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salsa_kernel.cu
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//
// Contains the autotuning logic and some utility functions.
// Note that all CUDA kernels have been moved to other .cu files
//
// NOTE: compile this .cu module for compute_10,sm_10 with --maxrregcount=64
//
#ifdef WIN32
#include <windows.h>
#endif
#include <stdio.h>
#include <time.h>
#include <sys/time.h>
#include <unistd.h>
#include <ctype.h>
#include <map>
#include <algorithm>
#include <cuda.h>
#include "salsa_kernel.h"
#include "titan_kernel.h"
#include "fermi_kernel.h"
#include "test_kernel.h"
#include "nv_kernel.h"
#include "nv_kernel2.h"
#include "kepler_kernel.h"
#include "miner.h"
#if WIN32
#ifdef _WIN64
#define _64BIT 1
#endif
#else
#if __x86_64__
#define _64BIT 1
#endif
#endif
#if _64BIT
#define MAXMEM 0x300000000ULL // 12 GB (the largest Kepler)
#else
#define MAXMEM 0xFFFFFFFFULL // nearly 4 GB (32 bit limitations)
#endif
// require CUDA 5.5 driver API
#define DMAJ 5
#define DMIN 5
// define some error checking macros
#undef checkCudaErrors
#if WIN32
#define DELIMITER '\\'
#else
#define DELIMITER '/'
#endif
#define __FILENAME__ ( strrchr(__FILE__, DELIMITER) != NULL ? strrchr(__FILE__, DELIMITER)+1 : __FILE__ )
#define checkCudaErrors(x) \
{ \
cudaGetLastError(); \
x; \
cudaError_t err = cudaGetLastError(); \
if (err != cudaSuccess) \
applog(LOG_ERR, "GPU #%d: cudaError %d (%s) calling '%s' (%s line %d)\n", device_map[thr_id], err, cudaGetErrorString(err), #x, __FILENAME__, __LINE__); \
}
// some globals containing pointers to device memory (for chunked allocation)
// [8] indexes up to 8 threads (0...7)
int MAXWARPS[8];
uint32_t* h_V[8][TOTAL_WARP_LIMIT*64]; // NOTE: the *64 prevents buffer overflow for --keccak
uint32_t h_V_extra[8][TOTAL_WARP_LIMIT*64]; // with really large kernel launch configurations
extern "C" int cuda_num_devices()
{
int version;
cudaError_t err = cudaDriverGetVersion(&version);
if (err != cudaSuccess)
{
applog(LOG_ERR, "Unable to query CUDA driver version! Is an nVidia driver installed?");
exit(1);
}
int maj = version / 1000, min = version % 100; // same as in deviceQuery sample
if (maj < DMAJ || (maj == DMAJ && min < DMIN))
{
applog(LOG_ERR, "Driver does not support CUDA %d.%d API! Update your nVidia driver!", DMAJ, DMIN);
exit(1);
}
int GPU_N;
err = cudaGetDeviceCount(&GPU_N);
if (err != cudaSuccess)
{
applog(LOG_ERR, "Unable to query number of CUDA devices! Is an nVidia driver installed?");
exit(1);
}
return GPU_N;
}
static bool substringsearch(const char *haystack, const char *needle, int &match)
{
int hlen = strlen(haystack);
int nlen = strlen(needle);
for (int i=0; i < hlen; ++i)
{
if (haystack[i] == ' ') continue;
int j=0, x = 0;
while(j < nlen)
{
if (haystack[i+x] == ' ') {++x; continue;}
if (needle[j] == ' ') {++j; continue;}
if (needle[j] == '#') return ++match == needle[j+1]-'0';
if (tolower(haystack[i+x]) != tolower(needle[j])) break;
++j; ++x;
}
if (j == nlen) return true;
}
return false;
}
extern "C" int cuda_finddevice(char *name)
{
int num = cuda_num_devices();
int match = 0;
for (int i=0; i < num; ++i)
{
cudaDeviceProp props;
if (cudaGetDeviceProperties(&props, i) == cudaSuccess)
if (substringsearch(props.name, name, match)) return i;
}
return -1;
}
KernelInterface *Best_Kernel_Heuristics(cudaDeviceProp *props)
{
KernelInterface *kernel = NULL;
if (opt_algo == ALGO_SCRYPT || (opt_algo == ALGO_SCRYPT_JANE && N <= 8192))
{
// high register count kernels (scrypt, low N-factor scrypt-jane)
if (props->major > 3 || (props->major == 3 && props->minor == 5))
kernel = new NV2Kernel();
else if (props->major == 3 && props->minor == 0)
kernel = new NVKernel();
else if (props->major == 2 || props->major == 1)
kernel = new FermiKernel();
}
else
{
// low register count kernels (high N-factor scrypt-jane)
if (props->major == 3 && props->minor == 5)
kernel = new TitanKernel();
else if (props->major == 3 && props->minor == 0)
kernel = new KeplerKernel();
else if (props->major == 2 || props->major == 1)
kernel = new TestKernel();
}
return kernel;
}
bool validate_config(char *config, int &b, int &w, KernelInterface **kernel = NULL, cudaDeviceProp *props = NULL)
{
bool success = false;
char kernelid = ' ';
if (config != NULL)
{
if (config[0] == 'T' || config[0] == 'K' || config[0] == 'F' || config[0] == 'L' ||
config[0] == 't' || config[0] == 'k' || config[0] == 'f' ||
config[0] == 'Z' || config[0] == 'Y' || config[0] == 'X') {
kernelid = config[0];
config++;
}
if (config[0] >= '0' && config[0] <= '9')
if (sscanf(config, "%dx%d", &b, &w) == 2)
success = true;
if (success && kernel != NULL)
{
switch (kernelid)
{
case 'T': case 'Z': *kernel = new NV2Kernel(); break;
case 't': *kernel = new TitanKernel(); break;
case 'K': case 'Y': *kernel = new NVKernel(); break;
case 'k': *kernel = new KeplerKernel(); break;
case 'F': case 'L': *kernel = new FermiKernel(); break;
case 'f': case 'X': *kernel = new TestKernel(); break;
case ' ': // choose based on device architecture
*kernel = Best_Kernel_Heuristics(props);
break;
}
}
}
return success;
}
std::map<int, int> context_blocks;
std::map<int, int> context_wpb;
std::map<int, bool> context_concurrent;
std::map<int, KernelInterface *> context_kernel;
std::map<int, uint32_t *> context_idata[2];
std::map<int, uint32_t *> context_odata[2];
std::map<int, cudaStream_t> context_streams[2];
std::map<int, uint32_t *> context_X[2];
std::map<int, uint32_t *> context_H[2];
std::map<int, cudaEvent_t> context_serialize[2];
// for SHA256 hashing on GPU
std::map<int, uint32_t *> context_tstate[2];
std::map<int, uint32_t *> context_ostate[2];
std::map<int, uint32_t *> context_hash[2];
int find_optimal_blockcount(int thr_id, KernelInterface* &kernel, bool &concurrent, int &wpb);
extern "C" void cuda_shutdown(int thr_id)
{
checkCudaErrors(cudaStreamSynchronize(context_streams[0][thr_id]));
checkCudaErrors(cudaStreamSynchronize(context_streams[1][thr_id]));
checkCudaErrors(cudaThreadExit());
}
extern "C" int cuda_throughput(int thr_id)
{
int GRID_BLOCKS, WARPS_PER_BLOCK;
if (context_blocks.find(thr_id) == context_blocks.end())
{
#if 0
CUcontext ctx;
cuCtxCreate( &ctx, CU_CTX_SCHED_YIELD, device_map[thr_id] );
cuCtxSetCurrent(ctx);
#else
checkCudaErrors(cudaSetDeviceFlags(cudaDeviceScheduleYield));
checkCudaErrors(cudaSetDevice(device_map[thr_id]));
checkCudaErrors(cudaFree(0));
#endif
KernelInterface *kernel;
bool concurrent; GRID_BLOCKS = find_optimal_blockcount(thr_id, kernel, concurrent, WARPS_PER_BLOCK);
unsigned int THREADS_PER_WU = kernel->threads_per_wu();
unsigned int mem_size = WU_PER_LAUNCH * sizeof(uint32_t) * 32;
unsigned int state_size = WU_PER_LAUNCH * sizeof(uint32_t) * 8;
// allocate device memory for scrypt_core inputs and outputs
uint32_t *tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, mem_size)); context_idata[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, mem_size)); context_idata[1][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, mem_size)); context_odata[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, mem_size)); context_odata[1][thr_id] = tmp;
// allocate pinned host memory for scrypt hashes
checkCudaErrors(cudaHostAlloc((void **) &tmp, state_size, cudaHostAllocDefault)); context_H[0][thr_id] = tmp;
checkCudaErrors(cudaHostAlloc((void **) &tmp, state_size, cudaHostAllocDefault)); context_H[1][thr_id] = tmp;
if (opt_algo == ALGO_SCRYPT)
{
if (parallel < 2)
{
// allocate pinned host memory for scrypt_core input/output
checkCudaErrors(cudaHostAlloc((void **) &tmp, mem_size, cudaHostAllocDefault)); context_X[0][thr_id] = tmp;
checkCudaErrors(cudaHostAlloc((void **) &tmp, mem_size, cudaHostAllocDefault)); context_X[1][thr_id] = tmp;
}
else
{
// allocate tstate, ostate, scrypt hash device memory
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_tstate[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_tstate[1][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_ostate[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_ostate[1][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[1][thr_id] = tmp;
}
}
else if (opt_algo == ALGO_SCRYPT_JANE)
{
// allocate pinned host memory for scrypt_core input/output
checkCudaErrors(cudaHostAlloc((void **) &tmp, mem_size, cudaHostAllocDefault)); context_X[0][thr_id] = tmp;
checkCudaErrors(cudaHostAlloc((void **) &tmp, mem_size, cudaHostAllocDefault)); context_X[1][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[1][thr_id] = tmp;
}
else if (opt_algo == ALGO_KECCAK)
{
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[0][thr_id] = tmp;
checkCudaErrors(cudaMalloc((void **) &tmp, state_size)); context_hash[1][thr_id] = tmp;
}
// create two CUDA streams
cudaStream_t tmp2;
checkCudaErrors( cudaStreamCreate(&tmp2) ); context_streams[0][thr_id] = tmp2;
checkCudaErrors( cudaStreamCreate(&tmp2) ); context_streams[1][thr_id] = tmp2;
// events used to serialize the kernel launches (we don't want any overlapping of kernels)
cudaEvent_t tmp4;
checkCudaErrors(cudaEventCreateWithFlags(&tmp4, cudaEventDisableTiming)); context_serialize[0][thr_id] = tmp4;
checkCudaErrors(cudaEventCreateWithFlags(&tmp4, cudaEventDisableTiming)); context_serialize[1][thr_id] = tmp4;
checkCudaErrors(cudaEventRecord(context_serialize[1][thr_id]));
context_kernel[thr_id] = kernel;
context_concurrent[thr_id] = concurrent;
context_blocks[thr_id] = GRID_BLOCKS;
context_wpb[thr_id] = WARPS_PER_BLOCK;
}
GRID_BLOCKS = context_blocks[thr_id];
WARPS_PER_BLOCK = context_wpb[thr_id];
unsigned int THREADS_PER_WU = context_kernel[thr_id]->threads_per_wu();
return WU_PER_LAUNCH;
}
// Beginning of GPU Architecture definitions
inline int _ConvertSMVer2Cores(int major, int minor)
{
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
typedef struct
{
int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
int Cores;
} sSMtoCores;
sSMtoCores nGpuArchCoresPerSM[] =
{
{ 0x10, 8 }, // Tesla Generation (SM 1.0) G80 class
{ 0x11, 8 }, // Tesla Generation (SM 1.1) G8x class
{ 0x12, 8 }, // Tesla Generation (SM 1.2) G9x class
{ 0x13, 8 }, // Tesla Generation (SM 1.3) GT200 class
{ 0x20, 32 }, // Fermi Generation (SM 2.0) GF100 class
{ 0x21, 48 }, // Fermi Generation (SM 2.1) GF10x class
{ 0x30, 192}, // Kepler Generation (SM 3.0) GK10x class
{ 0x35, 192}, // Kepler Generation (SM 3.5) GK11x class
};
int index = 0;
while (nGpuArchCoresPerSM[index].SM != -1)
{
if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor))
{
return nGpuArchCoresPerSM[index].Cores;
}
index++;
}
// If we don't find the values, we default use the previous one to run properly
// printf("MapSMtoCores for SM %d.%d is undefined. Default to use %d Cores/SM\n", major, minor, nGpuArchCoresPerSM[7].Cores);
return nGpuArchCoresPerSM[7].Cores;
}
#ifdef WIN32
#include <windows.h>
static int console_width()
{
CONSOLE_SCREEN_BUFFER_INFO csbi;
GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi);
return csbi.srWindow.Right - csbi.srWindow.Left + 1;
}
#else
int console_width()
{
return 999;
}
#endif
int find_optimal_blockcount(int thr_id, KernelInterface* &kernel, bool &concurrent, int &WARPS_PER_BLOCK)
{
int cw = console_width();
int optimal_blocks = 0;
cudaDeviceProp props;
checkCudaErrors(cudaGetDeviceProperties(&props, device_map[thr_id]));
concurrent = (props.concurrentKernels > 0);
device_name[thr_id] = strdup(props.name);
applog(LOG_INFO, "GPU #%d: %s with compute capability %d.%d", device_map[thr_id], props.name, props.major, props.minor);
WARPS_PER_BLOCK = -1;
// if not specified, use interactive mode for devices that have the watchdog timer enabled
if (device_interactive[thr_id] == -1)
device_interactive[thr_id] = props.kernelExecTimeoutEnabled;
// turn off texture cache if not otherwise specified
if (device_texturecache[thr_id] == -1)
device_texturecache[thr_id] = 0;
// if not otherwise specified or required, turn single memory allocations off as they reduce
// the amount of memory that we can allocate on Windows Vista, 7 and 8 (WDDM driver model issue)
if (device_singlememory[thr_id] == -1) device_singlememory[thr_id] = 0;
// figure out which kernel implementation to use
if (!validate_config(device_config[thr_id], optimal_blocks, WARPS_PER_BLOCK, &kernel, &props)) {
kernel = NULL;
if (device_config[thr_id] != NULL) {
if (device_config[thr_id][0] == 'T' || device_config[thr_id][0] == 'Z')
kernel = new NV2Kernel();
else if (device_config[thr_id][0] == 't')
kernel = new TitanKernel();
else if (device_config[thr_id][0] == 'K' || device_config[thr_id][0] == 'Y')
kernel = new NVKernel();
else if (device_config[thr_id][0] == 'k')
kernel = new KeplerKernel();
else if (device_config[thr_id][0] == 'F' || device_config[thr_id][0] == 'L')
kernel = new FermiKernel();
else if (device_config[thr_id][0] == 'f' || device_config[thr_id][0] == 'X')
kernel = new TestKernel();
}
if (kernel == NULL) kernel = Best_Kernel_Heuristics(&props);
}
if (kernel->get_major_version() > props.major || kernel->get_major_version() == props.major && kernel->get_minor_version() > props.minor)
{
applog(LOG_ERR, "GPU #%d: the '%c' kernel requires %d.%d capability!", device_map[thr_id], kernel->get_identifier(), kernel->get_major_version(), kernel->get_minor_version());
}
// set whatever cache configuration and shared memory bank mode the kernel prefers
checkCudaErrors(cudaDeviceSetCacheConfig(kernel->cache_config()));
checkCudaErrors(cudaDeviceSetSharedMemConfig(kernel->shared_mem_config()));
// some kernels (e.g. Titan) do not support the texture cache
if (kernel->no_textures() && device_texturecache[thr_id]) {
applog(LOG_WARNING, "GPU #%d: the '%c' kernel ignores the texture cache argument", device_map[thr_id], kernel->get_identifier());
device_texturecache[thr_id] = 0;
}
// Texture caching only works with single memory allocation
if (device_texturecache[thr_id]) device_singlememory[thr_id] = 1;
if (kernel->single_memory() && !device_singlememory[thr_id]) {
applog(LOG_WARNING, "GPU #%d: the '%c' kernel requires single memory allocation", device_map[thr_id], kernel->get_identifier());
device_singlememory[thr_id] = 1;
}
if (device_lookup_gap[thr_id] == 0) device_lookup_gap[thr_id] = 1;
if (!kernel->support_lookup_gap() && device_lookup_gap[thr_id] > 1)
{
applog(LOG_WARNING, "GPU #%d: the '%c' kernel does not support a lookup gap", device_map[thr_id], kernel->get_identifier());
device_lookup_gap[thr_id] = 1;
}
applog(LOG_INFO, "GPU #%d: interactive: %d, tex-cache: %d%c, single-alloc: %d", device_map[thr_id],
(device_interactive[thr_id] != 0) ? 1 : 0,
(device_texturecache[thr_id] != 0) ? device_texturecache[thr_id] : 0, (device_texturecache[thr_id] != 0) ? 'D' : ' ',
(device_singlememory[thr_id] != 0) ? 1 : 0 );
// number of threads collaborating on one work unit (hash)
unsigned int THREADS_PER_WU = kernel->threads_per_wu();
unsigned int LOOKUP_GAP = device_lookup_gap[thr_id];
unsigned int BACKOFF = device_backoff[thr_id];
applog(LOG_INFO, "GPU #%d: %d hashes / %.1f MB per warp.", device_map[thr_id], WU_PER_WARP, ((double)SCRATCH * WU_PER_WARP * sizeof(uint32_t) / (1024 * 1024)));
// compute highest MAXWARPS numbers for kernels allowing cudaBindTexture to succeed
int MW_1D_4 = 134217728 / (SCRATCH * WU_PER_WARP / 4); // for uint4_t textures
int MW_1D_2 = 134217728 / (SCRATCH * WU_PER_WARP / 2); // for uint2_t textures
int MW_1D = kernel->get_texel_width() == 2 ? MW_1D_2 : MW_1D_4;
uint32_t *d_V = NULL;
if (device_singlememory[thr_id])
{
// if no launch config was specified, we simply
// allocate the single largest memory chunk on the device that we can get
if (validate_config(device_config[thr_id], optimal_blocks, WARPS_PER_BLOCK)) {
MAXWARPS[thr_id] = optimal_blocks * WARPS_PER_BLOCK;
}
else {
// compute no. of warps to allocate the largest number producing a single memory block
// PROBLEM: one some devices, ALL allocations will fail if the first one failed. This sucks.
size_t MEM_LIMIT = (unsigned long long)min((unsigned long long)MAXMEM, (unsigned long long)props.totalGlobalMem);
int warpmax = (int)min((unsigned long long)TOTAL_WARP_LIMIT, (unsigned long long)MEM_LIMIT / (SCRATCH * WU_PER_WARP * sizeof(uint32_t)));
// run a bisection algorithm for memory allocation (way more reliable than the previous approach)
int best = 0;
int warp = (warpmax+1)/2;
int interval = (warpmax+1)/2;
while (interval > 0)
{
cudaGetLastError(); // clear the error state
cudaMalloc((void **)&d_V, (size_t)SCRATCH * WU_PER_WARP * warp * sizeof(uint32_t));
if (cudaGetLastError() == cudaSuccess) {
checkCudaErrors(cudaFree(d_V)); d_V = NULL;
if (warp > best) best = warp;
if (warp == warpmax) break;
interval = (interval+1)/2;
warp += interval;
if (warp > warpmax) warp = warpmax;
}
else
{
interval = interval/2;
warp -= interval;
if (warp < 1) warp = 1;
}
}
// back off a bit from the largest possible allocation size
MAXWARPS[thr_id] = ((100-BACKOFF)*best+50)/100;
}
// now allocate a buffer for determined MAXWARPS setting
cudaGetLastError(); // clear the error state
cudaMalloc((void **)&d_V, (size_t)SCRATCH * WU_PER_WARP * MAXWARPS[thr_id] * sizeof(uint32_t));
if (cudaGetLastError() == cudaSuccess) {
for (int i=0; i < MAXWARPS[thr_id]; ++i)
h_V[thr_id][i] = d_V + SCRATCH * WU_PER_WARP * i;
if (device_texturecache[thr_id] == 1)
{
if (validate_config(device_config[thr_id], optimal_blocks, WARPS_PER_BLOCK))
{
if ( optimal_blocks * WARPS_PER_BLOCK > MW_1D ) {
applog(LOG_ERR, "GPU #%d: '%s' exceeds limits for 1D cache. Using 2D cache instead.", device_map[thr_id], device_config[thr_id]);
device_texturecache[thr_id] = 2;
}
}
// bind linear memory to a 1D texture reference
if (kernel->get_texel_width() == 2)
kernel->bindtexture_1D(d_V, SCRATCH * WU_PER_WARP * std::min(MAXWARPS[thr_id],MW_1D_2) * sizeof(uint32_t));
else
kernel->bindtexture_1D(d_V, SCRATCH * WU_PER_WARP * std::min(MAXWARPS[thr_id],MW_1D_4) * sizeof(uint32_t));
}
else if (device_texturecache[thr_id] == 2)
{
// bind pitch linear memory to a 2D texture reference
if (kernel->get_texel_width() == 2)
kernel->bindtexture_2D(d_V, SCRATCH/2, WU_PER_WARP * MAXWARPS[thr_id], SCRATCH*sizeof(uint32_t));
else
kernel->bindtexture_2D(d_V, SCRATCH/4, WU_PER_WARP * MAXWARPS[thr_id], SCRATCH*sizeof(uint32_t));
}
}
else
{
applog(LOG_ERR, "GPU #%d: Launch config '%s' requires too much memory!", device_map[thr_id], device_config[thr_id]);
}
}
else
{
if (validate_config(device_config[thr_id], optimal_blocks, WARPS_PER_BLOCK))
MAXWARPS[thr_id] = optimal_blocks * WARPS_PER_BLOCK;
else
MAXWARPS[thr_id] = TOTAL_WARP_LIMIT;
// chunked memory allocation up to device limits
int warp;
for (warp = 0; warp < MAXWARPS[thr_id]; ++warp) {
// work around partition camping problems by adding a random start address offset to each allocation
h_V_extra[thr_id][warp] = (props.major == 1) ? (16 * (rand()%(16384/16))) : 0;
cudaGetLastError(); // clear the error state
cudaMalloc((void **) &h_V[thr_id][warp], (SCRATCH * WU_PER_WARP + h_V_extra[thr_id][warp])*sizeof(uint32_t));
if (cudaGetLastError() == cudaSuccess) h_V[thr_id][warp] += h_V_extra[thr_id][warp];
else {
h_V_extra[thr_id][warp] = 0;
// back off by several warp allocations to have some breathing room
int remove = (BACKOFF*warp+50)/100;
for (int i=0; warp > 0 && i < remove; ++i) {
warp--;
checkCudaErrors(cudaFree(h_V[thr_id][warp]-h_V_extra[thr_id][warp]));
h_V[thr_id][warp] = NULL; h_V_extra[thr_id][warp] = 0;
}
break;
}
}
MAXWARPS[thr_id] = warp;
}
if (opt_algo == ALGO_SCRYPT || opt_algo == ALGO_SCRYPT_JANE) kernel->set_scratchbuf_constants(MAXWARPS[thr_id], h_V[thr_id]);
if (validate_config(device_config[thr_id], optimal_blocks, WARPS_PER_BLOCK))
{
if (optimal_blocks * WARPS_PER_BLOCK > MAXWARPS[thr_id])
applog(LOG_ERR, "GPU #%d: Given launch config '%s' requires too much memory.", device_map[thr_id], device_config[thr_id]);
if (WARPS_PER_BLOCK > kernel->max_warps_per_block())
applog(LOG_ERR, "GPU #%d: Given launch config '%s' exceeds warp limit for '%c' kernel.", device_map[thr_id], device_config[thr_id], kernel->get_identifier());
}
else
{
if (device_config[thr_id] != NULL && strcasecmp("auto", device_config[thr_id]))
applog(LOG_WARNING, "GPU #%d: Given launch config '%s' does not validate.", device_map[thr_id], device_config[thr_id]);
if (autotune)
{
applog(LOG_INFO, "GPU #%d: Performing auto-tuning (Patience...)", device_map[thr_id]);
// allocate device memory
unsigned int mem_size = MAXWARPS[thr_id] * WU_PER_WARP * sizeof(uint32_t) * 32;
uint32_t *d_idata;
checkCudaErrors(cudaMalloc((void **) &d_idata, mem_size));
uint32_t *d_odata;
checkCudaErrors(cudaMalloc((void **) &d_odata, mem_size));
// pre-initialize some device memory
uint32_t *h_idata = (uint32_t*)malloc(mem_size);
for (unsigned int i=0; i < mem_size/sizeof(uint32_t); ++i) h_idata[i] = i*2654435761UL; // knuth's method
checkCudaErrors(cudaMemcpy(d_idata, h_idata, mem_size, cudaMemcpyHostToDevice));
free(h_idata);
double best_hash_sec = 0.0;
int best_wpb = 0;
// auto-tuning loop
{
// we want to have enough total warps for half the multiprocessors at least
// compute highest MAXWARPS number that we can support based on texture cache mode
int MINTW = props.multiProcessorCount / 2;
int MAXTW = (device_texturecache[thr_id] == 1) ? std::min(MAXWARPS[thr_id],MW_1D) : MAXWARPS[thr_id];
// we want to have blocks for half the multiprocessors at least
int MINB = props.multiProcessorCount / 2;
int MAXB = MAXTW;
applog(LOG_INFO, "GPU #%d: maximum total warps (BxW): %d", device_map[thr_id], MAXTW);
for (int GRID_BLOCKS = MINB; !abort_flag && GRID_BLOCKS <= MAXB; ++GRID_BLOCKS)
{
double Hash[32+1] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
for (WARPS_PER_BLOCK = 1; !abort_flag && WARPS_PER_BLOCK <= kernel->max_warps_per_block(); ++WARPS_PER_BLOCK)
{
double hash_sec = 0;
if (GRID_BLOCKS * WARPS_PER_BLOCK >= MINTW &&
GRID_BLOCKS * WARPS_PER_BLOCK <= MAXTW)
{
// setup execution parameters
dim3 grid(WU_PER_LAUNCH/WU_PER_BLOCK, 1, 1);
dim3 threads(THREADS_PER_WU*WU_PER_BLOCK, 1, 1);
struct timeval tv_start, tv_end;
double tdelta = 0;
checkCudaErrors(cudaDeviceSynchronize());
gettimeofday(&tv_start, NULL);
int repeat = 0;
bool r = false;
do // average several measurements for better exactness
{
r=kernel->run_kernel(grid, threads, WARPS_PER_BLOCK, thr_id, NULL, d_idata, d_odata, N, LOOKUP_GAP, device_interactive[thr_id], true, device_texturecache[thr_id]);
cudaDeviceSynchronize();
if (!r || cudaPeekAtLastError() != cudaSuccess) break;
++repeat;
gettimeofday(&tv_end, NULL);
// for a better result averaging, measure for at least 50ms
} while ((tdelta=(1e-6 * (tv_end.tv_usec-tv_start.tv_usec) + (tv_end.tv_sec-tv_start.tv_sec))) < 0.05);
if (cudaGetLastError() != cudaSuccess || !r) continue;
tdelta /= repeat; // BUGFIX: this averaging over multiple measurements was missing
// for scrypt: in interactive mode only find launch configs where kernel launch times are short enough
// TODO: instead we could reduce the batchsize parameter to meet the launch time requirement.
if (opt_algo == ALGO_SCRYPT && device_interactive[thr_id] && GRID_BLOCKS > 2*props.multiProcessorCount && tdelta > 1.0/30)
if (WARPS_PER_BLOCK == 1) goto skip; else goto skip2;
hash_sec = (double)WU_PER_LAUNCH / tdelta;
Hash[WARPS_PER_BLOCK] = hash_sec;
if (hash_sec > best_hash_sec) {
optimal_blocks = GRID_BLOCKS;
best_hash_sec = hash_sec;
best_wpb = WARPS_PER_BLOCK;
}
}
}
skip2: ;
if (opt_debug) {
if (GRID_BLOCKS == MINB) {
char line[512] = " ";
for (int i=1; i<=kernel->max_warps_per_block(); ++i) {
char tmp[16]; sprintf(tmp, i < 10 ? " x%-2d" : " x%-2d ", i);
strcat(line, tmp);
if (cw == 80 && (i % 8 == 0 && i != kernel->max_warps_per_block()))
strcat(line, "\n ");
}
applog(LOG_DEBUG, line);
}
char kMGT = ' '; bool flag;
for (int j=0; j < 4; ++j) {
flag=false; for (int i=1; i<=kernel->max_warps_per_block(); flag|=Hash[i] >= 1000, i++);
if (flag) for (int i=1; i<=kernel->max_warps_per_block(); Hash[i] /= 1000, i++);
else break;
if (kMGT == ' ') kMGT = 'k';
else if (kMGT == 'k') kMGT = 'M';
else if (kMGT == 'M') kMGT = 'G';
else if (kMGT == 'G') kMGT = 'T';
}
char *format = "%5.4f%c";
flag = false; for (int i=1; i<=kernel->max_warps_per_block(); flag|=Hash[i] >= 1, i++); if (flag) format = "%5.3f%c";
flag = false; for (int i=1; i<=kernel->max_warps_per_block(); flag|=Hash[i] >= 10, i++); if (flag) format = "%5.2f%c";
flag = false; for (int i=1; i<=kernel->max_warps_per_block(); flag|=Hash[i] >= 100, i++); if (flag) format = "%5.1f%c";
char line[512]; sprintf(line, "%3d:", GRID_BLOCKS);
for (int i=1; i<=kernel->max_warps_per_block(); ++i) {
char tmp[16];
if (Hash[i]>0)
sprintf(tmp, format, Hash[i], (i<kernel->max_warps_per_block())?'|':' ');
else
sprintf(tmp, " %c", (i<kernel->max_warps_per_block())?'|':' ');
strcat(line, tmp);
if (cw == 80 && (i % 8 == 0 && i != kernel->max_warps_per_block()))
strcat(line, "\n ");
}
int n = strlen(line)-1; line[n++] = '|'; line[n++] = ' '; line[n++] = kMGT; line[n++] = '\0';
strcat(line, "H/s");
applog(LOG_DEBUG, line);
}
}
skip: ;
}
checkCudaErrors(cudaFree(d_odata));
checkCudaErrors(cudaFree(d_idata));
WARPS_PER_BLOCK = best_wpb;
applog(LOG_INFO, "GPU #%d: %7.2f hash/s with configuration %c%dx%d", device_map[thr_id], best_hash_sec, kernel->get_identifier(), optimal_blocks, WARPS_PER_BLOCK);
}
else
{
// Heuristics for finding a good kernel launch configuration
// base the initial block estimate on the number of multiprocessors
int device_cores = props.multiProcessorCount * _ConvertSMVer2Cores(props.major, props.minor);
// defaults, in case nothing else is chosen below
optimal_blocks = 4 * device_cores / WU_PER_WARP;
WARPS_PER_BLOCK = 2;
// Based on compute capability, pick a known good block x warp configuration.
if (props.major == 3)
{
if (props.minor == 0) // GK104, GK106, GK107
{
if (MAXWARPS[thr_id] > (int)(optimal_blocks * 1.7261905) * 2)
{
// this results in 290x2 configuration on GTX 660Ti (3GB)
// but it requires 3GB memory on the card!
optimal_blocks = (int)(optimal_blocks * 1.7261905);
WARPS_PER_BLOCK = 2;
}
else
{
// this results in 148x2 configuration on GTX 660Ti (2GB)
optimal_blocks = (int)(optimal_blocks * 0.8809524);
WARPS_PER_BLOCK = 2;
}
}
else if (props.minor == 5) // GK110 (Tesla K20X, K20, GeForce GTX TITAN)
{
// TODO: what to do with Titan and Tesla K20(X)?
// for now, do the same as for GTX 660Ti (2GB)
optimal_blocks = (int)(optimal_blocks * 0.8809524);
WARPS_PER_BLOCK = 2;
}
}
// 1st generation Fermi (compute 2.0) GF100, GF110
else if (props.major == 2 && props.minor == 0)
{
// this results in a 60x4 configuration on GTX 570
optimal_blocks = 4 * device_cores / WU_PER_WARP;
WARPS_PER_BLOCK = 4;
}
// 2nd generation Fermi (compute 2.1) GF104,106,108,114,116
else if (props.major == 2 && props.minor == 1)
{
// this results in a 56x2 configuration on GTX 460
optimal_blocks = props.multiProcessorCount * 8;
WARPS_PER_BLOCK = 2;
}
// G80, G92, GT2xx
else if (props.major == 1)
{
if (props.minor == 0) // G80
{
// TODO: anyone knowing good settings for G80?
optimal_blocks = props.multiProcessorCount;
WARPS_PER_BLOCK = 4;
}
else if (props.minor == 1) // G92
{
// e.g. my 9600M works best at 4x4
optimal_blocks = props.multiProcessorCount;
WARPS_PER_BLOCK = 4;
}
else if (props.minor == 2) // GT218, GT216, GT215
{
// TODO: anyone knowing good settings for Compute 1.2?
// for now I assume performance is identical to compute 1.3
optimal_blocks = props.multiProcessorCount;
WARPS_PER_BLOCK = 3;
}
if (props.minor == 3) // GT200
{
// my GTX 260 works best at S27x3
optimal_blocks = props.multiProcessorCount;
WARPS_PER_BLOCK = 3;
}
}
// in case we run out of memory with the automatically chosen configuration,
// first back off with WARPS_PER_BLOCK, then reduce optimal_blocks.
if (WARPS_PER_BLOCK==3 && optimal_blocks * WARPS_PER_BLOCK > MAXWARPS[thr_id])
WARPS_PER_BLOCK = 2;
while (optimal_blocks > 0 && optimal_blocks * WARPS_PER_BLOCK > MAXWARPS[thr_id])
optimal_blocks--;
}
}
applog(LOG_INFO, "GPU #%d: using launch configuration %c%dx%d", device_map[thr_id], kernel->get_identifier(), optimal_blocks, WARPS_PER_BLOCK);
if (device_singlememory[thr_id])
{
if (MAXWARPS[thr_id] != optimal_blocks * WARPS_PER_BLOCK)
{
MAXWARPS[thr_id] = optimal_blocks * WARPS_PER_BLOCK;
if (device_texturecache[thr_id] == 1)
kernel->unbindtexture_1D();
else if (device_texturecache[thr_id] == 2)
kernel->unbindtexture_2D();
checkCudaErrors(cudaFree(d_V)); d_V = NULL;
cudaGetLastError(); // clear the error state
cudaMalloc((void **)&d_V, (size_t)SCRATCH * WU_PER_WARP * MAXWARPS[thr_id] * sizeof(uint32_t));
if (cudaGetLastError() == cudaSuccess) {
for (int i=0; i < MAXWARPS[thr_id]; ++i)
h_V[thr_id][i] = d_V + SCRATCH * WU_PER_WARP * i;
if (device_texturecache[thr_id] == 1)
{
// bind linear memory to a 1D texture reference
if (kernel->get_texel_width() == 2)
kernel->bindtexture_1D(d_V, SCRATCH * WU_PER_WARP * MAXWARPS[thr_id] * sizeof(uint32_t));
else
kernel->bindtexture_1D(d_V, SCRATCH * WU_PER_WARP * MAXWARPS[thr_id] * sizeof(uint32_t));
}
else if (device_texturecache[thr_id] == 2)
{
// bind pitch linear memory to a 2D texture reference
if (kernel->get_texel_width() == 2)
kernel->bindtexture_2D(d_V, SCRATCH/2, WU_PER_WARP * MAXWARPS[thr_id], SCRATCH*sizeof(uint32_t));
else
kernel->bindtexture_2D(d_V, SCRATCH/4, WU_PER_WARP * MAXWARPS[thr_id], SCRATCH*sizeof(uint32_t));
}
// update pointers to scratch buffer in constant memory after reallocation
if (opt_algo == ALGO_SCRYPT || opt_algo == ALGO_SCRYPT_JANE) kernel->set_scratchbuf_constants(MAXWARPS[thr_id], h_V[thr_id]);
}
else
{
applog(LOG_ERR, "GPU #%d: Unable to allocate enough memory for launch config '%s'.", device_map[thr_id], device_config[thr_id]);
}
}
}
else
{
// back off unnecessary memory allocations to have some breathing room
while (MAXWARPS[thr_id] > 0 && MAXWARPS[thr_id] > optimal_blocks * WARPS_PER_BLOCK) {
(MAXWARPS[thr_id])--;
checkCudaErrors(cudaFree(h_V[thr_id][MAXWARPS[thr_id]]-h_V_extra[thr_id][MAXWARPS[thr_id]]));
h_V[thr_id][MAXWARPS[thr_id]] = NULL; h_V_extra[thr_id][MAXWARPS[thr_id]] = 0;
}
}
return optimal_blocks;
}
typedef struct { double value[8]; } tsumarray;
cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id)
{
cudaError_t result = cudaSuccess;
if (situation >= 0)
{
static std::map<int, tsumarray> tsum;
double a = 0.95, b = 0.05;
if (tsum.find(situation) == tsum.end()) { a = 0.5; b = 0.5; } // faster initial convergence
double tsync = 0.0;
double tsleep = 0.95 * tsum[situation].value[thr_id];
if (cudaStreamQuery(stream) == cudaErrorNotReady)
{
usleep((useconds_t)(1e6*tsleep));
struct timeval tv_start, tv_end;
gettimeofday(&tv_start, NULL);
checkCudaErrors(result = cudaStreamSynchronize(stream));
gettimeofday(&tv_end, NULL);
tsync = 1e-6 * (tv_end.tv_usec-tv_start.tv_usec) + (tv_end.tv_sec-tv_start.tv_sec);
}
if (tsync >= 0) tsum[situation].value[thr_id] = a * tsum[situation].value[thr_id] + b * (tsleep+tsync);
#if 0
static int calls = 0;
if (++calls == 50) {
static std::map<int, tsumarray>::iterator it;
for (it = tsum.begin(); it != tsum.end() ; ++it)
{
const int &sit = (*it).first;
const tsumarray &ts = (*it).second;
fprintf(stderr, "%d: %f\n", sit, ts.value[thr_id]);
}
calls = 0;
}
#endif
}
else
{
checkCudaErrors(result = cudaStreamSynchronize(stream));
}
return result;
}
extern "C" void cuda_scrypt_HtoD(int thr_id, uint32_t *X, int stream)
{
unsigned int GRID_BLOCKS = context_blocks[thr_id];
unsigned int WARPS_PER_BLOCK = context_wpb[thr_id];
unsigned int THREADS_PER_WU = context_kernel[thr_id]->threads_per_wu();
unsigned int mem_size = WU_PER_LAUNCH * sizeof(uint32_t) * 32;
// copy host memory to device
checkCudaErrors(cudaMemcpyAsync(context_idata[stream][thr_id], X, mem_size,
cudaMemcpyHostToDevice, context_streams[stream][thr_id]));
}
extern "C" void cuda_scrypt_serialize(int thr_id, int stream)
{
// if the device can concurrently execute multiple kernels, then we must
// wait for the serialization event recorded by the other stream
if (context_concurrent[thr_id] || device_interactive[thr_id])
checkCudaErrors(cudaStreamWaitEvent(context_streams[stream][thr_id], context_serialize[(stream+1)&1][thr_id], 0));
}
extern "C" void cuda_scrypt_done(int thr_id, int stream)
{
// record the serialization event in the current stream
checkCudaErrors(cudaEventRecord(context_serialize[stream][thr_id], context_streams[stream][thr_id]));
}
extern "C" void cuda_scrypt_flush(int thr_id, int stream)
{
// flush the work queue (required for WDDM drivers)
checkCudaErrors(cudaStreamQuery(context_streams[stream][thr_id]));
}
extern "C" void cuda_scrypt_core(int thr_id, int stream, unsigned int N)
{
unsigned int GRID_BLOCKS = context_blocks[thr_id];
unsigned int WARPS_PER_BLOCK = context_wpb[thr_id];
unsigned int THREADS_PER_WU = context_kernel[thr_id]->threads_per_wu();
unsigned int LOOKUP_GAP = device_lookup_gap[thr_id];
// setup execution parameters
dim3 grid(WU_PER_LAUNCH/WU_PER_BLOCK, 1, 1);
dim3 threads(THREADS_PER_WU*WU_PER_BLOCK, 1, 1);
context_kernel[thr_id]->run_kernel(grid, threads, WARPS_PER_BLOCK, thr_id, context_streams[stream][thr_id], context_idata[stream][thr_id], context_odata[stream][thr_id], N, LOOKUP_GAP, device_interactive[thr_id], opt_benchmark, device_texturecache[thr_id]);
}
extern "C" void cuda_prepare_keccak256(int thr_id, const uint32_t host_pdata[20], const uint32_t ptarget[8])
{
context_kernel[thr_id]->prepare_keccak256(thr_id, host_pdata, ptarget);
}
extern "C" uint32_t cuda_do_keccak256(int thr_id, int stream, uint32_t *hash, uint32_t nonce, int throughput, bool do_d2h)
{
return context_kernel[thr_id]->do_keccak256(thr_id, stream, hash, nonce, throughput, do_d2h);
}
extern "C" void cuda_scrypt_DtoH(int thr_id, uint32_t *X, int stream)
{
unsigned int GRID_BLOCKS = context_blocks[thr_id];
unsigned int WARPS_PER_BLOCK = context_wpb[thr_id];
unsigned int THREADS_PER_WU = context_kernel[thr_id]->threads_per_wu();
unsigned int mem_size = WU_PER_LAUNCH * sizeof(uint32_t) * 32;
// copy result from device to host (asynchronously)
checkCudaErrors(cudaMemcpyAsync(X, context_odata[stream][thr_id], mem_size,
cudaMemcpyDeviceToHost, context_streams[stream][thr_id]));
}
extern "C" void cuda_scrypt_sync(int thr_id, int stream)
{
MyStreamSynchronize(context_streams[stream][thr_id], 0, thr_id);
}
extern "C" uint32_t* cuda_transferbuffer(int thr_id, int stream)
{
return context_X[stream][thr_id];
}
extern "C" uint32_t* cuda_hashbuffer(int thr_id, int stream)
{
return context_H[stream][thr_id];
}
////////////////////////////////////////////////////////////////////////////////
//! Compute reference data set on the CPU
//! @param idata input data as provided to device
//! @param reference reference data, computed but preallocated