145 lines
4 KiB
C++
145 lines
4 KiB
C++
/* XMRig
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* Copyright (c) 2018-2021 SChernykh <https://github.com/SChernykh>
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* Copyright (c) 2016-2021 XMRig <https://github.com/xmrig>, <support@xmrig.com>
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#ifndef XMRIG_CUDACONFIG_GEN_H
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#define XMRIG_CUDACONFIG_GEN_H
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#include "backend/common/Threads.h"
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#include "backend/cuda/CudaThreads.h"
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#include "backend/cuda/wrappers/CudaDevice.h"
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#include <algorithm>
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namespace xmrig {
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static inline size_t generate(const char *key, Threads<CudaThreads> &threads, const Algorithm &algorithm, const std::vector<CudaDevice> &devices)
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{
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if (threads.isExist(algorithm) || threads.has(key)) {
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return 0;
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}
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return threads.move(key, CudaThreads(devices, algorithm));
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}
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template<Algorithm::Family FAMILY>
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static inline size_t generate(Threads<CudaThreads> &, const std::vector<CudaDevice> &) { return 0; }
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template<>
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size_t inline generate<Algorithm::CN>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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size_t count = 0;
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count += generate(Algorithm::kCN, threads, Algorithm::CN_1, devices);
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count += generate(Algorithm::kCN_2, threads, Algorithm::CN_2, devices);
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if (!threads.isExist(Algorithm::CN_0)) {
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threads.disable(Algorithm::CN_0);
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count++;
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}
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return count;
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}
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#ifdef XMRIG_ALGO_CN_LITE
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template<>
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size_t inline generate<Algorithm::CN_LITE>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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size_t count = generate(Algorithm::kCN_LITE, threads, Algorithm::CN_LITE_1, devices);
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if (!threads.isExist(Algorithm::CN_LITE_0)) {
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threads.disable(Algorithm::CN_LITE_0);
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++count;
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}
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return count;
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}
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#endif
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#ifdef XMRIG_ALGO_CN_HEAVY
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template<>
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size_t inline generate<Algorithm::CN_HEAVY>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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return generate(Algorithm::kCN_HEAVY, threads, Algorithm::CN_HEAVY_0, devices);
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}
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#endif
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#ifdef XMRIG_ALGO_CN_PICO
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template<>
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size_t inline generate<Algorithm::CN_PICO>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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return generate(Algorithm::kCN_PICO, threads, Algorithm::CN_PICO_0, devices);
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}
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#endif
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#ifdef XMRIG_ALGO_CN_FEMTO
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template<>
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size_t inline generate<Algorithm::CN_FEMTO>(Threads<CudaThreads>& threads, const std::vector<CudaDevice>& devices)
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{
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return generate(Algorithm::kCN_UPX2, threads, Algorithm::CN_UPX2, devices);
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}
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#endif
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#ifdef XMRIG_ALGO_RANDOMX
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template<>
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size_t inline generate<Algorithm::RANDOM_X>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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size_t count = 0;
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auto rx = CudaThreads(devices, Algorithm::RX_0);
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auto wow = CudaThreads(devices, Algorithm::RX_WOW);
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auto arq = CudaThreads(devices, Algorithm::RX_ARQ);
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if (!threads.isExist(Algorithm::RX_WOW) && wow != rx) {
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count += threads.move(Algorithm::kRX_WOW, std::move(wow));
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}
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if (!threads.isExist(Algorithm::RX_ARQ) && arq != rx) {
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count += threads.move(Algorithm::kRX_ARQ, std::move(arq));
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}
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count += threads.move(Algorithm::kRX, std::move(rx));
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return count;
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}
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#endif
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#ifdef XMRIG_ALGO_KAWPOW
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template<>
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size_t inline generate<Algorithm::KAWPOW>(Threads<CudaThreads> &threads, const std::vector<CudaDevice> &devices)
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{
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return generate(Algorithm::kKAWPOW, threads, Algorithm::KAWPOW_RVN, devices);
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}
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#endif
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} /* namespace xmrig */
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#endif /* XMRIG_CUDACONFIG_GEN_H */
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