693f1a6f135c4UL,
0x39a872e3a8e3da39UL, 0x31a462f7a4f7c631UL, 0xd337bd5937598ad3UL, 0xf28bff868b8674f2UL,
0xd532b156325683d5UL, 0x8b430dc543c54e8bUL, 0x6e59dceb59eb856eUL, 0xdab7afc2b7c218daUL,
0x018c028f8c8f8e01UL, 0xb16479ac64ac1db1UL, 0x9cd2236dd26df19cUL, 0x49e0923be03b7249UL,
0xd8b4abc7b4c71fd8UL, 0xacfa4315fa15b9acUL, 0xf307fd090709faf3UL, 0xcf25856f256fa0cfUL,
0xcaaf8feaafea20caUL, 0xf48ef3898e897df4UL, 0x47e98e20e9206747UL, 0x1018202818283810UL,
0x6fd5de64d5640b6fUL, 0xf088fb83888373f0UL, 0x4a6f94b16fb1fb4aUL, 0x5c72b8967296ca5cUL,
0x3824706c246c5438UL, 0x57f1ae08f1085f57UL, 0x73c7e652c7522173UL, 0x975135f351f36497UL,
0xcb238d652365aecbUL, 0xa17c59847c8425a1UL, 0xe89ccbbf9cbf57e8UL, 0x3e217c6321635d3eUL,
0x96dd377cdd7cea96UL, 0x61dcc27fdc7f1e61UL, 0x0d861a9186919c0dUL, 0x0f851e9485949b0fUL,
0xe090dbab90ab4be0UL, 0x7c42f8c642c6ba7cUL, 0x71c4e257c4572671UL, 0xccaa83e5aae529ccUL,
0x90d83b73d873e390UL, 0x06050c0f050f0906UL, 0xf701f5030103f4f7UL, 0x1c12383612362a1cUL,
0xc2a39ffea3fe3cc2UL, 0x6a5fd4e15fe18b6aUL, 0xaef94710f910beaeUL, 0x69d0d26bd06b0269UL,
0x17912ea891a8bf17UL, 0x995829e858e87199UL, 0x3a2774692769533aUL, 0x27b94ed0b9d0f727UL,
0xd938a948384891d9UL, 0xeb13cd351335deebUL, 0x2bb356ceb3cee52bUL, 0x2233445533557722UL,
0xd2bbbfd6bbd604d2UL, 0xa9704990709039a9UL, 0x07890e8089808707UL, 0x33a766f2a7f2c133UL,
0x2db65ac1b6c1ec2dUL, 0x3c22786622665a3cUL, 0x15922aad92adb815UL, 0xc92089602060a9c9UL,
0x874915db49db5c87UL, 0xaaff4f1aff1ab0aaUL, 0x5078a0887888d850UL, 0xa57a518e7a8e2ba5UL,
0x038f068a8f8a8903UL, 0x59f8b213f8134a59UL, 0x0980129b809b9209UL, 0x1a1734391739231aUL,
0x65daca75da751065UL, 0xd731b553315384d7UL, 0x84c61351c651d584UL, 0xd0b8bbd3b8d303d0UL,
0x82c31f5ec35edc82UL, 0x29b052cbb0cbe229UL, 0x5a77b4997799c35aUL, 0x1e113c3311332d1eUL,
0x7bcbf646cb463d7bUL, 0xa8fc4b1ffc1fb7a8UL, 0x6dd6da61d6610c6dUL, 0x2c3a584e3a4e622cUL
};
__device__ __forceinline__
void GroestlPQ(uint64_t *HM, const uint64_t *H, const uint64_t *M, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
uint64_t QM[16];
for (int i = 0; i < 16; ++i) HM[i] = H[i] ^ M[i];
for (int i = 0; i < 16; ++i) QM[i] = M[i];
for (int i = 0; i < 14; ++i)
{
uint64_t Tmp1[16], Tmp2[16];
#pragma unroll
for (int x = 0; x < 16; ++x)
{
Tmp1[x] = HM[x] ^ PC64(x << 4, i);
Tmp2[x] = QM[x] ^ QC64(x << 4, i);
}
GROESTL_RBTT(HM[0], Tmp1, 0, 1, 2, 3, 4, 5, 6, 11);
GROESTL_RBTT(HM[1], Tmp1, 1, 2, 3, 4, 5, 6, 7, 12);
GROESTL_RBTT(HM[2], Tmp1, 2, 3, 4, 5, 6, 7, 8, 13);
GROESTL_RBTT(HM[3], Tmp1, 3, 4, 5, 6, 7, 8, 9, 14);
GROESTL_RBTT(HM[4], Tmp1, 4, 5, 6, 7, 8, 9, 10, 15);
GROESTL_RBTT(HM[5], Tmp1, 5, 6, 7, 8, 9, 10, 11, 0);
GROESTL_RBTT(HM[6], Tmp1, 6, 7, 8, 9, 10, 11, 12, 1);
GROESTL_RBTT(HM[7], Tmp1, 7, 8, 9, 10, 11, 12, 13, 2);
GROESTL_RBTT(HM[8], Tmp1, 8, 9, 10, 11, 12, 13, 14, 3);
GROESTL_RBTT(HM[9], Tmp1, 9, 10, 11, 12, 13, 14, 15, 4);
GROESTL_RBTT(HM[10], Tmp1, 10, 11, 12, 13, 14, 15, 0, 5);
GROESTL_RBTT(HM[11], Tmp1, 11, 12, 13, 14, 15, 0, 1, 6);
GROESTL_RBTT(HM[12], Tmp1, 12, 13, 14, 15, 0, 1, 2, 7);
GROESTL_RBTT(HM[13], Tmp1, 13, 14, 15, 0, 1, 2, 3, 8);
GROESTL_RBTT(HM[14], Tmp1, 14, 15, 0, 1, 2, 3, 4, 9);
GROESTL_RBTT(HM[15], Tmp1, 15, 0, 1, 2, 3, 4, 5, 10);
GROESTL_RBTT(QM[0], Tmp2, 1, 3, 5, 11, 0, 2, 4, 6);
GROESTL_RBTT(QM[1], Tmp2, 2, 4, 6, 12, 1, 3, 5, 7);
GROESTL_RBTT(QM[2], Tmp2, 3, 5, 7, 13, 2, 4, 6, 8);
GROESTL_RBTT(QM[3], Tmp2, 4, 6, 8, 14, 3, 5, 7, 9);
GROESTL_RBTT(QM[4], Tmp2, 5, 7, 9, 15, 4, 6, 8, 10);
GROESTL_RBTT(QM[5], Tmp2, 6, 8, 10, 0, 5, 7, 9, 11);
GROESTL_RBTT(QM[6], Tmp2, 7, 9, 11, 1, 6, 8, 10, 12);
GROESTL_RBTT(QM[7], Tmp2, 8, 10, 12, 2, 7, 9, 11, 13);
GROESTL_RBTT(QM[8], Tmp2, 9, 11, 13, 3, 8, 10, 12, 14);
GROESTL_RBTT(QM[9], Tmp2, 10, 12, 14, 4, 9, 11, 13, 15);
GROESTL_RBTT(QM[10], Tmp2, 11, 13, 15, 5, 10, 12, 14, 0);
GROESTL_RBTT(QM[11], Tmp2, 12, 14, 0, 6, 11, 13, 15, 1);
GROESTL_RBTT(QM[12], Tmp2, 13, 15, 1, 7, 12, 14, 0, 2);
GROESTL_RBTT(QM[13], Tmp2, 14, 0, 2, 8, 13, 15, 1, 3);
GROESTL_RBTT(QM[14], Tmp2, 15, 1, 3, 9, 14, 0, 2, 4);
GROESTL_RBTT(QM[15], Tmp2, 0, 2, 4, 10, 15, 1, 3, 5);
}
for (int i = 0; i < 16; ++i) HM[i] ^= QM[i] ^ H[i];
}
__device__ __forceinline__
void GroestlP(uint64_t *Out, const uint64_t *In, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
for (int i = 0; i < 16; ++i) Out[i] = In[i];
for (int i = 0; i < 14; ++i)
{
uint64_t H[16];
#pragma unroll
for (int x = 0; x < 16; ++x)
H[x] = Out[x] ^ PC64(x << 4, i);
GROESTL_RBTT(Out[0], H, 0, 1, 2, 3, 4, 5, 6, 11);
GROESTL_RBTT(Out[1], H, 1, 2, 3, 4, 5, 6, 7, 12);
GROESTL_RBTT(Out[2], H, 2, 3, 4, 5, 6, 7, 8, 13);
GROESTL_RBTT(Out[3], H, 3, 4, 5, 6, 7, 8, 9, 14);
GROESTL_RBTT(Out[4], H, 4, 5, 6, 7, 8, 9, 10, 15);
GROESTL_RBTT(Out[5], H, 5, 6, 7, 8, 9, 10, 11, 0);
GROESTL_RBTT(Out[6], H, 6, 7, 8, 9, 10, 11, 12, 1);
GROESTL_RBTT(Out[7], H, 7, 8, 9, 10, 11, 12, 13, 2);
GROESTL_RBTT(Out[8], H, 8, 9, 10, 11, 12, 13, 14, 3);
GROESTL_RBTT(Out[9], H, 9, 10, 11, 12, 13, 14, 15, 4);
GROESTL_RBTT(Out[10], H, 10, 11, 12, 13, 14, 15, 0, 5);
GROESTL_RBTT(Out[11], H, 11, 12, 13, 14, 15, 0, 1, 6);
GROESTL_RBTT(Out[12], H, 12, 13, 14, 15, 0, 1, 2, 7);
GROESTL_RBTT(Out[13], H, 13, 14, 15, 0, 1, 2, 3, 8);
GROESTL_RBTT(Out[14], H, 14, 15, 0, 1, 2, 3, 4, 9);
GROESTL_RBTT(Out[15], H, 15, 0, 1, 2, 3, 4, 5, 10);
}
}
#define WORKSIZE 64
__device__ __forceinline__
void GroestlCompress(uint64_t *State, uint64_t *Msg, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
uint64_t Output[16];
GroestlPQ(Output, State, Msg, T0, T1, T2, T3);
for (int i = 0; i < 16; ++i) State[i] = Output[i];
}
__global__
void quark_groestl512_gpu_hash_128(uint32_t threads, uint64_t *const __restrict__ g_hash)
{
uint64_t T0[256], T1[256], T2[256], T3[256];
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
const uint32_t hashPosition = thread;
uint64_t *inpHash = &g_hash[8 * hashPosition];
for (int i = 0; i < 256; i += WORKSIZE)
{
const uint64_t tmp = T0_G[i];
T0[i] = tmp;
T1[i] = ROTL64(tmp, 8UL);
T2[i] = ROTL64(tmp, 16UL);
T3[i] = ROTL64(tmp, 24UL);
}
uint64_t M[16] = { 0 }, H[16] = { 0 }, H2[16];
#pragma unroll
for (int i = 0; i < 16; ++i) M[i] = cuda_swab32(((uint32_t *)inpHash)[i]);
//M[8] = 0x80UL;
//M[15] = 0x0100000000000000UL;
H[15] = 0x0002000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
M[0] = 0x80UL;
for (int i = 1; i < 15; ++i) M[i] = 0x00UL;
M[15] = 0x0200000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
GroestlP(H2, H, T0, T1, T2, T3);
// vstore8(VSWAP8(((ulong8 *)H2)[1] ^ ((ulong8 *)H)[1]), 0, hash->h8);
#pragma unroll
for (int i = 8; i < 16; ++i)
{
H2[i] = (H2[i] ^ H[i]);
}
inpHash[0] = H2[0 + 8];
inpHash[1] = H2[1 + 8];
inpHash[2] = H2[2 + 8];
inpHash[3] = H2[3 + 8];
inpHash[4] = H2[4 + 8];
inpHash[5] = H2[5 + 8];
inpHash[6] = H2[6 + 8];
inpHash[7] = H2[7 + 8];
}
}
broken..
Code:
// Macros and table for Wolf's OpenCL Groestl implementation
#define BYTE(x, y) (bfe((uint32_t)((x) >> ((y >= 32U) ? 32U : 0U)), (y) - (((y) >= 32) ? 32U : 0), 8U))
#define B64_0(x) ((uint8_t)(x))
#define B64_1(x) BYTE((x), 8U)
#define B64_2(x) BYTE((x), 16U)
#define B64_3(x) BYTE((x), 24U)
#define B64_4(x) BYTE((x), 32U)
#define B64_5(x) BYTE((x), 40U)
#define B64_6(x) BYTE((x), 48U)
#define B64_7(x) BYTE((x), 56U)
#define GROESTL_RBTT(d, Hval, b0, b1, b2, b3, b4, b5, b6, b7)\
d = (T0[B64_0(Hval[b0])] ^ T1[B64_1(Hval[b1])] ^ T2[B64_2(Hval[b2])] ^ T3[B64_3(Hval[b3])] \
^ SWAPDWORDS(T0[B64_4(Hval[b4])]) ^ SWAPDWORDS(T1[B64_5(Hval[b5])]) ^ SWAPDWORDS(T2[B64_6(Hval[b6])]) ^ SWAPDWORDS(T3[B64_7(Hval[b7])]));
#define PC64(j, r) (uint64_t)((j) | (r))
#define QC64(j, r) ROTL64(((uint64_t)(r)) ^ (~((uint64_t)(j))), 56UL)
static const __constant__ uint64_t T0_G[] =
{
0xc6a597f4a5f432c6UL, 0xf884eb9784976ff8UL, 0xee99c7b099b05eeeUL, 0xf68df78c8d8c7af6UL,
0xff0de5170d17e8ffUL, 0xd6bdb7dcbddc0ad6UL, 0xdeb1a7c8b1c816deUL, 0x915439fc54fc6d91UL,
0x6050c0f050f09060UL, 0x0203040503050702UL, 0xcea987e0a9e02eceUL, 0x567dac877d87d156UL,
0xe719d52b192bcce7UL, 0xb56271a662a613b5UL, 0x4de69a31e6317c4dUL, 0xec9ac3b59ab559ecUL,
0x8f4505cf45cf408fUL, 0x1f9d3ebc9dbca31fUL, 0x894009c040c04989UL, 0xfa87ef92879268faUL,
0xef15c53f153fd0efUL, 0xb2eb7f26eb2694b2UL, 0x8ec90740c940ce8eUL, 0xfb0bed1d0b1de6fbUL,
0x41ec822fec2f6e41UL, 0xb3677da967a91ab3UL, 0x5ffdbe1cfd1c435fUL, 0x45ea8a25ea256045UL,
0x23bf46dabfdaf923UL, 0x53f7a602f7025153UL, 0xe496d3a196a145e4UL, 0x9b5b2ded5bed769bUL,
0x75c2ea5dc25d2875UL, 0xe11cd9241c24c5e1UL, 0x3dae7ae9aee9d43dUL, 0x4c6a98be6abef24cUL,
0x6c5ad8ee5aee826cUL, 0x7e41fcc341c3bd7eUL, 0xf502f1060206f3f5UL, 0x834f1dd14fd15283UL,
0x685cd0e45ce48c68UL, 0x51f4a207f4075651UL, 0xd134b95c345c8dd1UL, 0xf908e9180818e1f9UL,
0xe293dfae93ae4ce2UL, 0xab734d9573953eabUL, 0x6253c4f553f59762UL, 0x2a3f54413f416b2aUL,
0x080c10140c141c08UL, 0x955231f652f66395UL, 0x46658caf65afe946UL, 0x9d5e21e25ee27f9dUL,
0x3028607828784830UL, 0x37a16ef8a1f8cf37UL, 0x0a0f14110f111b0aUL, 0x2fb55ec4b5c4eb2fUL,
0x0e091c1b091b150eUL, 0x2436485a365a7e24UL, 0x1b9b36b69bb6ad1bUL, 0xdf3da5473d4798dfUL,
0xcd26816a266aa7cdUL, 0x4e699cbb69bbf54eUL, 0x7fcdfe4ccd4c337fUL, 0xea9fcfba9fba50eaUL,
0x121b242d1b2d3f12UL, 0x1d9e3ab99eb9a41dUL, 0x5874b09c749cc458UL, 0x342e68722e724634UL,
0x362d6c772d774136UL, 0xdcb2a3cdb2cd11dcUL, 0xb4ee7329ee299db4UL, 0x5bfbb616fb164d5bUL,
0xa4f65301f601a5a4UL, 0x764decd74dd7a176UL, 0xb76175a361a314b7UL, 0x7dcefa49ce49347dUL,
0x527ba48d7b8ddf52UL, 0xdd3ea1423e429fddUL, 0x5e71bc937193cd5eUL, 0x139726a297a2b113UL,
0xa6f55704f504a2a6UL, 0xb96869b868b801b9UL, 0x0000000000000000UL, 0xc12c99742c74b5c1UL,
0x406080a060a0e040UL, 0xe31fdd211f21c2e3UL, 0x79c8f243c8433a79UL, 0xb6ed772ced2c9ab6UL,
0xd4beb3d9bed90dd4UL, 0x8d4601ca46ca478dUL, 0x67d9ce70d9701767UL, 0x724be4dd4bddaf72UL,
0x94de3379de79ed94UL, 0x98d42b67d467ff98UL, 0xb0e87b23e82393b0UL, 0x854a11de4ade5b85UL,
0xbb6b6dbd6bbd06bbUL, 0xc52a917e2a7ebbc5UL, 0x4fe59e34e5347b4fUL, 0xed16c13a163ad7edUL,
0x86c51754c554d286UL, 0x9ad72f62d762f89aUL, 0x6655ccff55ff9966UL, 0x119422a794a7b611UL,
0x8acf0f4acf4ac08aUL, 0xe910c9301030d9e9UL, 0x0406080a060a0e04UL, 0xfe81e798819866feUL,
0xa0f05b0bf00baba0UL, 0x7844f0cc44ccb478UL, 0x25ba4ad5bad5f025UL, 0x4be3963ee33e754bUL,
0xa2f35f0ef30eaca2UL, 0x5dfeba19fe19445dUL, 0x80c01b5bc05bdb80UL, 0x058a0a858a858005UL,
0x3fad7eecadecd33fUL, 0x21bc42dfbcdffe21UL, 0x7048e0d848d8a870UL, 0xf104f90c040cfdf1UL,
0x63dfc67adf7a1963UL, 0x77c1ee58c1582f77UL, 0xaf75459f759f30afUL, 0x426384a563a5e742UL,
0x2030405030507020UL, 0xe51ad12e1a2ecbe5UL, 0xfd0ee1120e12effdUL, 0xbf6d65b76db708bfUL,
0x814c19d44cd45581UL, 0x1814303c143c2418UL, 0x26354c5f355f7926UL, 0xc32f9d712f71b2c3UL,
0xbee16738e13886beUL, 0x35a26afda2fdc835UL, 0x88cc0b4fcc4fc788UL, 0x2e395c4b394b652eUL,
0x93573df957f96a93UL, 0x55f2aa0df20d5855UL, 0xfc82e39d829d61fcUL, 0x7a47f4c947c9b37aUL,
0xc8ac8befacef27c8UL, 0xbae76f32e73288baUL, 0x322b647d2b7d4f32UL, 0xe695d7a495a442e6UL,
0xc0a09bfba0fb3bc0UL, 0x199832b398b3aa19UL, 0x9ed12768d168f69eUL, 0xa37f5d817f8122a3UL,
0x446688aa66aaee44UL, 0x547ea8827e82d654UL, 0x3bab76e6abe6dd3bUL, 0x0b83169e839e950bUL,
0x8cca0345ca45c98cUL, 0xc729957b297bbcc7UL, 0x6bd3d66ed36e056bUL, 0x283c50443c446c28UL,
0xa779558b798b2ca7UL, 0xbce2633de23d81bcUL, 0x161d2c271d273116UL, 0xad76419a769a37adUL,
0xdb3bad4d3b4d96dbUL, 0x6456c8fa56fa9e64UL, 0x744ee8d24ed2a674UL, 0x141e28221e223614UL,
0x92db3f76db76e492UL, 0x0c0a181e0a1e120cUL, 0x486c90b46cb4fc48UL, 0xb8e46b37e4378fb8UL,
0x9f5d25e75de7789fUL, 0xbd6e61b26eb20fbdUL, 0x43ef862aef2a6943UL, 0xc4a693f1a6f135c4UL,
0x39a872e3a8e3da39UL, 0x31a462f7a4f7c631UL, 0xd337bd5937598ad3UL, 0xf28bff868b8674f2UL,
0xd532b156325683d5UL, 0x8b430dc543c54e8bUL, 0x6e59dceb59eb856eUL, 0xdab7afc2b7c218daUL,
0x018c028f8c8f8e01UL, 0xb16479ac64ac1db1UL, 0x9cd2236dd26df19cUL, 0x49e0923be03b7249UL,
0xd8b4abc7b4c71fd8UL, 0xacfa4315fa15b9acUL, 0xf307fd090709faf3UL, 0xcf25856f256fa0cfUL,
0xcaaf8feaafea20caUL, 0xf48ef3898e897df4UL, 0x47e98e20e9206747UL, 0x1018202818283810UL,
0x6fd5de64d5640b6fUL, 0xf088fb83888373f0UL, 0x4a6f94b16fb1fb4aUL, 0x5c72b8967296ca5cUL,
0x3824706c246c5438UL, 0x57f1ae08f1085f57UL, 0x73c7e652c7522173UL, 0x975135f351f36497UL,
0xcb238d652365aecbUL, 0xa17c59847c8425a1UL, 0xe89ccbbf9cbf57e8UL, 0x3e217c6321635d3eUL,
0x96dd377cdd7cea96UL, 0x61dcc27fdc7f1e61UL, 0x0d861a9186919c0dUL, 0x0f851e9485949b0fUL,
0xe090dbab90ab4be0UL, 0x7c42f8c642c6ba7cUL, 0x71c4e257c4572671UL, 0xccaa83e5aae529ccUL,
0x90d83b73d873e390UL, 0x06050c0f050f0906UL, 0xf701f5030103f4f7UL, 0x1c12383612362a1cUL,
0xc2a39ffea3fe3cc2UL, 0x6a5fd4e15fe18b6aUL, 0xaef94710f910beaeUL, 0x69d0d26bd06b0269UL,
0x17912ea891a8bf17UL, 0x995829e858e87199UL, 0x3a2774692769533aUL, 0x27b94ed0b9d0f727UL,
0xd938a948384891d9UL, 0xeb13cd351335deebUL, 0x2bb356ceb3cee52bUL, 0x2233445533557722UL,
0xd2bbbfd6bbd604d2UL, 0xa9704990709039a9UL, 0x07890e8089808707UL, 0x33a766f2a7f2c133UL,
0x2db65ac1b6c1ec2dUL, 0x3c22786622665a3cUL, 0x15922aad92adb815UL, 0xc92089602060a9c9UL,
0x874915db49db5c87UL, 0xaaff4f1aff1ab0aaUL, 0x5078a0887888d850UL, 0xa57a518e7a8e2ba5UL,
0x038f068a8f8a8903UL, 0x59f8b213f8134a59UL, 0x0980129b809b9209UL, 0x1a1734391739231aUL,
0x65daca75da751065UL, 0xd731b553315384d7UL, 0x84c61351c651d584UL, 0xd0b8bbd3b8d303d0UL,
0x82c31f5ec35edc82UL, 0x29b052cbb0cbe229UL, 0x5a77b4997799c35aUL, 0x1e113c3311332d1eUL,
0x7bcbf646cb463d7bUL, 0xa8fc4b1ffc1fb7a8UL, 0x6dd6da61d6610c6dUL, 0x2c3a584e3a4e622cUL
};
__device__ __forceinline__
void GroestlPQ(uint64_t *HM, const uint64_t *H, const uint64_t *M, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
uint64_t QM[16];
for (int i = 0; i < 16; ++i) HM[i] = H[i] ^ M[i];
for (int i = 0; i < 16; ++i) QM[i] = M[i];
for (int i = 0; i < 14; ++i)
{
uint64_t Tmp1[16], Tmp2[16];
#pragma unroll
for (int x = 0; x < 16; ++x)
{
Tmp1[x] = HM[x] ^ PC64(x << 4, i);
Tmp2[x] = QM[x] ^ QC64(x << 4, i);
}
GROESTL_RBTT(HM[0], Tmp1, 0, 1, 2, 3, 4, 5, 6, 11);
GROESTL_RBTT(HM[1], Tmp1, 1, 2, 3, 4, 5, 6, 7, 12);
GROESTL_RBTT(HM[2], Tmp1, 2, 3, 4, 5, 6, 7, 8, 13);
GROESTL_RBTT(HM[3], Tmp1, 3, 4, 5, 6, 7, 8, 9, 14);
GROESTL_RBTT(HM[4], Tmp1, 4, 5, 6, 7, 8, 9, 10, 15);
GROESTL_RBTT(HM[5], Tmp1, 5, 6, 7, 8, 9, 10, 11, 0);
GROESTL_RBTT(HM[6], Tmp1, 6, 7, 8, 9, 10, 11, 12, 1);
GROESTL_RBTT(HM[7], Tmp1, 7, 8, 9, 10, 11, 12, 13, 2);
GROESTL_RBTT(HM[8], Tmp1, 8, 9, 10, 11, 12, 13, 14, 3);
GROESTL_RBTT(HM[9], Tmp1, 9, 10, 11, 12, 13, 14, 15, 4);
GROESTL_RBTT(HM[10], Tmp1, 10, 11, 12, 13, 14, 15, 0, 5);
GROESTL_RBTT(HM[11], Tmp1, 11, 12, 13, 14, 15, 0, 1, 6);
GROESTL_RBTT(HM[12], Tmp1, 12, 13, 14, 15, 0, 1, 2, 7);
GROESTL_RBTT(HM[13], Tmp1, 13, 14, 15, 0, 1, 2, 3, 8);
GROESTL_RBTT(HM[14], Tmp1, 14, 15, 0, 1, 2, 3, 4, 9);
GROESTL_RBTT(HM[15], Tmp1, 15, 0, 1, 2, 3, 4, 5, 10);
GROESTL_RBTT(QM[0], Tmp2, 1, 3, 5, 11, 0, 2, 4, 6);
GROESTL_RBTT(QM[1], Tmp2, 2, 4, 6, 12, 1, 3, 5, 7);
GROESTL_RBTT(QM[2], Tmp2, 3, 5, 7, 13, 2, 4, 6, 8);
GROESTL_RBTT(QM[3], Tmp2, 4, 6, 8, 14, 3, 5, 7, 9);
GROESTL_RBTT(QM[4], Tmp2, 5, 7, 9, 15, 4, 6, 8, 10);
GROESTL_RBTT(QM[5], Tmp2, 6, 8, 10, 0, 5, 7, 9, 11);
GROESTL_RBTT(QM[6], Tmp2, 7, 9, 11, 1, 6, 8, 10, 12);
GROESTL_RBTT(QM[7], Tmp2, 8, 10, 12, 2, 7, 9, 11, 13);
GROESTL_RBTT(QM[8], Tmp2, 9, 11, 13, 3, 8, 10, 12, 14);
GROESTL_RBTT(QM[9], Tmp2, 10, 12, 14, 4, 9, 11, 13, 15);
GROESTL_RBTT(QM[10], Tmp2, 11, 13, 15, 5, 10, 12, 14, 0);
GROESTL_RBTT(QM[11], Tmp2, 12, 14, 0, 6, 11, 13, 15, 1);
GROESTL_RBTT(QM[12], Tmp2, 13, 15, 1, 7, 12, 14, 0, 2);
GROESTL_RBTT(QM[13], Tmp2, 14, 0, 2, 8, 13, 15, 1, 3);
GROESTL_RBTT(QM[14], Tmp2, 15, 1, 3, 9, 14, 0, 2, 4);
GROESTL_RBTT(QM[15], Tmp2, 0, 2, 4, 10, 15, 1, 3, 5);
}
for (int i = 0; i < 16; ++i) HM[i] ^= QM[i] ^ H[i];
}
__device__ __forceinline__
void GroestlP(uint64_t *Out, const uint64_t *In, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
for (int i = 0; i < 16; ++i) Out[i] = In[i];
for (int i = 0; i < 14; ++i)
{
uint64_t H[16];
#pragma unroll
for (int x = 0; x < 16; ++x)
H[x] = Out[x] ^ PC64(x << 4, i);
GROESTL_RBTT(Out[0], H, 0, 1, 2, 3, 4, 5, 6, 11);
GROESTL_RBTT(Out[1], H, 1, 2, 3, 4, 5, 6, 7, 12);
GROESTL_RBTT(Out[2], H, 2, 3, 4, 5, 6, 7, 8, 13);
GROESTL_RBTT(Out[3], H, 3, 4, 5, 6, 7, 8, 9, 14);
GROESTL_RBTT(Out[4], H, 4, 5, 6, 7, 8, 9, 10, 15);
GROESTL_RBTT(Out[5], H, 5, 6, 7, 8, 9, 10, 11, 0);
GROESTL_RBTT(Out[6], H, 6, 7, 8, 9, 10, 11, 12, 1);
GROESTL_RBTT(Out[7], H, 7, 8, 9, 10, 11, 12, 13, 2);
GROESTL_RBTT(Out[8], H, 8, 9, 10, 11, 12, 13, 14, 3);
GROESTL_RBTT(Out[9], H, 9, 10, 11, 12, 13, 14, 15, 4);
GROESTL_RBTT(Out[10], H, 10, 11, 12, 13, 14, 15, 0, 5);
GROESTL_RBTT(Out[11], H, 11, 12, 13, 14, 15, 0, 1, 6);
GROESTL_RBTT(Out[12], H, 12, 13, 14, 15, 0, 1, 2, 7);
GROESTL_RBTT(Out[13], H, 13, 14, 15, 0, 1, 2, 3, 8);
GROESTL_RBTT(Out[14], H, 14, 15, 0, 1, 2, 3, 4, 9);
GROESTL_RBTT(Out[15], H, 15, 0, 1, 2, 3, 4, 5, 10);
}
}
#define WORKSIZE 64
__device__ __forceinline__
void GroestlCompress(uint64_t *State, uint64_t *Msg, uint64_t *T0, uint64_t *T1, uint64_t *T2, uint64_t *T3)
{
uint64_t Output[16];
GroestlPQ(Output, State, Msg, T0, T1, T2, T3);
for (int i = 0; i < 16; ++i) State[i] = Output[i];
}
__global__
void quark_groestl512_gpu_hash_128(uint32_t threads, uint64_t *const __restrict__ g_hash)
{
uint64_t T0[256], T1[256], T2[256], T3[256];
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
const uint32_t hashPosition = thread;
uint64_t *inpHash = &g_hash[8 * hashPosition];
for (int i = 0; i < 256; i += WORKSIZE)
{
const uint64_t tmp = T0_G[i];
T0[i] = tmp;
T1[i] = ROTL64(tmp, 8UL);
T2[i] = ROTL64(tmp, 16UL);
T3[i] = ROTL64(tmp, 24UL);
}
uint64_t M[16] = { 0 }, H[16] = { 0 }, H2[16];
#pragma unroll
for (int i = 0; i < 16; ++i) M[i] = cuda_swab32(((uint32_t *)inpHash)[i]);
//M[8] = 0x80UL;
//M[15] = 0x0100000000000000UL;
H[15] = 0x0002000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
M[0] = 0x80UL;
for (int i = 1; i < 15; ++i) M[i] = 0x00UL;
M[15] = 0x0200000000000000UL;
GroestlCompress(H, M, T0, T1, T2, T3);
GroestlP(H2, H, T0, T1, T2, T3);
// vstore8(VSWAP8(((ulong8 *)H2)[1] ^ ((ulong8 *)H)[1]), 0, hash->h8);
#pragma unroll
for (int i = 8; i < 16; ++i)
{
H2[i] = (H2[i] ^ H[i]);
}
inpHash[0] = H2[0 + 8];
inpHash[1] = H2[1 + 8];
inpHash[2] = H2[2 + 8];
inpHash[3] = H2[3 + 8];
inpHash[4] = H2[4 + 8];
inpHash[5] = H2[5 + 8];
inpHash[6] = H2[6 + 8];
inpHash[7] = H2[7 + 8];
}
}
Sp_ is preparing his next 0.5 BTC by asking
Compression512(h, msg0);
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