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lyra2z algo (temporary algo)

based on djm34 version, cleaned up and adapted to ccminer 2.0
2upstream
Tanguy Pruvot 8 years ago
parent
commit
1b7c2fc296
  1. 1
      Makefile.am
  2. 2
      algos.h
  3. 1
      bench.cpp
  4. 6
      ccminer.cpp
  5. 20
      ccminer.vcxproj
  6. 15
      ccminer.vcxproj.filters
  7. 215
      lyra2/Lyra2Z.c
  8. 42
      lyra2/Lyra2Z.h
  9. 966
      lyra2/cuda_lyra2Z.cu
  10. 819
      lyra2/cuda_lyra2Z_sm5.cuh
  11. 8
      lyra2/cuda_lyra2_vectors.h
  12. 164
      lyra2/lyra2Z.cu
  13. 3
      miner.h
  14. 3
      util.cpp

1
Makefile.am

@ -34,6 +34,7 @@ ccminer_SOURCES = elist.h miner.h compat.h \
lyra2/Lyra2.c lyra2/Sponge.c \ lyra2/Lyra2.c lyra2/Sponge.c \
lyra2/lyra2RE.cu lyra2/cuda_lyra2.cu \ lyra2/lyra2RE.cu lyra2/cuda_lyra2.cu \
lyra2/lyra2REv2.cu lyra2/cuda_lyra2v2.cu \ lyra2/lyra2REv2.cu lyra2/cuda_lyra2v2.cu \
lyra2/Lyra2Z.c lyra2/lyra2Z.cu lyra2/cuda_lyra2Z.cu \
Algo256/cuda_bmw256.cu Algo256/cuda_cubehash256.cu \ Algo256/cuda_bmw256.cu Algo256/cuda_cubehash256.cu \
Algo256/cuda_blake256.cu Algo256/cuda_groestl256.cu Algo256/cuda_keccak256.cu Algo256/cuda_skein256.cu \ Algo256/cuda_blake256.cu Algo256/cuda_groestl256.cu Algo256/cuda_keccak256.cu Algo256/cuda_skein256.cu \
Algo256/blake256.cu Algo256/decred.cu Algo256/vanilla.cu Algo256/keccak256.cu \ Algo256/blake256.cu Algo256/decred.cu Algo256/vanilla.cu Algo256/keccak256.cu \

2
algos.h

@ -26,6 +26,7 @@ enum sha_algos {
ALGO_LUFFA, ALGO_LUFFA,
ALGO_LYRA2, ALGO_LYRA2,
ALGO_LYRA2v2, ALGO_LYRA2v2,
ALGO_LYRA2Z,
ALGO_MJOLLNIR, /* Hefty hash */ ALGO_MJOLLNIR, /* Hefty hash */
ALGO_MYR_GR, ALGO_MYR_GR,
ALGO_NEOSCRYPT, ALGO_NEOSCRYPT,
@ -82,6 +83,7 @@ static const char *algo_names[] = {
"luffa", "luffa",
"lyra2", "lyra2",
"lyra2v2", "lyra2v2",
"lyra2z",
"mjollnir", "mjollnir",
"myr-gr", "myr-gr",
"neoscrypt", "neoscrypt",

1
bench.cpp

@ -63,6 +63,7 @@ void algo_free_all(int thr_id)
free_luffa(thr_id); free_luffa(thr_id);
free_lyra2(thr_id); free_lyra2(thr_id);
free_lyra2v2(thr_id); free_lyra2v2(thr_id);
free_lyra2Z(thr_id);
free_myriad(thr_id); free_myriad(thr_id);
free_neoscrypt(thr_id); free_neoscrypt(thr_id);
free_nist5(thr_id); free_nist5(thr_id);

6
ccminer.cpp

@ -244,6 +244,7 @@ Options:\n\
luffa Joincoin\n\ luffa Joincoin\n\
lyra2 CryptoCoin\n\ lyra2 CryptoCoin\n\
lyra2v2 VertCoin\n\ lyra2v2 VertCoin\n\
lyra2z ZeroCoin (3rd impl)\n\
mjollnir Mjollnircoin\n\ mjollnir Mjollnircoin\n\
myr-gr Myriad-Groestl\n\ myr-gr Myriad-Groestl\n\
neoscrypt FeatherCoin, Phoenix, UFO...\n\ neoscrypt FeatherCoin, Phoenix, UFO...\n\
@ -1616,6 +1617,7 @@ static bool stratum_gen_work(struct stratum_ctx *sctx, struct work *work)
case ALGO_GROESTL: case ALGO_GROESTL:
case ALGO_LBRY: case ALGO_LBRY:
case ALGO_LYRA2v2: case ALGO_LYRA2v2:
case ALGO_LYRA2Z:
work_set_target(work, sctx->job.diff / (256.0 * opt_difficulty)); work_set_target(work, sctx->job.diff / (256.0 * opt_difficulty));
break; break;
case ALGO_KECCAK: case ALGO_KECCAK:
@ -2131,6 +2133,7 @@ static void *miner_thread(void *userdata)
minmax = 0x300000; minmax = 0x300000;
break; break;
case ALGO_LYRA2: case ALGO_LYRA2:
case ALGO_LYRA2Z:
case ALGO_NEOSCRYPT: case ALGO_NEOSCRYPT:
case ALGO_SIB: case ALGO_SIB:
case ALGO_SCRYPT: case ALGO_SCRYPT:
@ -2272,6 +2275,9 @@ static void *miner_thread(void *userdata)
case ALGO_LYRA2v2: case ALGO_LYRA2v2:
rc = scanhash_lyra2v2(thr_id, &work, max_nonce, &hashes_done); rc = scanhash_lyra2v2(thr_id, &work, max_nonce, &hashes_done);
break; break;
case ALGO_LYRA2Z:
rc = scanhash_lyra2Z(thr_id, &work, max_nonce, &hashes_done);
break;
case ALGO_NEOSCRYPT: case ALGO_NEOSCRYPT:
rc = scanhash_neoscrypt(thr_id, &work, max_nonce, &hashes_done); rc = scanhash_neoscrypt(thr_id, &work, max_nonce, &hashes_done);
break; break;

20
ccminer.vcxproj

@ -39,10 +39,10 @@
</PropertyGroup> </PropertyGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" /> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
<ImportGroup Label="ExtensionSettings" Condition="'$(Platform)'=='Win32'"> <ImportGroup Label="ExtensionSettings" Condition="'$(Platform)'=='Win32'">
<Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 7.5.props" /> <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 6.5.props" />
</ImportGroup> </ImportGroup>
<ImportGroup Label="ExtensionSettings" Condition="'$(Platform)'=='x64'"> <ImportGroup Label="ExtensionSettings" Condition="'$(Platform)'=='x64'">
<Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 8.0.props" /> <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 6.5.props" />
</ImportGroup> </ImportGroup>
<ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'"> <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
<Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" /> <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
@ -256,7 +256,7 @@
<ClCompile Include="myriadgroestl.cpp" /> <ClCompile Include="myriadgroestl.cpp" />
<ClCompile Include="lyra2\Lyra2.c" /> <ClCompile Include="lyra2\Lyra2.c" />
<ClCompile Include="lyra2\Sponge.c" /> <ClCompile Include="lyra2\Sponge.c" />
<ClInclude Include="lyra2\cuda_lyra2_sm2.cuh" /> <ClCompile Include="lyra2\Lyra2Z.c" />
<ClInclude Include="neoscrypt\neoscrypt.h" /> <ClInclude Include="neoscrypt\neoscrypt.h" />
<ClCompile Include="neoscrypt\neoscrypt.cpp" /> <ClCompile Include="neoscrypt\neoscrypt.cpp" />
<ClCompile Include="neoscrypt\neoscrypt-cpu.c" /> <ClCompile Include="neoscrypt\neoscrypt-cpu.c" />
@ -383,7 +383,7 @@
<ClInclude Include="uint256.h" /> <ClInclude Include="uint256.h" />
<ClInclude Include="lyra2\Lyra2.h" /> <ClInclude Include="lyra2\Lyra2.h" />
<ClInclude Include="lyra2\Sponge.h" /> <ClInclude Include="lyra2\Sponge.h" />
<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh" /> <ClInclude Include="lyra2\Lyra2Z.h" />
<ClInclude Include="quark\groestl_transf_quad.h" /> <ClInclude Include="quark\groestl_transf_quad.h" />
<ClInclude Include="quark\groestl_functions_quad.h" /> <ClInclude Include="quark\groestl_functions_quad.h" />
<ClInclude Include="quark\cuda_quark.h" /> <ClInclude Include="quark\cuda_quark.h" />
@ -505,6 +505,11 @@
<CudaCompile Include="lyra2\cuda_lyra2.cu" /> <CudaCompile Include="lyra2\cuda_lyra2.cu" />
<CudaCompile Include="lyra2\lyra2REv2.cu" /> <CudaCompile Include="lyra2\lyra2REv2.cu" />
<CudaCompile Include="lyra2\cuda_lyra2v2.cu" /> <CudaCompile Include="lyra2\cuda_lyra2v2.cu" />
<ClInclude Include="lyra2\cuda_lyra2_sm2.cuh" />
<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh" />
<CudaCompile Include="lyra2\lyra2Z.cu" />
<CudaCompile Include="lyra2\cuda_lyra2Z.cu" />
<ClInclude Include="lyra2\cuda_lyra2Z_sm5.cuh" />
<CudaCompile Include="sia\sia.cu" /> <CudaCompile Include="sia\sia.cu" />
<CudaCompile Include="skein.cu"> <CudaCompile Include="skein.cu">
<MaxRegCount>64</MaxRegCount> <MaxRegCount>64</MaxRegCount>
@ -567,11 +572,8 @@
<Text Include="README.txt" /> <Text Include="README.txt" />
</ItemGroup> </ItemGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" /> <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
<ImportGroup Label="ExtensionTargets" Condition="'$(Platform)'=='Win32'"> <ImportGroup Label="ExtensionTargets">
<Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 7.5.targets" /> <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 6.5.targets" />
</ImportGroup>
<ImportGroup Label="ExtensionTargets" Condition="'$(Platform)'=='x64'">
<Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 8.0.targets" />
</ImportGroup> </ImportGroup>
<!-- Copy the required dlls --> <!-- Copy the required dlls -->
<Target Name="AfterBuild"> <Target Name="AfterBuild">

15
ccminer.vcxproj.filters

@ -255,6 +255,9 @@
<ClCompile Include="lyra2\Sponge.c"> <ClCompile Include="lyra2\Sponge.c">
<Filter>Source Files\sph</Filter> <Filter>Source Files\sph</Filter>
</ClCompile> </ClCompile>
<ClCompile Include="lyra2\Lyra2Z.c">
<Filter>Source Files\sph</Filter>
</ClCompile>
<ClCompile Include="scrypt.cpp"> <ClCompile Include="scrypt.cpp">
<Filter>Source Files\CUDA\scrypt</Filter> <Filter>Source Files\CUDA\scrypt</Filter>
</ClCompile> </ClCompile>
@ -473,6 +476,9 @@
<ClInclude Include="lyra2\Lyra2.h"> <ClInclude Include="lyra2\Lyra2.h">
<Filter>Header Files\lyra2</Filter> <Filter>Header Files\lyra2</Filter>
</ClInclude> </ClInclude>
<ClInclude Include="lyra2\Lyra2Z.h">
<Filter>Header Files\lyra2</Filter>
</ClInclude>
<ClInclude Include="lyra2\Sponge.h"> <ClInclude Include="lyra2\Sponge.h">
<Filter>Header Files\lyra2</Filter> <Filter>Header Files\lyra2</Filter>
</ClInclude> </ClInclude>
@ -506,6 +512,9 @@
<ClInclude Include="lyra2\cuda_lyra2_sm2.cuh"> <ClInclude Include="lyra2\cuda_lyra2_sm2.cuh">
<Filter>Source Files\CUDA\lyra2</Filter> <Filter>Source Files\CUDA\lyra2</Filter>
</ClInclude> </ClInclude>
<ClInclude Include="lyra2\cuda_lyra2Z_sm5.cuh">
<Filter>Source Files\CUDA\lyra2</Filter>
</ClInclude>
<ClInclude Include="quark\cuda_quark_blake512_sp.cuh"> <ClInclude Include="quark\cuda_quark_blake512_sp.cuh">
<Filter>Source Files\CUDA\quark</Filter> <Filter>Source Files\CUDA\quark</Filter>
</ClInclude> </ClInclude>
@ -820,6 +829,12 @@
<CudaCompile Include="lyra2\lyra2REv2.cu"> <CudaCompile Include="lyra2\lyra2REv2.cu">
<Filter>Source Files\CUDA\lyra2</Filter> <Filter>Source Files\CUDA\lyra2</Filter>
</CudaCompile> </CudaCompile>
<CudaCompile Include="lyra2\cuda_lyra2Z.cu">
<Filter>Source Files\CUDA\lyra2</Filter>
</CudaCompile>
<CudaCompile Include="lyra2\lyra2Z.cu">
<Filter>Source Files\CUDA\lyra2</Filter>
</CudaCompile>
<CudaCompile Include="Algo256\blake2s.cu"> <CudaCompile Include="Algo256\blake2s.cu">
<Filter>Source Files\CUDA\Algo256</Filter> <Filter>Source Files\CUDA\Algo256</Filter>
</CudaCompile> </CudaCompile>

215
lyra2/Lyra2Z.c

@ -0,0 +1,215 @@
/**
* Implementation of the Lyra2 Password Hashing Scheme (PHS).
*
* Author: The Lyra PHC team (http://www.lyra-kdf.net/) -- 2014.
*
* This software is hereby placed in the public domain.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHORS ''AS IS'' AND ANY EXPRESS
* OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
* BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
* EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include "Lyra2Z.h"
#include "Sponge.h"
/**
* Executes Lyra2 based on the G function from Blake2b. This version supports salts and passwords
* whose combined length is smaller than the size of the memory matrix, (i.e., (nRows x nCols x b) bits,
* where "b" is the underlying sponge's bitrate). In this implementation, the "basil" is composed by all
* integer parameters (treated as type "unsigned int") in the order they are provided, plus the value
* of nCols, (i.e., basil = kLen || pwdlen || saltlen || timeCost || nRows || nCols).
*
* @param K The derived key to be output by the algorithm
* @param kLen Desired key length
* @param pwd User password
* @param pwdlen Password length
* @param salt Salt
* @param saltlen Salt length
* @param timeCost Parameter to determine the processing time (T)
* @param nRows Number or rows of the memory matrix (R)
* @param nCols Number of columns of the memory matrix (C)
*
* @return 0 if the key is generated correctly; -1 if there is an error (usually due to lack of memory for allocation)
*/
int LYRA2Z(void *K, int64_t kLen, const void *pwd, int32_t pwdlen, const void *salt, int32_t saltlen, int64_t timeCost, const int16_t nRows, const int16_t nCols)
{
//============================= Basic variables ============================//
int64_t row = 2; //index of row to be processed
int64_t prev = 1; //index of prev (last row ever computed/modified)
int64_t rowa = 0; //index of row* (a previous row, deterministically picked during Setup and randomly picked while Wandering)
int64_t tau; //Time Loop iterator
int64_t step = 1; //Visitation step (used during Setup and Wandering phases)
int64_t window = 2; //Visitation window (used to define which rows can be revisited during Setup)
int64_t gap = 1; //Modifier to the step, assuming the values 1 or -1
int64_t i; //auxiliary iteration counter
int64_t v64; // 64bit var for memcpy
//==========================================================================/
//========== Initializing the Memory Matrix and pointers to it =============//
//Tries to allocate enough space for the whole memory matrix
const int64_t ROW_LEN_INT64 = BLOCK_LEN_INT64 * nCols;
const int64_t ROW_LEN_BYTES = ROW_LEN_INT64 * 8;
// for Lyra2REv2, nCols = 4, v1 was using 8
const int64_t BLOCK_LEN = BLOCK_LEN_BLAKE2_SAFE_INT64;
size_t sz = (size_t)ROW_LEN_BYTES * nRows;
uint64_t *wholeMatrix = malloc(sz);
if (wholeMatrix == NULL) {
return -1;
}
memset(wholeMatrix, 0, sz);
//Allocates pointers to each row of the matrix
uint64_t **memMatrix = malloc(sizeof(uint64_t*) * nRows);
if (memMatrix == NULL) {
return -1;
}
//Places the pointers in the correct positions
uint64_t *ptrWord = wholeMatrix;
for (i = 0; i < nRows; i++) {
memMatrix[i] = ptrWord;
ptrWord += ROW_LEN_INT64;
}
//==========================================================================/
//============= Getting the password + salt + basil padded with 10*1 ===============//
//OBS.:The memory matrix will temporarily hold the password: not for saving memory,
//but this ensures that the password copied locally will be overwritten as soon as possible
//First, we clean enough blocks for the password, salt, basil and padding
int64_t nBlocksInput = ((saltlen + pwdlen + 6 * sizeof(uint64_t)) / BLOCK_LEN_BLAKE2_SAFE_BYTES) + 1;
byte *ptrByte = (byte*) wholeMatrix;
//Prepends the password
memcpy(ptrByte, pwd, pwdlen);
ptrByte += pwdlen;
//Concatenates the salt
memcpy(ptrByte, salt, saltlen);
ptrByte += saltlen;
memset(ptrByte, 0, (size_t) (nBlocksInput * BLOCK_LEN_BLAKE2_SAFE_BYTES - (saltlen + pwdlen)));
//Concatenates the basil: every integer passed as parameter, in the order they are provided by the interface
memcpy(ptrByte, &kLen, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
v64 = pwdlen;
memcpy(ptrByte, &v64, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
v64 = saltlen;
memcpy(ptrByte, &v64, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
v64 = timeCost;
memcpy(ptrByte, &v64, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
v64 = nRows;
memcpy(ptrByte, &v64, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
v64 = nCols;
memcpy(ptrByte, &v64, sizeof(int64_t));
ptrByte += sizeof(uint64_t);
//Now comes the padding
*ptrByte = 0x80; //first byte of padding: right after the password
ptrByte = (byte*) wholeMatrix; //resets the pointer to the start of the memory matrix
ptrByte += nBlocksInput * BLOCK_LEN_BLAKE2_SAFE_BYTES - 1; //sets the pointer to the correct position: end of incomplete block
*ptrByte ^= 0x01; //last byte of padding: at the end of the last incomplete block
//==========================================================================/
//======================= Initializing the Sponge State ====================//
//Sponge state: 16 uint64_t, BLOCK_LEN_INT64 words of them for the bitrate (b) and the remainder for the capacity (c)
uint64_t state[16];
initState(state);
//==========================================================================/
//================================ Setup Phase =============================//
//Absorbing salt, password and basil: this is the only place in which the block length is hard-coded to 512 bits
ptrWord = wholeMatrix;
for (i = 0; i < nBlocksInput; i++) {
absorbBlockBlake2Safe(state, ptrWord); //absorbs each block of pad(pwd || salt || basil)
ptrWord += BLOCK_LEN; //goes to next block of pad(pwd || salt || basil)
}
//Initializes M[0] and M[1]
reducedSqueezeRow0(state, memMatrix[0], nCols); //The locally copied password is most likely overwritten here
reducedDuplexRow1(state, memMatrix[0], memMatrix[1], nCols);
do {
//M[row] = rand; //M[row*] = M[row*] XOR rotW(rand)
reducedDuplexRowSetup(state, memMatrix[prev], memMatrix[rowa], memMatrix[row], nCols);
//updates the value of row* (deterministically picked during Setup))
rowa = (rowa + step) & (window - 1);
//update prev: it now points to the last row ever computed
prev = row;
//updates row: goes to the next row to be computed
row++;
//Checks if all rows in the window where visited.
if (rowa == 0) {
step = window + gap; //changes the step: approximately doubles its value
window *= 2; //doubles the size of the re-visitation window
gap = -gap; //inverts the modifier to the step
}
} while (row < nRows);
//==========================================================================/
//============================ Wandering Phase =============================//
row = 0; //Resets the visitation to the first row of the memory matrix
for (tau = 1; tau <= timeCost; tau++) {
//Step is approximately half the number of all rows of the memory matrix for an odd tau; otherwise, it is -1
step = (tau % 2 == 0) ? -1 : nRows / 2 - 1;
do {
//Selects a pseudorandom index row*
//------------------------------------------------------------------------------------------
rowa = state[0] & (unsigned int)(nRows-1); //(USE THIS IF nRows IS A POWER OF 2)
//rowa = state[0] % nRows; //(USE THIS FOR THE "GENERIC" CASE)
//------------------------------------------------------------------------------------------
//Performs a reduced-round duplexing operation over M[row*] XOR M[prev], updating both M[row*] and M[row]
reducedDuplexRow(state, memMatrix[prev], memMatrix[rowa], memMatrix[row], nCols);
//update prev: it now points to the last row ever computed
prev = row;
//updates row: goes to the next row to be computed
//------------------------------------------------------------------------------------------
row = (row + step) & (unsigned int)(nRows-1); //(USE THIS IF nRows IS A POWER OF 2)
//row = (row + step) % nRows; //(USE THIS FOR THE "GENERIC" CASE)
//------------------------------------------------------------------------------------------
} while (row != 0);
}
//============================ Wrap-up Phase ===============================//
//Absorbs the last block of the memory matrix
absorbBlock(state, memMatrix[rowa]);
//Squeezes the key
squeeze(state, K, (unsigned int) kLen);
//========================= Freeing the memory =============================//
free(memMatrix);
free(wholeMatrix);
return 0;
}

42
lyra2/Lyra2Z.h

@ -0,0 +1,42 @@
/**
* Header file for the Lyra2 Password Hashing Scheme (PHS).
*
* Author: The Lyra PHC team (http://www.lyra-kdf.net/) -- 2014.
*
* This software is hereby placed in the public domain.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHORS ''AS IS'' AND ANY EXPRESS
* OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
* BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
* OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
* EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef LYRA2Z_H_
#define LYRA2Z_H_
#include <stdint.h>
typedef unsigned char byte;
//Block length required so Blake2's Initialization Vector (IV) is not overwritten (THIS SHOULD NOT BE MODIFIED)
#define BLOCK_LEN_BLAKE2_SAFE_INT64 8 //512 bits (=64 bytes, =8 uint64_t)
#define BLOCK_LEN_BLAKE2_SAFE_BYTES (BLOCK_LEN_BLAKE2_SAFE_INT64 * 8) //same as above, in bytes
#ifdef BLOCK_LEN_BITS
#define BLOCK_LEN_INT64 (BLOCK_LEN_BITS/64) //Block length: 768 bits (=96 bytes, =12 uint64_t)
#define BLOCK_LEN_BYTES (BLOCK_LEN_BITS/8) //Block length, in bytes
#else //default block lenght: 768 bits
#define BLOCK_LEN_INT64 12 //Block length: 768 bits (=96 bytes, =12 uint64_t)
#define BLOCK_LEN_BYTES (BLOCK_LEN_INT64 * 8) //Block length, in bytes
#endif
int LYRA2Z(void *K, int64_t kLen, const void *pwd, int32_t pwdlen, const void *salt, int32_t saltlen, int64_t timeCost, const int16_t nRows, const int16_t nCols);
#endif /* LYRA2_H_ */

966
lyra2/cuda_lyra2Z.cu

@ -0,0 +1,966 @@
/**
* Lyra2 (v1) cuda implementation based on djm34 work
* tpruvot@github 2015, Nanashi 08/2016 (from 1.8-r2)
* Lyra2Z implentation for Zcoin based on all the previous
* djm34 2017
**/
#include <stdio.h>
#include <memory.h>
#define TPB52 32
#define TPB30 160
#define TPB20 160
#include "cuda_lyra2Z_sm5.cuh"
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
__device__ uint32_t __shfl(uint32_t a, uint32_t b, uint32_t c);
#define atomicMin()
#define __CUDA_ARCH__ 520
#endif
static uint32_t *h_GNonces[16]; // this need to get fixed as the rest of that routine
static uint32_t *d_GNonces[16];
#define reduceDuplexRow(rowIn, rowInOut, rowOut) { \
for (int i = 0; i < 8; i++) { \
for (int j = 0; j < 12; j++) \
state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut]; \
round_lyra_sm2(state); \
for (int j = 0; j < 12; j++) \
Matrix[j + 12 * i][rowOut] ^= state[j]; \
Matrix[0 + 12 * i][rowInOut] ^= state[11]; \
Matrix[1 + 12 * i][rowInOut] ^= state[0]; \
Matrix[2 + 12 * i][rowInOut] ^= state[1]; \
Matrix[3 + 12 * i][rowInOut] ^= state[2]; \
Matrix[4 + 12 * i][rowInOut] ^= state[3]; \
Matrix[5 + 12 * i][rowInOut] ^= state[4]; \
Matrix[6 + 12 * i][rowInOut] ^= state[5]; \
Matrix[7 + 12 * i][rowInOut] ^= state[6]; \
Matrix[8 + 12 * i][rowInOut] ^= state[7]; \
Matrix[9 + 12 * i][rowInOut] ^= state[8]; \
Matrix[10+ 12 * i][rowInOut] ^= state[9]; \
Matrix[11+ 12 * i][rowInOut] ^= state[10]; \
} \
}
#define absorbblock(in) { \
state[0] ^= Matrix[0][in]; \
state[1] ^= Matrix[1][in]; \
state[2] ^= Matrix[2][in]; \
state[3] ^= Matrix[3][in]; \
state[4] ^= Matrix[4][in]; \
state[5] ^= Matrix[5][in]; \
state[6] ^= Matrix[6][in]; \
state[7] ^= Matrix[7][in]; \
state[8] ^= Matrix[8][in]; \
state[9] ^= Matrix[9][in]; \
state[10] ^= Matrix[10][in]; \
state[11] ^= Matrix[11][in]; \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
round_lyra_sm2(state); \
}
__device__ __forceinline__
static void round_lyra_sm2(uint2 *s)
{
Gfunc(s[0], s[4], s[8], s[12]);
Gfunc(s[1], s[5], s[9], s[13]);
Gfunc(s[2], s[6], s[10], s[14]);
Gfunc(s[3], s[7], s[11], s[15]);
Gfunc(s[0], s[5], s[10], s[15]);
Gfunc(s[1], s[6], s[11], s[12]);
Gfunc(s[2], s[7], s[8], s[13]);
Gfunc(s[3], s[4], s[9], s[14]);
}
__device__ __forceinline__
void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[16], uint2 Matrix[96][8])
{
#if __CUDA_ARCH__ > 500
#pragma unroll
#endif
for (int i = 0; i < 8; i++)
{
#pragma unroll
for (int j = 0; j < 12; j++)
state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut];
round_lyra_sm2(state);
#pragma unroll
for (int j = 0; j < 12; j++)
Matrix[j + 84 - 12 * i][rowOut] = Matrix[12 * i + j][rowIn] ^ state[j];
Matrix[0 + 12 * i][rowInOut] ^= state[11];
Matrix[1 + 12 * i][rowInOut] ^= state[0];
Matrix[2 + 12 * i][rowInOut] ^= state[1];
Matrix[3 + 12 * i][rowInOut] ^= state[2];
Matrix[4 + 12 * i][rowInOut] ^= state[3];
Matrix[5 + 12 * i][rowInOut] ^= state[4];
Matrix[6 + 12 * i][rowInOut] ^= state[5];
Matrix[7 + 12 * i][rowInOut] ^= state[6];
Matrix[8 + 12 * i][rowInOut] ^= state[7];
Matrix[9 + 12 * i][rowInOut] ^= state[8];
Matrix[10 + 12 * i][rowInOut] ^= state[9];
Matrix[11 + 12 * i][rowInOut] ^= state[10];
}
}
#if __CUDA_ARCH__ < 350
__constant__ static uint2 blake2b_IV_sm2[8] = {
{ 0xf3bcc908, 0x6a09e667 }, { 0x84caa73b, 0xbb67ae85 },
{ 0xfe94f82b, 0x3c6ef372 }, { 0x5f1d36f1, 0xa54ff53a },
{ 0xade682d1, 0x510e527f }, { 0x2b3e6c1f, 0x9b05688c },
{ 0xfb41bd6b, 0x1f83d9ab }, { 0x137e2179, 0x5be0cd19 }
};
__global__ __launch_bounds__(TPB30, 1)
void lyra2Z_gpu_hash_32_sm2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash, uint32_t *resNonces)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
const uint2 Mask[8] = {
{ 0x00000020, 0x00000000 },{ 0x00000020, 0x00000000 },
{ 0x00000020, 0x00000000 },{ 0x00000008, 0x00000000 },
{ 0x00000008, 0x00000000 },{ 0x00000008, 0x00000000 },
{ 0x00000080, 0x00000000 },{ 0x00000000, 0x01000000 }
};
if (thread < threads)
{
uint2 state[16];
#pragma unroll
for (int i = 0; i<4; i++) {
LOHI(state[i].x, state[i].y, g_hash[threads*i + thread]);
} //password
#pragma unroll
for (int i = 0; i<4; i++) {
state[i + 4] = state[i];
} //salt
#pragma unroll
for (int i = 0; i<8; i++) {
state[i + 8] = blake2b_IV_sm2[i];
}
// blake2blyra x2
//#pragma unroll 24
for (int i = 0; i<12; i++) {
round_lyra_sm2(state);
}
for (int i = 0; i<8; i++)
state[i] ^= Mask[i];
for (int i = 0; i<12; i++) {
round_lyra_sm2(state);
}
uint2 Matrix[96][8]; // not cool
// reducedSqueezeRow0
#pragma unroll 8
for (int i = 0; i < 8; i++)
{
#pragma unroll 12
for (int j = 0; j<12; j++) {
Matrix[j + 84 - 12 * i][0] = state[j];
}
round_lyra_sm2(state);
}
// reducedSqueezeRow1
#pragma unroll 8
for (int i = 0; i < 8; i++)
{
#pragma unroll 12
for (int j = 0; j<12; j++) {
state[j] ^= Matrix[j + 12 * i][0];
}
round_lyra_sm2(state);
#pragma unroll 12
for (int j = 0; j<12; j++) {
Matrix[j + 84 - 12 * i][1] = Matrix[j + 12 * i][0] ^ state[j];
}
}
reduceDuplexRowSetup(1, 0, 2, state, Matrix);
reduceDuplexRowSetup(2, 1, 3, state, Matrix);
reduceDuplexRowSetup(3, 0, 4, state, Matrix);
reduceDuplexRowSetup(4, 3, 5, state, Matrix);
reduceDuplexRowSetup(5, 2, 6, state, Matrix);
reduceDuplexRowSetup(6, 1, 7, state, Matrix);
uint32_t rowa;
uint32_t prev = 7;
uint32_t iterator = 0;
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = state[0].x & 7;
reduceDuplexRow(prev, rowa, iterator);
prev = iterator;
iterator = (iterator - 1) & 7;
}
absorbblock(rowa);
uint32_t nonce = startNounce + thread;
if (((uint64_t*)state)[3] <= ((uint64_t*)pTarget)[3]) {
atomicMin(&resNonces[1], resNonces[0]);
atomicMin(&resNonces[0], nonce);
}
} //thread
}
#else
__global__ void lyra2Z_gpu_hash_32_sm2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash, uint32_t *resNonces) {}
#endif
#if __CUDA_ARCH__ > 500
#include "cuda_lyra2_vectors.h"
//#include "cuda_vector_uint2x4.h"
#define Nrow 8
#define Ncol 8
#define memshift 3
#define BUF_COUNT 0
__device__ uint2 *DMatrix;
__device__ __forceinline__
void LD4S(uint2 res[3], const int row, const int col, const int thread, const int threads)
{
#if BUF_COUNT != 8
extern __shared__ uint2 shared_mem[];
const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift;
#endif
#if BUF_COUNT != 0
const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x;
#endif
#if BUF_COUNT == 8
#pragma unroll
for (int j = 0; j < 3; j++)
res[j] = *(DMatrix + d0 + j * threads * blockDim.x);
#elif BUF_COUNT == 0
#pragma unroll
for (int j = 0; j < 3; j++)
res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
#else
if (row < BUF_COUNT) {
#pragma unroll
for (int j = 0; j < 3; j++)
res[j] = *(DMatrix + d0 + j * threads * blockDim.x);
} else {
#pragma unroll
for (int j = 0; j < 3; j++)
res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
}
#endif
}
__device__ __forceinline__
void ST4S(const int row, const int col, const uint2 data[3], const int thread, const int threads)
{
#if BUF_COUNT != 8
extern __shared__ uint2 shared_mem[];
const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift;
#endif
#if BUF_COUNT != 0
const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x;
#endif
#if BUF_COUNT == 8
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + d0 + j * threads * blockDim.x) = data[j];
#elif BUF_COUNT == 0
#pragma unroll
for (int j = 0; j < 3; j++)
shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j];
#else
if (row < BUF_COUNT) {
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + d0 + j * threads * blockDim.x) = data[j];
} else {
#pragma unroll
for (int j = 0; j < 3; j++)
shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j];
}
#endif
}
#if __CUDA_ARCH__ >= 300
__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
{
return __shfl(a, b, c);
}
__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
{
return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c));
}
__device__ __forceinline__
void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
{
a1 = WarpShuffle(a1, b1, c);
a2 = WarpShuffle(a2, b2, c);
a3 = WarpShuffle(a3, b3, c);
}
#else
__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
uint32_t *_ptr = (uint32_t*)shared_mem;
__threadfence_block();
uint32_t buf = _ptr[thread];
_ptr[thread] = a;
__threadfence_block();
uint32_t result = _ptr[(thread&~(c - 1)) + (b&(c - 1))];
__threadfence_block();
_ptr[thread] = buf;
__threadfence_block();
return result;
}
__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
__threadfence_block();
uint2 buf = shared_mem[thread];
shared_mem[thread] = a;
__threadfence_block();
uint2 result = shared_mem[(thread&~(c - 1)) + (b&(c - 1))];
__threadfence_block();
shared_mem[thread] = buf;
__threadfence_block();
return result;
}
__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
__threadfence_block();
uint2 buf = shared_mem[thread];
shared_mem[thread] = a1;
__threadfence_block();
a1 = shared_mem[(thread&~(c - 1)) + (b1&(c - 1))];
__threadfence_block();
shared_mem[thread] = a2;
__threadfence_block();
a2 = shared_mem[(thread&~(c - 1)) + (b2&(c - 1))];
__threadfence_block();
shared_mem[thread] = a3;
__threadfence_block();
a3 = shared_mem[(thread&~(c - 1)) + (b3&(c - 1))];
__threadfence_block();
shared_mem[thread] = buf;
__threadfence_block();
}
#endif
__device__ __forceinline__ void round_lyra(uint2 s[4])
{
Gfunc(s[0], s[1], s[2], s[3]);
WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 1, threadIdx.x + 2, threadIdx.x + 3, 4);
Gfunc(s[0], s[1], s[2], s[3]);
WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 3, threadIdx.x + 2, threadIdx.x + 1, 4);
}
static __device__ __forceinline__
void round_lyra(uint2x4* s)
{
Gfunc(s[0].x, s[1].x, s[2].x, s[3].x);
Gfunc(s[0].y, s[1].y, s[2].y, s[3].y);
Gfunc(s[0].z, s[1].z, s[2].z, s[3].z);
Gfunc(s[0].w, s[1].w, s[2].w, s[3].w);
Gfunc(s[0].x, s[1].y, s[2].z, s[3].w);
Gfunc(s[0].y, s[1].z, s[2].w, s[3].x);
Gfunc(s[0].z, s[1].w, s[2].x, s[3].y);
Gfunc(s[0].w, s[1].x, s[2].y, s[3].z);
}
static __device__ __forceinline__
void reduceDuplex(uint2 state[4], uint32_t thread, const uint32_t threads)
{
uint2 state1[3];
#if __CUDA_ARCH__ > 500
#pragma unroll
#endif
for (int i = 0; i < Nrow; i++)
{
ST4S(0, Ncol - i - 1, state, thread, threads);
round_lyra(state);
}
#pragma unroll 4
for (int i = 0; i < Nrow; i++)
{
LD4S(state1, 0, i, thread, threads);
for (int j = 0; j < 3; j++)
state[j] ^= state1[j];
round_lyra(state);
for (int j = 0; j < 3; j++)
state1[j] ^= state[j];
ST4S(1, Ncol - i - 1, state1, thread, threads);
}
}
static __device__ __forceinline__
void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], uint32_t thread, const uint32_t threads)
{
uint2 state1[3], state2[3];
#pragma unroll 1
for (int i = 0; i < Nrow; i++)
{
LD4S(state1, rowIn, i, thread, threads);
LD4S(state2, rowInOut, i, thread, threads);
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] ^= state[j];
ST4S(rowOut, Ncol - i - 1, state1, thread, threads);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
ST4S(rowInOut, i, state2, thread, threads);
}
}
static __device__ __forceinline__
void reduceDuplexRowt(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
{
for (int i = 0; i < Nrow; i++)
{
uint2 state1[3], state2[3];
LD4S(state1, rowIn, i, thread, threads);
LD4S(state2, rowInOut, i, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
}
else
{
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
ST4S(rowInOut, i, state2, thread, threads);
LD4S(state1, rowOut, i, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] ^= state[j];
ST4S(rowOut, i, state1, thread, threads);
}
}
#if 0
static __device__ __forceinline__
void reduceDuplexRowt_8(const int rowInOut, uint2* state, const uint32_t thread, const uint32_t threads)
{
uint2 state1[3], state2[3], last[3];
LD4S(state1, 2, 0, thread, threads);
LD4S(last, rowInOut, 0, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + last[j];
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
last[0] ^= Data2;
last[1] ^= Data0;
last[2] ^= Data1;
} else {
last[0] ^= Data0;
last[1] ^= Data1;
last[2] ^= Data2;
}
if (rowInOut == 5)
{
#pragma unroll
for (int j = 0; j < 3; j++)
last[j] ^= state[j];
}
for (int i = 1; i < Nrow; i++)
{
LD4S(state1, 2, i, thread, threads);
LD4S(state2, rowInOut, i, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
}
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= last[j];
}
#endif
static __device__ __forceinline__
void reduceDuplexRowt_8_v2(const int rowIn, const int rowOut, const int rowInOut, uint2* state, const uint32_t thread, const uint32_t threads)
{
uint2 state1[3], state2[3], last[3];
LD4S(state1, rowIn, 0, thread, threads);
LD4S(last, rowInOut, 0, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + last[j];
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
last[0] ^= Data2;
last[1] ^= Data0;
last[2] ^= Data1;
}
else {
last[0] ^= Data0;
last[1] ^= Data1;
last[2] ^= Data2;
}
if (rowInOut == rowOut) {
#pragma unroll
for (int j = 0; j < 3; j++)
last[j] ^= state[j];
}
for (int i = 1; i < Nrow; i++)
{
LD4S(state1, rowIn, i, thread, threads);
LD4S(state2, rowInOut, i, thread, threads);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
}
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= last[j];
}
__global__
__launch_bounds__(64, 1)
void lyra2Z_gpu_hash_32_1(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
const uint2x4 Mask[2] = {
0x00000020UL, 0x00000000UL, 0x00000020UL, 0x00000000UL,
0x00000020UL, 0x00000000UL, 0x00000008UL, 0x00000000UL,
0x00000008UL, 0x00000000UL, 0x00000008UL, 0x00000000UL,
0x00000080UL, 0x00000000UL, 0x00000000UL, 0x01000000UL
};
const uint2x4 blake2b_IV[2] = {
0xf3bcc908lu, 0x6a09e667lu,
0x84caa73blu, 0xbb67ae85lu,
0xfe94f82blu, 0x3c6ef372lu,
0x5f1d36f1lu, 0xa54ff53alu,
0xade682d1lu, 0x510e527flu,
0x2b3e6c1flu, 0x9b05688clu,
0xfb41bd6blu, 0x1f83d9ablu,
0x137e2179lu, 0x5be0cd19lu
};
if (thread < threads)
{
uint2x4 state[4];
state[0].x = state[1].x = __ldg(&g_hash[thread + threads * 0]);
state[0].y = state[1].y = __ldg(&g_hash[thread + threads * 1]);
state[0].z = state[1].z = __ldg(&g_hash[thread + threads * 2]);
state[0].w = state[1].w = __ldg(&g_hash[thread + threads * 3]);
state[2] = blake2b_IV[0];
state[3] = blake2b_IV[1];
for (int i = 0; i<12; i++)
round_lyra(state);
state[0] ^= Mask[0];
state[1] ^= Mask[1];
for (int i = 0; i<12; i++)
round_lyra(state); //because 12 is not enough
((uint2x4*)DMatrix)[threads * 0 + thread] = state[0];
((uint2x4*)DMatrix)[threads * 1 + thread] = state[1];
((uint2x4*)DMatrix)[threads * 2 + thread] = state[2];
((uint2x4*)DMatrix)[threads * 3 + thread] = state[3];
}
}
__global__
__launch_bounds__(TPB52, 1)
void lyra2Z_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash)
{
const uint32_t thread = blockDim.y * blockIdx.x + threadIdx.y;
if (thread < threads)
{
uint2 state[4];
state[0] = __ldg(&DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x]);
state[1] = __ldg(&DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x]);
state[2] = __ldg(&DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x]);
state[3] = __ldg(&DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x]);
reduceDuplex(state, thread, threads);
reduceDuplexRowSetup(1, 0, 2, state, thread, threads);
reduceDuplexRowSetup(2, 1, 3, state, thread, threads);
reduceDuplexRowSetup(3, 0, 4, state, thread, threads);
reduceDuplexRowSetup(4, 3, 5, state, thread, threads);
reduceDuplexRowSetup(5, 2, 6, state, thread, threads);
reduceDuplexRowSetup(6, 1, 7, state, thread, threads);
uint32_t rowa; // = WarpShuffle(state[0].x, 0, 4) & 7;
uint32_t prev = 7;
uint32_t iterator = 0;
//for (uint32_t j=0;j<4;j++) {
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<7; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
//}
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowt_8_v2(prev,iterator,rowa, state, thread, threads);
DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x] = state[0];
DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x] = state[1];
DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x] = state[2];
DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x] = state[3];
}
}
__global__
__launch_bounds__(64, 1)
void lyra2Z_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces)
{
const uint32_t thread = blockDim.x * blockIdx.x + threadIdx.x;
uint28 state[4];
if (thread < threads)
{
state[0] = __ldg4(&((uint2x4*)DMatrix)[threads * 0 + thread]);
state[1] = __ldg4(&((uint2x4*)DMatrix)[threads * 1 + thread]);
state[2] = __ldg4(&((uint2x4*)DMatrix)[threads * 2 + thread]);
state[3] = __ldg4(&((uint2x4*)DMatrix)[threads * 3 + thread]);
for (int i = 0; i < 12; i++)
round_lyra(state);
uint32_t nonce = startNounce + thread;
if (((uint64_t*)state)[3] <= ((uint64_t*)pTarget)[3]) {
atomicMin(&resNonces[1], resNonces[0]);
atomicMin(&resNonces[0], nonce);
}
/*
g_hash[thread + threads * 0] = state[0].x;
g_hash[thread + threads * 1] = state[0].y;
g_hash[thread + threads * 2] = state[0].z;
g_hash[thread + threads * 3] = state[0].w;
*/
}
}
#else
#if __CUDA_ARCH__ < 350
__device__ void* DMatrix;
#endif
__global__ void lyra2Z_gpu_hash_32_1(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
__global__ void lyra2Z_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash) {}
__global__ void lyra2Z_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces) {}
#endif
__host__
void lyra2Z_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix)
{
// just assign the device pointer allocated in main loop
cudaMemcpyToSymbol(DMatrix, &d_matrix, sizeof(uint64_t*), 0, cudaMemcpyHostToDevice);
cudaMalloc(&d_GNonces[thr_id], 2 * sizeof(uint32_t));
cudaMallocHost(&h_GNonces[thr_id], 2 * sizeof(uint32_t));
}
__host__
void lyra2Z_cpu_init_sm2(int thr_id, uint32_t threads)
{
// just assign the device pointer allocated in main loop
cudaMalloc(&d_GNonces[thr_id], 2 * sizeof(uint32_t));
cudaMallocHost(&h_GNonces[thr_id], 2 * sizeof(uint32_t));
}
__host__
uint32_t lyra2Z_getSecNonce(int thr_id, int num)
{
uint32_t results[2];
memset(results, 0xFF, sizeof(results));
cudaMemcpy(results, d_GNonces[thr_id], sizeof(results), cudaMemcpyDeviceToHost);
if (results[1] == results[0])
return UINT32_MAX;
return results[num];
}
__host__
void lyra2Z_setTarget(const void *pTargetIn)
{
cudaMemcpyToSymbol(pTarget, pTargetIn, 32, 0, cudaMemcpyHostToDevice);
}
__host__
uint32_t lyra2Z_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_hash, bool gtx750ti)
{
uint32_t result = UINT32_MAX;
cudaMemset(d_GNonces[thr_id], 0xff, 2 * sizeof(uint32_t));
int dev_id = device_map[thr_id % MAX_GPUS];
uint32_t tpb = TPB52;
if (device_sm[dev_id] == 500)
tpb = TPB50;
if (device_sm[dev_id] == 200)
tpb = TPB20;
dim3 grid1((threads * 4 + tpb - 1) / tpb);
dim3 block1(4, tpb >> 2);
dim3 grid2((threads + 64 - 1) / 64);
dim3 block2(64);
dim3 grid3((threads + tpb - 1) / tpb);
dim3 block3(tpb);
if (device_sm[dev_id] >= 520)
{
lyra2Z_gpu_hash_32_1 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
lyra2Z_gpu_hash_32_2 <<< grid1, block1, 24 * (8 - 0) * sizeof(uint2) * tpb >>> (threads, startNounce, d_hash);
lyra2Z_gpu_hash_32_3 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash, d_GNonces[thr_id]);
}
else if (device_sm[dev_id] == 500 || device_sm[dev_id] == 350)
{
size_t shared_mem = 0;
if (gtx750ti)
// 8Warpに調整のため、8192バイト確保する
shared_mem = 8192;
else
// 10Warpに調整のため、6144バイト確保する
shared_mem = 6144;
lyra2Z_gpu_hash_32_1_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
lyra2Z_gpu_hash_32_2_sm5 <<< grid1, block1, shared_mem >>> (threads, startNounce, (uint2*)d_hash);
lyra2Z_gpu_hash_32_3_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash, d_GNonces[thr_id]);
}
else
lyra2Z_gpu_hash_32_sm2 <<< grid3, block3 >>> (threads, startNounce, d_hash, d_GNonces[thr_id]);
// get first found nonce
cudaMemcpy(h_GNonces[thr_id], d_GNonces[thr_id], 1 * sizeof(uint32_t), cudaMemcpyDeviceToHost);
result = *h_GNonces[thr_id];
return result;
}

819
lyra2/cuda_lyra2Z_sm5.cuh

@ -0,0 +1,819 @@
#include <memory.h>
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
//#define __CUDA_ARCH__ 500
#define __threadfence_block()
#define __ldg(x) *(x)
#define atomicMin(p,y) y
#endif
#include "cuda_helper.h"
#define TPB50 32
__constant__ uint32_t pTarget[8];
static __device__ __forceinline__
void Gfunc(uint2 & a, uint2 &b, uint2 &c, uint2 &d)
{
#if __CUDA_ARCH__ > 500
a += b; uint2 tmp = d; d.y = a.x ^ tmp.x; d.x = a.y ^ tmp.y;
c += d; b ^= c; b = ROR24(b);
a += b; d ^= a; d = ROR16(d);
c += d; b ^= c; b = ROR2(b, 63);
#else
a += b; d ^= a; d = SWAPUINT2(d);
c += d; b ^= c; b = ROR2(b, 24);
a += b; d ^= a; d = ROR2(d, 16);
c += d; b ^= c; b = ROR2(b, 63);
#endif
}
#if __CUDA_ARCH__ == 500 || __CUDA_ARCH__ == 350
#include "cuda_lyra2_vectors.h"
#define Nrow 8
#define Ncol 8
#define memshift 3
__device__ uint2 *DMatrix;
__device__ __forceinline__ uint2 LD4S(const int index)
{
extern __shared__ uint2 shared_mem[];
return shared_mem[(index * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
}
__device__ __forceinline__ void ST4S(const int index, const uint2 data)
{
extern __shared__ uint2 shared_mem[];
shared_mem[(index * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data;
}
#if __CUDA_ARCH__ == 300
__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
{
return __shfl(a, b, c);
}
__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
{
return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c));
}
__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
{
a1 = WarpShuffle(a1, b1, c);
a2 = WarpShuffle(a2, b2, c);
a3 = WarpShuffle(a3, b3, c);
}
#else // != 300
__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
uint32_t *_ptr = (uint32_t*)shared_mem;
__threadfence_block();
uint32_t buf = _ptr[thread];
_ptr[thread] = a;
__threadfence_block();
uint32_t result = _ptr[(thread&~(c - 1)) + (b&(c - 1))];
__threadfence_block();
_ptr[thread] = buf;
__threadfence_block();
return result;
}
__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
__threadfence_block();
uint2 buf = shared_mem[thread];
shared_mem[thread] = a;
__threadfence_block();
uint2 result = shared_mem[(thread&~(c - 1)) + (b&(c - 1))];
__threadfence_block();
shared_mem[thread] = buf;
__threadfence_block();
return result;
}
__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
{
extern __shared__ uint2 shared_mem[];
const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
__threadfence_block();
uint2 buf = shared_mem[thread];
shared_mem[thread] = a1;
__threadfence_block();
a1 = shared_mem[(thread&~(c - 1)) + (b1&(c - 1))];
__threadfence_block();
shared_mem[thread] = a2;
__threadfence_block();
a2 = shared_mem[(thread&~(c - 1)) + (b2&(c - 1))];
__threadfence_block();
shared_mem[thread] = a3;
__threadfence_block();
a3 = shared_mem[(thread&~(c - 1)) + (b3&(c - 1))];
__threadfence_block();
shared_mem[thread] = buf;
__threadfence_block();
}
#endif // != 300
__device__ __forceinline__ void round_lyra(uint2 s[4])
{
Gfunc(s[0], s[1], s[2], s[3]);
WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 1, threadIdx.x + 2, threadIdx.x + 3, 4);
Gfunc(s[0], s[1], s[2], s[3]);
WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 3, threadIdx.x + 2, threadIdx.x + 1, 4);
}
static __device__ __forceinline__
void round_lyra(uint2x4* s)
{
Gfunc(s[0].x, s[1].x, s[2].x, s[3].x);
Gfunc(s[0].y, s[1].y, s[2].y, s[3].y);
Gfunc(s[0].z, s[1].z, s[2].z, s[3].z);
Gfunc(s[0].w, s[1].w, s[2].w, s[3].w);
Gfunc(s[0].x, s[1].y, s[2].z, s[3].w);
Gfunc(s[0].y, s[1].z, s[2].w, s[3].x);
Gfunc(s[0].z, s[1].w, s[2].x, s[3].y);
Gfunc(s[0].w, s[1].x, s[2].y, s[3].z);
}
static __device__ __forceinline__
void reduceDuplexV5(uint2 state[4], const uint32_t thread, const uint32_t threads)
{
uint2 state1[3], state2[3];
const uint32_t ps0 = (memshift * Ncol * 0 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps1 = (memshift * Ncol * 1 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps2 = (memshift * Ncol * 2 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps3 = (memshift * Ncol * 3 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps4 = (memshift * Ncol * 4 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps5 = (memshift * Ncol * 5 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps6 = (memshift * Ncol * 6 * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps7 = (memshift * Ncol * 7 * threads + thread)*blockDim.x + threadIdx.x;
for (int i = 0; i < 8; i++)
{
const uint32_t s0 = memshift * Ncol * 0 + (Ncol - 1 - i) * memshift;
#pragma unroll
for (int j = 0; j < 3; j++)
ST4S(s0 + j, state[j]);
round_lyra(state);
}
for (int i = 0; i < 8; i++)
{
const uint32_t s0 = memshift * Ncol * 0 + i * memshift;
const uint32_t s1 = ps1 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = LD4S(s0 + j);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s1 + j*threads*blockDim.x) = state1[j] ^ state[j];
}
// 1, 0, 2
for (int i = 0; i < 8; i++)
{
const uint32_t s0 = memshift * Ncol * 0 + i * memshift;
const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
const uint32_t s2 = ps2 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s1 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = LD4S(s0 + j);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s2 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
}
else
{
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
ST4S(s0 + j, state2[j]);
}
// 2, 1, 3
for (int i = 0; i < 8; i++)
{
const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
const uint32_t s2 = ps2 + i * memshift* threads*blockDim.x;
const uint32_t s3 = ps3 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s2 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = *(DMatrix + s1 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s3 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s1 + j*threads*blockDim.x) = state2[j];
}
// 3, 0, 4
for (int i = 0; i < 8; i++)
{
const uint32_t ls0 = memshift * Ncol * 0 + i * memshift;
const uint32_t s0 = ps0 + i * memshift* threads*blockDim.x;
const uint32_t s3 = ps3 + i * memshift* threads*blockDim.x;
const uint32_t s4 = ps4 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s3 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = LD4S(ls0 + j);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s4 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s0 + j*threads*blockDim.x) = state2[j];
}
// 4, 3, 5
for (int i = 0; i < 8; i++)
{
const uint32_t s3 = ps3 + i * memshift* threads*blockDim.x;
const uint32_t s4 = ps4 + i * memshift* threads*blockDim.x;
const uint32_t s5 = ps5 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s4 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = *(DMatrix + s3 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s5 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
}
else
{
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s3 + j*threads*blockDim.x) = state2[j];
}
// 5, 2, 6
for (int i = 0; i < 8; i++)
{
const uint32_t s2 = ps2 + i * memshift* threads*blockDim.x;
const uint32_t s5 = ps5 + i * memshift* threads*blockDim.x;
const uint32_t s6 = ps6 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s5 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = *(DMatrix + s2 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s6 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
}
else
{
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s2 + j*threads*blockDim.x) = state2[j];
}
// 6, 1, 7
for (int i = 0; i < 8; i++)
{
const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
const uint32_t s6 = ps6 + i * memshift* threads*blockDim.x;
const uint32_t s7 = ps7 + (7 - i)*memshift* threads*blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state1[j] = *(DMatrix + s6 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = *(DMatrix + s1 + j*threads*blockDim.x);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= state1[j] + state2[j];
round_lyra(state);
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s7 + j*threads*blockDim.x) = state1[j] ^ state[j];
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
*(DMatrix + s1 + j*threads*blockDim.x) = state2[j];
}
}
static __device__ __forceinline__
void reduceDuplexRowV50(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
{
const uint32_t ps1 = (memshift * Ncol * rowIn*threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps2 = (memshift * Ncol * rowInOut *threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps3 = (memshift * Ncol * rowOut*threads + thread)*blockDim.x + threadIdx.x;
#pragma unroll 1
for (int i = 0; i < 8; i++)
{
uint2 state1[3], state2[3];
const uint32_t s1 = ps1 + i*memshift*threads *blockDim.x;
const uint32_t s2 = ps2 + i*memshift*threads *blockDim.x;
const uint32_t s3 = ps3 + i*memshift*threads *blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] = *(DMatrix + s1 + j*threads*blockDim.x);
state2[j] = *(DMatrix + s2 + j*threads*blockDim.x);
}
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] += state2[j];
state[j] ^= state1[j];
}
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll
for (int j = 0; j < 3; j++)
{
*(DMatrix + s2 + j*threads*blockDim.x) = state2[j];
*(DMatrix + s3 + j*threads*blockDim.x) ^= state[j];
}
}
}
static __device__ __forceinline__
void reduceDuplexRowV50_8(const int rowInOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
{
const uint32_t ps1 = (memshift * Ncol * 2*threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps2 = (memshift * Ncol * rowInOut *threads + thread)*blockDim.x + threadIdx.x;
// const uint32_t ps3 = (memshift * Ncol * 5*threads + thread)*blockDim.x + threadIdx.x;
uint2 state1[3], last[3];
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] = *(DMatrix + ps1 + j*threads*blockDim.x);
last[j] = *(DMatrix + ps2 + j*threads*blockDim.x);
}
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] += last[j];
state[j] ^= state1[j];
}
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
last[0] ^= Data2;
last[1] ^= Data0;
last[2] ^= Data1;
} else {
last[0] ^= Data0;
last[1] ^= Data1;
last[2] ^= Data2;
}
if (rowInOut == 5)
{
#pragma unroll
for (int j = 0; j < 3; j++)
last[j] ^= state[j];
}
for (int i = 1; i < 8; i++)
{
const uint32_t s1 = ps1 + i*memshift*threads *blockDim.x;
const uint32_t s2 = ps2 + i*memshift*threads *blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= *(DMatrix + s1 + j*threads*blockDim.x) + *(DMatrix + s2 + j*threads*blockDim.x);
round_lyra(state);
}
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= last[j];
}
static __device__ __forceinline__
void reduceDuplexRowV50_8_v2(const int rowIn, const int rowOut,const int rowInOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
{
const uint32_t ps1 = (memshift * Ncol * rowIn * threads + thread)*blockDim.x + threadIdx.x;
const uint32_t ps2 = (memshift * Ncol * rowInOut *threads + thread)*blockDim.x + threadIdx.x;
// const uint32_t ps3 = (memshift * Ncol * 5*threads + thread)*blockDim.x + threadIdx.x;
uint2 state1[3], last[3];
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] = *(DMatrix + ps1 + j*threads*blockDim.x);
last[j] = *(DMatrix + ps2 + j*threads*blockDim.x);
}
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] += last[j];
state[j] ^= state1[j];
}
round_lyra(state);
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
uint2 Data0 = state[0];
uint2 Data1 = state[1];
uint2 Data2 = state[2];
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
if (threadIdx.x == 0)
{
last[0] ^= Data2;
last[1] ^= Data0;
last[2] ^= Data1;
}
else {
last[0] ^= Data0;
last[1] ^= Data1;
last[2] ^= Data2;
}
if (rowInOut == rowOut)
{
#pragma unroll
for (int j = 0; j < 3; j++)
last[j] ^= state[j];
}
for (int i = 1; i < 8; i++)
{
const uint32_t s1 = ps1 + i*memshift*threads *blockDim.x;
const uint32_t s2 = ps2 + i*memshift*threads *blockDim.x;
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= *(DMatrix + s1 + j*threads*blockDim.x) + *(DMatrix + s2 + j*threads*blockDim.x);
round_lyra(state);
}
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= last[j];
}
__global__ __launch_bounds__(64, 1)
void lyra2Z_gpu_hash_32_1_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
const uint2x4 blake2b_IV[2] = {
{ { 0xf3bcc908, 0x6a09e667 }, { 0x84caa73b, 0xbb67ae85 }, { 0xfe94f82b, 0x3c6ef372 }, { 0x5f1d36f1, 0xa54ff53a } },
{ { 0xade682d1, 0x510e527f }, { 0x2b3e6c1f, 0x9b05688c }, { 0xfb41bd6b, 0x1f83d9ab }, { 0x137e2179, 0x5be0cd19 } }
};
const uint2x4 Mask[2] = {
0x00000020UL, 0x00000000UL, 0x00000020UL, 0x00000000UL,
0x00000020UL, 0x00000000UL, 0x00000008UL, 0x00000000UL,
0x00000008UL, 0x00000000UL, 0x00000008UL, 0x00000000UL,
0x00000080UL, 0x00000000UL, 0x00000000UL, 0x01000000UL
};
if (thread < threads)
{
uint2x4 state[4];
((uint2*)state)[0] = __ldg(&g_hash[thread]);
((uint2*)state)[1] = __ldg(&g_hash[thread + threads]);
((uint2*)state)[2] = __ldg(&g_hash[thread + threads * 2]);
((uint2*)state)[3] = __ldg(&g_hash[thread + threads * 3]);
state[1] = state[0];
state[2] = blake2b_IV[0];
state[3] = blake2b_IV[1];
for (int i = 0; i < 12; i++)
round_lyra(state); //because 12 is not enough
state[0] ^= Mask[0];
state[1] ^= Mask[1];
for (int i = 0; i < 12; i++)
round_lyra(state); //because 12 is not enough
((uint2x4*)DMatrix)[0 * threads + thread] = state[0];
((uint2x4*)DMatrix)[1 * threads + thread] = state[1];
((uint2x4*)DMatrix)[2 * threads + thread] = state[2];
((uint2x4*)DMatrix)[3 * threads + thread] = state[3];
}
}
__global__ __launch_bounds__(TPB50, 1)
void lyra2Z_gpu_hash_32_2_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
{
const uint32_t thread = (blockDim.y * blockIdx.x + threadIdx.y);
if (thread < threads)
{
uint2 state[4];
state[0] = __ldg(&DMatrix[(0 * threads + thread)*blockDim.x + threadIdx.x]);
state[1] = __ldg(&DMatrix[(1 * threads + thread)*blockDim.x + threadIdx.x]);
state[2] = __ldg(&DMatrix[(2 * threads + thread)*blockDim.x + threadIdx.x]);
state[3] = __ldg(&DMatrix[(3 * threads + thread)*blockDim.x + threadIdx.x]);
reduceDuplexV5(state, thread, threads);
uint32_t rowa; // = WarpShuffle(state[0].x, 0, 4) & 7;
uint32_t prev = 7;
uint32_t iterator = 0;
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
for (uint32_t i = 0; i<8; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator + 3) & 7;
}
for (uint32_t i = 0; i<7; i++) {
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50(prev, rowa, iterator, state, thread, threads);
prev = iterator;
iterator = (iterator - 1) & 7;
}
rowa = WarpShuffle(state[0].x, 0, 4) & 7;
reduceDuplexRowV50_8_v2(prev,iterator,rowa, state, thread, threads);
DMatrix[(0 * threads + thread)*blockDim.x + threadIdx.x] = state[0];
DMatrix[(1 * threads + thread)*blockDim.x + threadIdx.x] = state[1];
DMatrix[(2 * threads + thread)*blockDim.x + threadIdx.x] = state[2];
DMatrix[(3 * threads + thread)*blockDim.x + threadIdx.x] = state[3];
}
}
__global__ __launch_bounds__(64, 1)
void lyra2Z_gpu_hash_32_3_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
uint2x4 state[4];
state[0] = __ldg4(&((uint2x4*)DMatrix)[0 * threads + thread]);
state[1] = __ldg4(&((uint2x4*)DMatrix)[1 * threads + thread]);
state[2] = __ldg4(&((uint2x4*)DMatrix)[2 * threads + thread]);
state[3] = __ldg4(&((uint2x4*)DMatrix)[3 * threads + thread]);
for (int i = 0; i < 12; i++)
round_lyra(state);
uint32_t nonce = startNounce + thread;
if (((uint64_t*)state)[3] <= ((uint64_t*)pTarget)[3]) {
atomicMin(&resNonces[1], resNonces[0]);
atomicMin(&resNonces[0], nonce);
}
}
}
#else
/* if __CUDA_ARCH__ != 500 .. host */
__global__ void lyra2Z_gpu_hash_32_1_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
__global__ void lyra2Z_gpu_hash_32_2_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
__global__ void lyra2Z_gpu_hash_32_3_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces) {}
#endif

8
lyra2/cuda_lyra2_vectors.h

@ -36,11 +36,11 @@ typedef struct __align__(128) ulonglong8to16 {
ulonglong2to8 lo, hi; ulonglong2to8 lo, hi;
} ulonglong8to16; } ulonglong8to16;
typedef struct __align__(256) ulonglong16to32 { typedef struct __align__(128) ulonglong16to32{
ulonglong8to16 lo, hi; ulonglong8to16 lo, hi;
} ulonglong16to32; } ulonglong16to32;
typedef struct __align__(512) ulonglong32to64 { typedef struct __align__(128) ulonglong32to64{
ulonglong16to32 lo, hi; ulonglong16to32 lo, hi;
} ulonglong32to64; } ulonglong32to64;
@ -79,7 +79,7 @@ struct __align__(128) ulong8 {
}; };
typedef __device_builtin__ struct ulong8 ulong8; typedef __device_builtin__ struct ulong8 ulong8;
typedef struct __align__(256) ulonglong16 { typedef struct __align__(128) ulonglong16{
ulonglong4 s0, s1, s2, s3, s4, s5, s6, s7; ulonglong4 s0, s1, s2, s3, s4, s5, s6, s7;
} ulonglong16; } ulonglong16;
@ -92,7 +92,7 @@ typedef struct __builtin_align__(32) uint48 {
uint4 s0,s1; uint4 s0,s1;
} uint48; } uint48;
typedef struct __align__(256) uint4x16 { typedef struct __builtin_align__(128) uint4x16{
uint4 s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15; uint4 s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15;
} uint4x16; } uint4x16;

164
lyra2/lyra2Z.cu

@ -0,0 +1,164 @@
extern "C" {
#include <sph/sph_blake.h>
#include "Lyra2Z.h"
}
#include <miner.h>
#include <cuda_helper.h>
static uint64_t* d_hash[MAX_GPUS];
static uint64_t* d_matrix[MAX_GPUS];
extern void blake256_cpu_init(int thr_id, uint32_t threads);
extern void blake256_cpu_hash_80(const int thr_id, const uint32_t threads, const uint32_t startNonce, uint64_t *Hash, int order);
extern void blake256_cpu_setBlock_80(uint32_t *pdata);
extern void lyra2Z_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix);
extern void lyra2Z_cpu_init_sm2(int thr_id, uint32_t threads);
extern uint32_t lyra2Z_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNonce, uint64_t *d_outputHash, bool gtx750ti);
extern void lyra2Z_setTarget(const void *ptarget);
extern uint32_t lyra2Z_getSecNonce(int thr_id, int num);
extern "C" void lyra2Z_hash(void *state, const void *input)
{
uint32_t _ALIGN(64) hashA[8], hashB[8];
sph_blake256_context ctx_blake;
sph_blake256_set_rounds(14);
sph_blake256_init(&ctx_blake);
sph_blake256(&ctx_blake, input, 80);
sph_blake256_close(&ctx_blake, hashA);
LYRA2Z(hashB, 32, hashA, 32, hashA, 32, 8, 8, 8);
memcpy(state, hashB, 32);
}
static bool init[MAX_GPUS] = { 0 };
static __thread uint32_t throughput = 0;
static __thread bool gtx750ti = false;
extern "C" int scanhash_lyra2Z(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done)
{
uint32_t *pdata = work->data;
uint32_t *ptarget = work->target;
uint32_t _ALIGN(64) endiandata[20];
const uint32_t first_nonce = pdata[19];
int dev_id = device_map[thr_id];
if (opt_benchmark)
ptarget[7] = 0x00ff;
if (!init[thr_id])
{
cudaSetDevice(dev_id);
if (opt_cudaschedule == -1 && gpu_threads == 1) {
cudaDeviceReset();
cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync);
CUDA_LOG_ERROR();
}
int intensity = (device_sm[dev_id] > 500 && !is_windows()) ? 17 : 16;
if (device_sm[dev_id] <= 500) intensity = 15;
throughput = cuda_default_throughput(thr_id, 1U << intensity); // 18=256*256*4;
if (init[thr_id]) throughput = min(throughput, max_nonce - first_nonce);
cudaDeviceProp props;
cudaGetDeviceProperties(&props, dev_id);
gtx750ti = (strstr(props.name, "750 Ti") != NULL);
gpulog(LOG_INFO, thr_id, "Intensity set to %g, %u cuda threads", throughput2intensity(throughput), throughput);
blake256_cpu_init(thr_id, throughput);
if (device_sm[dev_id] >= 350)
{
size_t matrix_sz = device_sm[dev_id] > 500 ? sizeof(uint64_t) * 4 * 4 : sizeof(uint64_t) * 8 * 8 * 3 * 4;
CUDA_SAFE_CALL(cudaMalloc(&d_matrix[thr_id], matrix_sz * throughput));
lyra2Z_cpu_init(thr_id, throughput, d_matrix[thr_id]);
}
else
lyra2Z_cpu_init_sm2(thr_id, throughput);
CUDA_SAFE_CALL(cudaMalloc(&d_hash[thr_id], (size_t)32 * throughput));
init[thr_id] = true;
}
for (int k=0; k < 20; k++)
be32enc(&endiandata[k], pdata[k]);
blake256_cpu_setBlock_80(pdata);
lyra2Z_setTarget(ptarget);
do {
int order = 0;
blake256_cpu_hash_80(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
*hashes_done = pdata[19] - first_nonce + throughput;
work->nonces[0] = lyra2Z_cpu_hash_32(thr_id, throughput, pdata[19], d_hash[thr_id], gtx750ti);
if (work->nonces[0] != UINT32_MAX)
{
uint32_t _ALIGN(64) vhash[8];
be32enc(&endiandata[19], work->nonces[0]);
lyra2Z_hash(vhash, endiandata);
if (vhash[7] <= ptarget[7] && fulltest(vhash, ptarget)) {
work->valid_nonces = 1;
work->nonces[1] = lyra2Z_getSecNonce(thr_id, 1);
work_set_target_ratio(work, vhash);
pdata[19] = work->nonces[0] + 1;
if (work->nonces[1] != UINT32_MAX)
{
be32enc(&endiandata[19], work->nonces[1]);
lyra2Z_hash(vhash, endiandata);
if (vhash[7] <= ptarget[7] && fulltest(vhash, ptarget)) {
bn_set_target_ratio(work, vhash, 1);
work->valid_nonces++;
}
pdata[19] = max(work->nonces[0], work->nonces[1]) + 1; // cursor
}
return work->valid_nonces;
}
else if (vhash[7] > ptarget[7]) {
gpu_increment_reject(thr_id);
if (!opt_quiet) gpulog(LOG_WARNING, thr_id,
"result for %08x does not validate on CPU!", work->nonces[0]);
pdata[19] = work->nonces[0];
continue;
}
}
if ((uint64_t)throughput + pdata[19] >= max_nonce) {
pdata[19] = max_nonce;
break;
}
pdata[19] += throughput;
} while (!work_restart[thr_id].restart);
*hashes_done = pdata[19] - first_nonce;
return 0;
}
// cleanup
extern "C" void free_lyra2Z(int thr_id)
{
int dev_id = device_map[thr_id];
if (!init[thr_id])
return;
cudaThreadSynchronize();
cudaFree(d_hash[thr_id]);
if (device_sm[dev_id] >= 350)
cudaFree(d_matrix[thr_id]);
init[thr_id] = false;
cudaDeviceSynchronize();
}

3
miner.h

@ -292,6 +292,7 @@ extern int scanhash_lbry(int thr_id, struct work *work, uint32_t max_nonce, unsi
extern int scanhash_luffa(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_luffa(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_lyra2v2(int thr_id,struct work* work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_lyra2v2(int thr_id,struct work* work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_lyra2Z(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_myriad(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_myriad(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_neoscrypt(int thr_id, struct work *work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_neoscrypt(int thr_id, struct work *work, uint32_t max_nonce, unsigned long *hashes_done);
extern int scanhash_nist5(int thr_id, struct work *work, uint32_t max_nonce, unsigned long *hashes_done); extern int scanhash_nist5(int thr_id, struct work *work, uint32_t max_nonce, unsigned long *hashes_done);
@ -344,6 +345,7 @@ extern void free_lbry(int thr_id);
extern void free_luffa(int thr_id); extern void free_luffa(int thr_id);
extern void free_lyra2(int thr_id); extern void free_lyra2(int thr_id);
extern void free_lyra2v2(int thr_id); extern void free_lyra2v2(int thr_id);
extern void free_lyra2Z(int thr_id);
extern void free_myriad(int thr_id); extern void free_myriad(int thr_id);
extern void free_neoscrypt(int thr_id); extern void free_neoscrypt(int thr_id);
extern void free_nist5(int thr_id); extern void free_nist5(int thr_id);
@ -863,6 +865,7 @@ void groestlhash(void *state, const void *input);
void lbry_hash(void *output, const void *input); void lbry_hash(void *output, const void *input);
void lyra2re_hash(void *state, const void *input); void lyra2re_hash(void *state, const void *input);
void lyra2v2_hash(void *state, const void *input); void lyra2v2_hash(void *state, const void *input);
void lyra2Z_hash(void *state, const void *input);
void myriadhash(void *state, const void *input); void myriadhash(void *state, const void *input);
void neoscrypt(uchar *output, const uchar *input, uint32_t profile); void neoscrypt(uchar *output, const uchar *input, uint32_t profile);
void nist5hash(void *state, const void *input); void nist5hash(void *state, const void *input);

3
util.cpp

@ -2201,6 +2201,9 @@ void print_hash_tests(void)
lyra2v2_hash(&hash[0], &buf[0]); lyra2v2_hash(&hash[0], &buf[0]);
printpfx("lyra2v2", hash); printpfx("lyra2v2", hash);
lyra2Z_hash(&hash[0], &buf[0]);
printpfx("lyra2z", hash);
myriadhash(&hash[0], &buf[0]); myriadhash(&hash[0], &buf[0]);
printpfx("myriad", hash); printpfx("myriad", hash);

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