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clHost.cpp
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clHost.cpp
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// BEAM OpenCL Miner
// OpenCL Host Interface
// Copyright 2018 The Beam Team
// Copyright 2018 Wilke Trei
#include "clHost.h"
#include "./kernels/equihash_150_5_inc.h"
namespace beamMiner {
// Helper functions to split a string
inline vector<string> &split(const string &s, char delim, vector<string> &elems) {
stringstream ss(s);
string item;
while(getline(ss, item, delim)) {
elems.push_back(item);
}
return elems;
}
inline vector<string> split(const string &s, char delim) {
vector<string> elems;
return split(s, delim, elems);
}
// Helper function that tests if a OpenCL device supports a certain CL extension
inline bool hasExtension(cl::Device &device, string extension) {
string info;
device.getInfo(CL_DEVICE_EXTENSIONS, &info);
vector<string> extens = split(info, ' ');
for (int i=0; i<extens.size(); i++) {
if (extens[i].compare(extension) == 0) return true;
}
return false;
}
// This is a bit ugly c-style, but the OpenCL headers are initially for c and
// support c-style callback functions (no member functions) only.
// This function will be called every time a GPU is done with its current work
void CL_CALLBACK CCallbackFunc(cl_event ev, cl_int err , void* data) {
clHost* self = static_cast<clHost*>(((clCallbackData*) data)->host);
self->callbackFunc(err,data);
}
// Function to load the OpenCL kernel and prepare our device for mining
void clHost::loadAndCompileKernel(cl::Device &device, uint32_t pl, bool use3G) {
cout << " Beam OpenCL kernel: loading & compiling" << endl;
// reading the kernel
string progStr = string(__equihash_150_5_cl, __equihash_150_5_cl_len);
/* ifstream file("./kernels/equihash_150_5.cl");
string progStr(istreambuf_iterator<char>(file),(istreambuf_iterator<char>())); */
cl::Program::Sources source(1,std::make_pair(progStr.c_str(), progStr.length()+1));
// Create a program object and build it
vector<cl::Device> devicesTMP;
devicesTMP.push_back(device);
cl::Program program(contexts[pl], source);
cl_int err;
if (!use3G) {
err = program.build(devicesTMP,"");
} else {
err = program.build(devicesTMP,"-DMEM3G");
}
// Check if the build was Ok
if (!err) {
cout << " Beam OpenCL kernel: build sucessfully" << endl;
// Store the device and create a queue for it
cl_command_queue_properties queue_prop = 0;
devices.push_back(device);
queues.push_back(cl::CommandQueue(contexts[pl], devices[devices.size()-1], queue_prop, NULL));
// Reserve events, space for storing results and so on
events.push_back(cl::Event());
results.push_back(NULL);
currentWork.push_back(clCallbackData());
paused.push_back(true);
is3G.push_back(use3G);
solutionCnt.push_back(0);
// Create the kernels
vector<cl::Kernel> newKernels;
newKernels.push_back(cl::Kernel(program, "clearCounter", &err));
newKernels.push_back(cl::Kernel(program, "round0", &err));
newKernels.push_back(cl::Kernel(program, "round1", &err));
newKernels.push_back(cl::Kernel(program, "round2", &err));
newKernels.push_back(cl::Kernel(program, "round3", &err));
newKernels.push_back(cl::Kernel(program, "round4", &err));
newKernels.push_back(cl::Kernel(program, "round5", &err));
if (use3G) {
newKernels.push_back(cl::Kernel(program, "combine3G", &err));
newKernels.push_back(cl::Kernel(program, "repack", &err));
newKernels.push_back(cl::Kernel(program, "move", &err));
} else {
newKernels.push_back(cl::Kernel(program, "combine", &err));
}
kernels.push_back(newKernels);
// Create the buffers
vector<cl::Buffer> newBuffers;
if (!use3G) {
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 71303168, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 71303168, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 71303168, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint2) * 71303168, NULL, &err));
} else {
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 69599232, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 69599232, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 52199424, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint2) * 1, NULL, &err));
}
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint4) * 256, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint) * 49152, NULL, &err));
newBuffers.push_back(cl::Buffer(contexts[pl], CL_MEM_READ_WRITE, sizeof(cl_uint) * 324, NULL, &err));
buffers.push_back(newBuffers);
} else {
cout << " Program build error, device will not be used. " << endl;
// Print error msg so we can debug the kernel source
cout << " Build Log: " << program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(devicesTMP[0]) << endl;
}
}
// Detect the OpenCL hardware on this system
void clHost::detectPlatFormDevices(vector<int32_t> selDev, bool allowCPU, bool force3G) {
// read the OpenCL platforms on this system
cl::Platform::get(&platforms);
// this is for enumerating the devices
uint32_t curDiv = 0;
uint32_t selNum = 0;
for (int pl=0; pl<platforms.size(); pl++) {
// Create the OpenCL contexts, one for each platform
cl_context_properties properties[] = { CL_CONTEXT_PLATFORM, (cl_context_properties)platforms[pl](), 0};
cl::Context context;
if (allowCPU) {
context = cl::Context(CL_DEVICE_TYPE_ALL, properties);
} else {
context = cl::Context(CL_DEVICE_TYPE_GPU, properties);
}
contexts.push_back(context);
// Read the devices of this platform
vector< cl::Device > nDev = context.getInfo<CL_CONTEXT_DEVICES>();
for (uint32_t di=0; di<nDev.size(); di++) {
// Print the device name
string name;
if ( hasExtension(nDev[di], "cl_amd_device_attribute_query") ) {
nDev[di].getInfo(0x4038,&name); // on AMD this gives more readable result
} else {
nDev[di].getInfo(CL_DEVICE_NAME, &name); // for all other GPUs
}
// Get rid of strange characters at the end of device name
if (isalnum((int) name.back()) == 0) {
name.pop_back();
}
cout << "Found device " << curDiv << ": " << name << endl;
// Check if the device should be selected
bool pick = false;
if (selDev[0] == -1) pick = true;
if (selNum < selDev.size()) {
if (curDiv == selDev[selNum]) {
pick = true;
selNum++;
}
}
if (pick) {
// Check if the CPU / GPU has enough memory
uint64_t deviceMemory = nDev[di].getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
uint64_t needed_4G = 7* ((uint64_t) 570425344) + 4096 + 196608 + 1296;
uint64_t needed_3G = 4* ((uint64_t) 556793856) + ((uint64_t) 835190784) + 4096 + 196608 + 1296;
cout << " Total memory: " << deviceMemory / (1024*1024) << " MByte" << endl;
if ( hasExtension(nDev[di], "cl_amd_device_attribute_query") ) {
uint64_t freeDeviceMemory;
nDev[di].getInfo(0x4039, &freeDeviceMemory); // CL_DEVICE_GLOBAL_FREE_MEMORY_AMD
freeDeviceMemory *= 1024;
cout << " Free memory: " << freeDeviceMemory / (1024*1024) << " MByte" << endl;
deviceMemory = min<uint64_t>(deviceMemory, freeDeviceMemory);
}
if ((deviceMemory > needed_4G) && (!force3G)) {
cout << " Beam OpenCL kernel: using 4 Gbyte" << endl;
loadAndCompileKernel(nDev[di], pl, false);
} else if (deviceMemory > needed_3G) {
cout << " Beam OpenCL kernel: using 3 Gbyte" << endl;
loadAndCompileKernel(nDev[di], pl, true);
} else {
cout << " Memory check failed, required minimum memory: " << needed_3G/(1024*1024) << endl;
}
} else {
cout << " Device not used. Not included in --devices parameter." << endl;
}
curDiv++;
}
}
if (devices.size() == 0) {
cout << "No compatible OpenCL devices found or all are deselected. Closing beamMiner." << endl;
exit(0);
}
}
// Setup function called from outside
void clHost::setup(beamStratum* stratumIn, vector<int32_t> devSel, bool allowCPU, bool force3G) {
stratum = stratumIn;
detectPlatFormDevices(devSel, allowCPU, force3G);
}
// Function that will catch new work from the stratum interface and then queue the work on the device
void clHost::queueKernels(uint32_t gpuIndex, clCallbackData* workData) {
cl_ulong4 work;
cl_ulong nonce;
// Get a new set of work from the stratum interface
stratum->getWork(workData->wd, (uint8_t *) &work);
nonce = workData->wd.nonce;
if (!is3G[gpuIndex]) { // Starting the 4G kernels
// Kernel arguments for cleanCounter
kernels[gpuIndex][0].setArg(0, buffers[gpuIndex][5]);
kernels[gpuIndex][0].setArg(1, buffers[gpuIndex][6]);
// Kernel arguments for round0
kernels[gpuIndex][1].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][1].setArg(1, buffers[gpuIndex][2]);
kernels[gpuIndex][1].setArg(2, buffers[gpuIndex][5]);
kernels[gpuIndex][1].setArg(3, work);
kernels[gpuIndex][1].setArg(4, nonce);
// Kernel arguments for round1
kernels[gpuIndex][2].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][2].setArg(1, buffers[gpuIndex][2]);
kernels[gpuIndex][2].setArg(2, buffers[gpuIndex][1]);
kernels[gpuIndex][2].setArg(3, buffers[gpuIndex][3]); // Index tree will be stored here
kernels[gpuIndex][2].setArg(4, buffers[gpuIndex][5]);
// Kernel arguments for round2
kernels[gpuIndex][3].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][3].setArg(1, buffers[gpuIndex][0]); // Index tree will be stored here
kernels[gpuIndex][3].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for round3
kernels[gpuIndex][4].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][4].setArg(1, buffers[gpuIndex][1]); // Index tree will be stored here
kernels[gpuIndex][4].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for round4
kernels[gpuIndex][5].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][5].setArg(1, buffers[gpuIndex][2]); // Index tree will be stored here
kernels[gpuIndex][5].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for round5
kernels[gpuIndex][6].setArg(0, buffers[gpuIndex][2]);
kernels[gpuIndex][6].setArg(1, buffers[gpuIndex][4]); // Index tree will be stored here
kernels[gpuIndex][6].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for Combine
kernels[gpuIndex][7].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][7].setArg(1, buffers[gpuIndex][1]);
kernels[gpuIndex][7].setArg(2, buffers[gpuIndex][2]);
kernels[gpuIndex][7].setArg(3, buffers[gpuIndex][3]);
kernels[gpuIndex][7].setArg(4, buffers[gpuIndex][4]);
kernels[gpuIndex][7].setArg(5, buffers[gpuIndex][5]);
kernels[gpuIndex][7].setArg(6, buffers[gpuIndex][6]);
cl_int err;
// Queue the kernels
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][0], cl::NDRange(0), cl::NDRange(12288), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][1], cl::NDRange(0), cl::NDRange(22369536), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][2], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
queues[gpuIndex].flush();
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][3], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][4], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][5], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][6], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][7], cl::NDRange(0), cl::NDRange(4096), cl::NDRange(16), NULL, NULL);
} else { // Starting the 3G kernels
// Kernel arguments for cleanCounter
kernels[gpuIndex][0].setArg(0, buffers[gpuIndex][5]);
kernels[gpuIndex][0].setArg(1, buffers[gpuIndex][6]);
// Kernel arguments for round0
kernels[gpuIndex][1].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][1].setArg(1, buffers[gpuIndex][5]);
kernels[gpuIndex][1].setArg(2, work);
kernels[gpuIndex][1].setArg(3, nonce);
kernels[gpuIndex][1].setArg(4, (cl_uint) 0);
// Kernel arguments for round1
kernels[gpuIndex][2].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][2].setArg(1, buffers[gpuIndex][1]);
kernels[gpuIndex][2].setArg(2, buffers[gpuIndex][2]); // Index tree will be stored here
kernels[gpuIndex][2].setArg(3, buffers[gpuIndex][5]);
kernels[gpuIndex][2].setArg(4, (cl_uint) 0);
// Kernel arguments for round2
kernels[gpuIndex][3].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][3].setArg(1, buffers[gpuIndex][0]); // Index tree will be stored here
kernels[gpuIndex][3].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for move
kernels[gpuIndex][9].setArg(0, buffers[gpuIndex][2]);
kernels[gpuIndex][9].setArg(1, buffers[gpuIndex][1]);
// Kernel arguments for repack
kernels[gpuIndex][8].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][8].setArg(1, buffers[gpuIndex][0]);
kernels[gpuIndex][8].setArg(2, buffers[gpuIndex][2]); // Index tree will be stored here
// Kernel arguments for round3
kernels[gpuIndex][4].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][4].setArg(1, buffers[gpuIndex][1]); // Index tree will be stored here
kernels[gpuIndex][4].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for round4
kernels[gpuIndex][5].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][5].setArg(1, buffers[gpuIndex][0]); // Index tree will be stored here
kernels[gpuIndex][5].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for round5
kernels[gpuIndex][6].setArg(0, buffers[gpuIndex][0]);
kernels[gpuIndex][6].setArg(1, buffers[gpuIndex][4]); // Index tree will be stored here
kernels[gpuIndex][6].setArg(2, buffers[gpuIndex][5]);
// Kernel arguments for Combine
kernels[gpuIndex][7].setArg(0, buffers[gpuIndex][1]);
kernels[gpuIndex][7].setArg(1, buffers[gpuIndex][2]);
kernels[gpuIndex][7].setArg(2, buffers[gpuIndex][4]);
kernels[gpuIndex][7].setArg(3, buffers[gpuIndex][5]);
kernels[gpuIndex][7].setArg(4, buffers[gpuIndex][6]);
cl_int err;
// Queue the kernels
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][0], cl::NDRange(0), cl::NDRange(12288), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][1], cl::NDRange(0), cl::NDRange(22369536), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][2], cl::NDRange(0), cl::NDRange(8388608), cl::NDRange(256), NULL, NULL);
queues[gpuIndex].flush();
kernels[gpuIndex][1].setArg(4, (cl_uint) 1);
kernels[gpuIndex][2].setArg(4, (cl_uint) 1);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][1], cl::NDRange(0), cl::NDRange(22369536), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][2], cl::NDRange(0), cl::NDRange(8388608), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][3], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][9], cl::NDRange(0), cl::NDRange(34799616), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][8], cl::NDRange(0), cl::NDRange(69599232), cl::NDRange(256), NULL, NULL);
queues[gpuIndex].flush();
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][4], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][5], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][6], cl::NDRange(0), cl::NDRange(16777216), cl::NDRange(256), NULL, NULL);
err = queues[gpuIndex].enqueueNDRangeKernel(kernels[gpuIndex][7], cl::NDRange(0), cl::NDRange(4096), cl::NDRange(16), NULL, NULL);
}
}
// this function will sumit the solutions done on GPU, then fetch new work and restart mining
void clHost::callbackFunc(cl_int err , void* data){
clCallbackData* workInfo = (clCallbackData*) data;
uint32_t gpu = workInfo->gpuIndex;
// Read the number of solutions of the last iteration
uint32_t solutions = results[gpu][0];
for (uint32_t i=0; i<solutions; i++) {
vector<uint32_t> indexes;
indexes.assign(32,0);
memcpy(indexes.data(), &results[gpu][4 + 32*i], sizeof(uint32_t) * 32);
stratum->handleSolution(workInfo->wd,indexes);
}
solutionCnt[gpu] += solutions;
// Get new work and resume working
if (stratum->hasWork()) {
queues[gpu].enqueueUnmapMemObject(buffers[gpu][6], results[gpu], NULL, NULL);
queueKernels(gpu, ¤tWork[gpu]);
results[gpu] = (unsigned *) queues[gpu].enqueueMapBuffer(buffers[gpu][6], CL_FALSE, CL_MAP_READ, 0, sizeof(cl_uint4) * 81, NULL, &events[gpu], NULL);
events[gpu].setCallback(CL_COMPLETE, &CCallbackFunc, (void*) ¤tWork[gpu]);
queues[gpu].flush();
} else {
paused[gpu] = true;
cout << "Device will be paused, waiting for new work" << endl;
}
}
void clHost::startMining() {
// Start mining initially
for (int i=0; i<devices.size(); i++) {
paused[i] = false;
currentWork[i].gpuIndex = i;
currentWork[i].host = (void*) this;
queueKernels(i, ¤tWork[i]);
results[i] = (unsigned *) queues[i].enqueueMapBuffer(buffers[i][6], CL_FALSE, CL_MAP_READ, 0, sizeof(cl_uint4) * 81, NULL, &events[i], NULL);
events[i].setCallback(CL_COMPLETE, &CCallbackFunc, (void*) ¤tWork[i]);
queues[i].flush();
}
// While the mining is running print some statistics
while (restart) {
this_thread::sleep_for(std::chrono::seconds(15));
// Print performance stats (roughly)
cout << "Performance: ";
uint32_t totalSols = 0;
for (int i=0; i<devices.size(); i++) {
uint32_t sol = solutionCnt[i];
solutionCnt[i] = 0;
totalSols += sol;
cout << fixed << setprecision(2) << (double) sol / 15.0 << " sol/s ";
}
if (devices.size() > 1) cout << "| Total: " << setprecision(2) << (double) totalSols / 15.0 << " sol/s ";
cout << endl;
// Check if there are paused devices and restart them
for (int i=0; i<devices.size(); i++) {
if (paused[i] && stratum->hasWork()) {
paused[i] = false;
// Same as above
queueKernels(i, ¤tWork[i]);
results[i] = (unsigned *) queues[i].enqueueMapBuffer(buffers[i][6], CL_FALSE, CL_MAP_READ, 0, sizeof(cl_uint4) * 81, NULL, &events[i], NULL);
events[i].setCallback(CL_COMPLETE, &CCallbackFunc, (void*) ¤tWork[i]);
queues[i].flush();
}
}
}
}
} // end namespace