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158 lines
3.8 KiB
158 lines
3.8 KiB
#include <stdio.h> |
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#include <memory.h> |
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#include <string.h> |
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#include <map> |
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#ifndef _WIN32 |
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#include <unistd.h> |
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#endif |
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// include thrust |
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#ifndef __cplusplus |
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#include <thrust/version.h> |
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#include <thrust/remove.h> |
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#include <thrust/device_vector.h> |
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#include <thrust/iterator/constant_iterator.h> |
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#else |
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#include <ctype.h> |
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#endif |
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#include "miner.h" |
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#include "cuda_runtime.h" |
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#ifdef WIN32 |
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#include "compat.h" // sleep |
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#endif |
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extern char *device_name[8]; |
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extern int device_map[8]; |
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extern int device_sm[8]; |
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// CUDA Devices on the System |
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int cuda_num_devices() |
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{ |
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int version; |
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cudaError_t err = cudaDriverGetVersion(&version); |
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if (err != cudaSuccess) |
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{ |
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applog(LOG_ERR, "Unable to query CUDA driver version! Is an nVidia driver installed?"); |
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exit(1); |
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} |
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int maj = version / 1000, min = version % 100; // same as in deviceQuery sample |
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if (maj < 5 || (maj == 5 && min < 5)) |
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{ |
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applog(LOG_ERR, "Driver does not support CUDA %d.%d API! Update your nVidia driver!", 5, 5); |
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exit(1); |
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} |
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int GPU_N; |
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err = cudaGetDeviceCount(&GPU_N); |
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if (err != cudaSuccess) |
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{ |
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applog(LOG_ERR, "Unable to query number of CUDA devices! Is an nVidia driver installed?"); |
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exit(1); |
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} |
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return GPU_N; |
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} |
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void cuda_devicenames() |
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{ |
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cudaError_t err; |
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int GPU_N; |
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err = cudaGetDeviceCount(&GPU_N); |
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if (err != cudaSuccess) |
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{ |
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applog(LOG_ERR, "Unable to query number of CUDA devices! Is an nVidia driver installed?"); |
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exit(1); |
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} |
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for (int i=0; i < GPU_N; i++) |
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{ |
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cudaDeviceProp props; |
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cudaGetDeviceProperties(&props, device_map[i]); |
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device_name[i] = strdup(props.name); |
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device_sm[i] = props.major * 100 + props.minor * 10; |
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} |
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} |
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// Can't be called directly in cpu-miner.c |
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void cuda_devicereset() |
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{ |
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cudaDeviceReset(); |
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} |
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static bool substringsearch(const char *haystack, const char *needle, int &match) |
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{ |
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int hlen = (int) strlen(haystack); |
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int nlen = (int) strlen(needle); |
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for (int i=0; i < hlen; ++i) |
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{ |
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if (haystack[i] == ' ') continue; |
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int j=0, x = 0; |
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while(j < nlen) |
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{ |
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if (haystack[i+x] == ' ') {++x; continue;} |
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if (needle[j] == ' ') {++j; continue;} |
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if (needle[j] == '#') return ++match == needle[j+1]-'0'; |
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if (tolower(haystack[i+x]) != tolower(needle[j])) break; |
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++j; ++x; |
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} |
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if (j == nlen) return true; |
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} |
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return false; |
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} |
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// CUDA Gerät nach Namen finden (gibt Geräte-Index zurück oder -1) |
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int cuda_finddevice(char *name) |
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{ |
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int num = cuda_num_devices(); |
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int match = 0; |
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for (int i=0; i < num; ++i) |
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{ |
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cudaDeviceProp props; |
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if (cudaGetDeviceProperties(&props, i) == cudaSuccess) |
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if (substringsearch(props.name, name, match)) return i; |
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} |
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return -1; |
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} |
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// Zeitsynchronisations-Routine von cudaminer mit CPU sleep |
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typedef struct { double value[8]; } tsumarray; |
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cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id) |
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{ |
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cudaError_t result = cudaSuccess; |
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if (situation >= 0) |
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{ |
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static std::map<int, tsumarray> tsum; |
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double a = 0.95, b = 0.05; |
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if (tsum.find(situation) == tsum.end()) { a = 0.5; b = 0.5; } // faster initial convergence |
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double tsync = 0.0; |
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double tsleep = 0.95 * tsum[situation].value[thr_id]; |
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if (cudaStreamQuery(stream) == cudaErrorNotReady) |
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{ |
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usleep((useconds_t)(1e6*tsleep)); |
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struct timeval tv_start, tv_end; |
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gettimeofday(&tv_start, NULL); |
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result = cudaStreamSynchronize(stream); |
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gettimeofday(&tv_end, NULL); |
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tsync = 1e-6 * (tv_end.tv_usec-tv_start.tv_usec) + (tv_end.tv_sec-tv_start.tv_sec); |
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} |
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if (tsync >= 0) tsum[situation].value[thr_id] = a * tsum[situation].value[thr_id] + b * (tsleep+tsync); |
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} |
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else |
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result = cudaStreamSynchronize(stream); |
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return result; |
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} |
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void cudaReportHardwareFailure(int thr_id, cudaError_t err, const char* func) |
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{ |
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struct cgpu_info *gpu = &thr_info[thr_id].gpu; |
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gpu->hw_errors++; |
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applog(LOG_ERR, "GPU #%d: %s %s", device_map[thr_id], func, cudaGetErrorString(err)); |
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sleep(1); |
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}
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