GOSTcoin support for ccminer CUDA miner project, compatible with most nvidia cards
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#include <stdio.h>
#include <memory.h>
#include <string.h>
#include <unistd.h>
#include <map>
// include thrust
#ifndef __cplusplus
#include <thrust/version.h>
#include <thrust/remove.h>
#include <thrust/device_vector.h>
#include <thrust/iterator/constant_iterator.h>
#else
#include <ctype.h>
#endif
#include "miner.h"
#include "nvml.h"
#include "cuda_runtime.h"
#ifdef __cplusplus
/* miner.h functions are declared in C type, not C++ */
extern "C" {
#endif
// CUDA Devices on the System
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 < 5 || (maj == 5 && min < 5))
{
applog(LOG_ERR, "Driver does not support CUDA %d.%d API! Update your nVidia driver!", 5, 5);
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;
}
int cuda_version()
{
return (int) CUDART_VERSION;
}
void cuda_devicenames()
{
cudaError_t err;
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);
}
if (opt_n_threads)
GPU_N = min(MAX_GPUS, opt_n_threads);
for (int i=0; i < GPU_N; i++)
{
char vendorname[32] = { 0 };
int dev_id = device_map[i];
cudaDeviceProp props;
cudaGetDeviceProperties(&props, dev_id);
device_sm[dev_id] = (props.major * 100 + props.minor * 10);
if (device_name[dev_id]) {
free(device_name[dev_id]);
device_name[dev_id] = NULL;
}
#ifdef USE_WRAPNVML
if (gpu_vendor((uint8_t)props.pciBusID, vendorname) > 0 && strlen(vendorname)) {
device_name[dev_id] = (char*) calloc(1, strlen(vendorname) + strlen(props.name) + 2);
if (!strncmp(props.name, "GeForce ", 8))
sprintf(device_name[dev_id], "%s %s", vendorname, &props.name[8]);
else
sprintf(device_name[dev_id], "%s %s", vendorname, props.name);
} else
#endif
device_name[dev_id] = strdup(props.name);
}
}
void cuda_print_devices()
{
int ngpus = cuda_num_devices();
cuda_devicenames();
for (int n=0; n < ngpus; n++) {
int dev_id = device_map[n % MAX_GPUS];
cudaDeviceProp props;
cudaGetDeviceProperties(&props, dev_id);
if (!opt_n_threads || n < opt_n_threads) {
fprintf(stderr, "GPU #%d: SM %d.%d %s\n", dev_id, props.major, props.minor, device_name[dev_id]);
#ifdef USE_WRAPNVML
if (opt_debug) nvml_print_device_info(dev_id);
#endif
}
}
}
void cuda_shutdown()
{
cudaDeviceSynchronize();
cudaDeviceReset();
}
static bool substringsearch(const char *haystack, const char *needle, int &match)
{
int hlen = (int) strlen(haystack);
int nlen = (int) 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;
}
// CUDA Gerät nach Namen finden (gibt Geräte-Index zurück oder -1)
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;
}
// since 1.7
uint32_t cuda_default_throughput(int thr_id, uint32_t defcount)
{
//int dev_id = device_map[thr_id % MAX_GPUS];
uint32_t throughput = gpus_intensity[thr_id] ? gpus_intensity[thr_id] : defcount;
if (gpu_threads > 1 && throughput == defcount) throughput /= (gpu_threads-1);
if (api_thr_id != -1) api_set_throughput(thr_id, throughput);
//gpulog(LOG_INFO, thr_id, "throughput %u", throughput);
return throughput;
}
// if we use 2 threads on the same gpu, we need to reinit the threads
void cuda_reset_device(int thr_id, bool *init)
{
int dev_id = device_map[thr_id % MAX_GPUS];
cudaSetDevice(dev_id);
if (init != NULL) {
// with init array, its meant to be used in algo's scan code...
for (int i=0; i < MAX_GPUS; i++) {
if (device_map[i] == dev_id) {
init[i] = false;
}
}
// force exit from algo's scan loops/function
restart_threads();
cudaDeviceSynchronize();
while (cudaStreamQuery(NULL) == cudaErrorNotReady)
usleep(1000);
}
cudaDeviceReset();
if (opt_cudaschedule >= 0) {
cudaSetDeviceFlags((unsigned)(opt_cudaschedule & cudaDeviceScheduleMask));
} else {
cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync);
}
cudaDeviceSynchronize();
}
// return free memory in megabytes
int cuda_available_memory(int thr_id)
{
int dev_id = device_map[thr_id % MAX_GPUS];
size_t mtotal, mfree = 0;
cudaSetDevice(dev_id);
cudaMemGetInfo(&mfree, &mtotal);
return (int) (mfree / (1024 * 1024));
}
// Check (and reset) last cuda error, and report it in logs
void cuda_log_lasterror(int thr_id, const char* func, int line)
{
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess && !opt_quiet)
gpulog(LOG_WARNING, thr_id, "%s:%d %s", func, line, cudaGetErrorString(err));
}
// Clear any cuda error in non-cuda unit (.c/.cpp)
void cuda_clear_lasterror()
{
cudaGetLastError();
}
#ifdef __cplusplus
} /* extern "C" */
#endif
int cuda_gpu_clocks(struct cgpu_info *gpu)
{
cudaDeviceProp props;
if (cudaGetDeviceProperties(&props, gpu->gpu_id) == cudaSuccess) {
gpu->gpu_clock = props.clockRate;
gpu->gpu_memclock = props.memoryClockRate;
gpu->gpu_mem = props.totalGlobalMem;
return 0;
}
return -1;
}
// Zeitsynchronisations-Routine von cudaminer mit CPU sleep
// Note: if you disable all of these calls, CPU usage will hit 100%
typedef struct { double value[8]; } tsumarray;
cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id)
{
cudaError_t result = cudaSuccess;
if (abort_flag)
return result;
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);
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);
}
else
result = cudaStreamSynchronize(stream);
return result;
}
void cudaReportHardwareFailure(int thr_id, cudaError_t err, const char* func)
{
struct cgpu_info *gpu = &thr_info[thr_id].gpu;
gpu->hw_errors++;
gpulog(LOG_ERR, thr_id, "%s %s", func, cudaGetErrorString(err));
sleep(1);
}