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Framework.cuh.bak
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/*
*
GMiner can find frequent itemsets using computing power of GPUs.
Copyright (C)2018 Pradyumn Agrawal, Akash Budhauliya, Arjun Gupta
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef FRAMEWORK_CUH_
#define FRAMEWORK_CUH_
#include <iostream>
#include <vector>
#include <map>
#include <set>
#include <boost/unordered_map.hpp>
#include <omp.h>
#include <pthread.h>
#include <unistd.h>
#include <fstream>
#include "SAUnit.h"
#include "PagesetTmp.h"
#include "VerticalDatabase.h"
#include "Global.h"
#include "DeviceBuf.cuh"
#include "Materialization.cuh"
#include "item.h"
#include "BlockPage.h"
using namespace std;
class Framework {
public:
// constructor with no parameter
Framework() {
}
~Framework() {
}
void initFramework ( verticalDatabase& vd,
inputParameter* input,
int _maxDepth = 30) {
minSingleton = 0;
this->mapFrequentSingletons = vd.mapFrequentSingletons;
this->numOfCandidates = vd.allitems.size();
this->numOfSingletons = vd.allitems.size();
this->verticalListLength = input->verticalListLength;//_verticalListLength;
this->numOfTransactions = vd.tidCount;
this->wholeVerticalListLength = (numOfTransactions % 32 == 0 ? (numOfTransactions / 32) : (numOfTransactions / 32 + 1));
this->numOfPages = ( wholeVerticalListLength % verticalListLength == 0 ? (wholeVerticalListLength / verticalListLength) : (wholeVerticalListLength / verticalListLength + 1));
this->minsupAbsoulte = vd.minSupAbsolute;
this->numOfStreams = input->numOfStreams;
this->maxDepth = _maxDepth;
this->isOutput = input->isWriteOutput;
if(this->isOutput == 1) {
fout.open(input->outputPath);
}
// a variable for the multiple GPUs system
this->numOfGPUs = input->numOfGPUs;
// initialize the virables for parallelization of recordIndexes()
this->numOfCPUs = sysconf(_SC_NPROCESSORS_ONLN);
// a variable used in Apriori-based approach
this->level = 1;
// a varialble used in Materialization
this->isMaterialization = input->isMaterialization;//_isMaterialization;
// initialize the buffers on MM
this->initBufferMM(vd);
// initialize tries
this->initTrie(vd);
// initialize the buffers on DM
this->initBufferDM(numOfCandidates, verticalListLength, numOfStreams, numOfGPUs);
// generate pre-computations in the HIL strategy
if (isMaterialization == true) {
materialization = new Materialization();
materialization->initSystemConfig(this->numOfPages, this->numOfStreams, this->numOfGPUs);
materialization->initMaterialization(vd, verticalListLength, input->sizeOfFragments);
materialization->buildMaterialization(this->initialPageset, this->DM, this->saVec->SAVec);
this->initialPageset->destroyPageset();
} else {
this->mapSingletonIndex = vd.mapSingletonIndex;
}
}
void printFrameworkStatus() {
cout << "initFramework():: numOfCandidates - " << numOfCandidates
<< ", verticalListLength - " << verticalListLength
<< ", numOfTransactions - " << numOfTransactions
<< ", wholeVerticalListLength - " << wholeVerticalListLength
<< ", numOfPages - " << numOfPages << ", numOfStreams - "
<< numOfStreams << endl;
}
void initBufferDM(int _numOfCandidates, int _verticalListLength, int _numOfStreams, int _numOfGPUs) {
cout <<"Allocating the buffers on GPU device memory. [";
Timer startInitBufferDM = getCurrentTime();
this->DM = new DeviceMemoryForStream[_numOfGPUs];
for (uint loopIdx = 0; loopIdx < _numOfGPUs; loopIdx++) {
this->DM[loopIdx].setDevice(loopIdx);
this->DM[loopIdx].setVariable(_numOfCandidates, _verticalListLength, _numOfStreams);
this->DM[loopIdx].createStreams();
this->DM[loopIdx].cudaMallocMiningBuffers();
}
Timer endInitBufferDM = getCurrentTime();
cout <<(endInitBufferDM-startInitBufferDM)<<" sec]"<<endl;
}
void initBufferMM(verticalDatabase& vd) {
cudaError_t cudaStatus;
for(int idx = 0; idx < CPU_THREAD; idx++) {
vector<uint> tmp_v;
positionVector.push_back(tmp_v);
}
// Computing the number of blocks
uint blk_num = (wholeVerticalListLength-1) / verticalListLength + 1;
wholeVerticalListLength = blk_num * verticalListLength;
wholeVerticalList = new uint*[numOfCandidates];
for (int candidateIdx = 0; candidateIdx < numOfCandidates; candidateIdx++) {
wholeVerticalList[candidateIdx] = new uint[wholeVerticalListLength];
for (int lenIdx = 0; lenIdx < wholeVerticalListLength; lenIdx++) {
wholeVerticalList[candidateIdx][lenIdx] = 0;
}
}
Timer startBuildVerticalDB, endBuildVerticalDB;
startBuildVerticalDB = getCurrentTime();
//convert tids of vd into bitsets
cout <<"Transforming vertical format with bit vectors from horizontal format. [";
for (int idx = 0; idx < vd.allitems.size(); idx++) {
vector<unsigned> itemsetVector;
itemsetVector.push_back(vd.allitems[idx].id);
frequentItemsets.push_back(itemsetVector);
uint* tmp = wholeVerticalList[idx];
for (int arrayIdx = 0; arrayIdx < vd.allitems[idx].transactions.size(); arrayIdx++) {
setbit(tmp, vd.allitems[idx].transactions[arrayIdx], 1);
}
if(isOutput == 1) {
if (vd.allitems[idx].support > 0){
fout << vd.allitems[idx].id<<" ("<<(float)vd.allitems[idx].support/(float)numOfTransactions<<")"<<endl;
//cout << vd.allitems[idx].id<<" ("<<(float)vd.allitems[idx].support/(float)numOfTransactions<<")"<<endl;
}
}
}
endBuildVerticalDB = getCurrentTime();
cout <<(endBuildVerticalDB - startBuildVerticalDB) <<" sec]"<<endl;
// Building the buffers with respect to GMiner
cout <<"Building the buffers of TB and SA with respect to the TFL strategy. [";
Timer startBuildingBuffers = getCurrentTime();
initialPageset = new PagesetTmp();
initialPageset->initPageset(numOfPages, numOfCandidates, verticalListLength);
long long int idxSetArraySize = (long long int)MAX_NUM_CANDIDATE * (long long int)maxDepth;
idxSetArray = new uint[idxSetArraySize];
memset(idxSetArray, 0, sizeof(uint) * idxSetArraySize);
cudaStatus = cudaHostAlloc( (void**) &supportArray,
sizeof(uint) * MAX_NUM_CANDIDATE,
cudaHostAllocDefault);
if (cudaStatus != cudaSuccess) {
cerr << "initFramework():: Error in cudaHostAlloc for supportArray" << endl;
}
memset(supportArray, 0, sizeof(uint) * MAX_NUM_CANDIDATE);
this->saVec = new SAUnitVector();
this->saVec->initSAUnitVector(numOfPages, MAX_NUM_CANDIDATE);
for (int pageIdx = 0; pageIdx < numOfPages; pageIdx++) {
for (int candidateIdx = 0; candidateIdx < numOfCandidates; candidateIdx++) {
uint* oneDimVerticalList = wholeVerticalList[candidateIdx];
initialPageset->pageVector[pageIdx]->updateDataFromWholeArray(
candidateIdx,
oneDimVerticalList,
numOfPages,
wholeVerticalListLength);
}
}
Timer endBuildingBuffers = getCurrentTime();
cout <<(endBuildingBuffers-startBuildingBuffers)<<" sec]"<<endl;
cntArr = new int[CPU_THREAD];
this->cleanWholeVerticalList();
}
void initTrie(verticalDatabase& vd) {
cout <<"Allocating the buffers with respect to candidate itemsets. [";
Timer startInitTrie = getCurrentTime();
// memory optimization by the pingpong execution
candidate_list_length = numOfCandidates;
candidate_list_width = 1;
candidate_list = new unsigned short*[maxDepth];
for (int idx = 0; idx < maxDepth; idx++) {
candidate_list[idx] = new unsigned short[MAX_NUM_CANDIDATE];
memset((void*) candidate_list[idx], 0, sizeof(unsigned short) * MAX_NUM_CANDIDATE);
}
validate = new unsigned short[MAX_NUM_CANDIDATE];
p_boundary_list = new vector<int>;
memset((void *) (validate), 0, sizeof(unsigned short) * MAX_NUM_CANDIDATE);
for (int idx = 0; idx < vd.allitems.size(); idx++) {
candidate_list[0][vd.allitems[idx].id] = (unsigned short)vd.allitems[idx].id;
validate[idx] = 1;
}
p_boundary_list->push_back(candidate_list_length);
// initialize the buffers for the pingpong execution
new_candidate_list = new unsigned short*[maxDepth];
for (int idx = 0; idx < maxDepth; idx++) {
new_candidate_list[idx] = new unsigned short[MAX_NUM_CANDIDATE];
memset((void*) new_candidate_list[idx], 0, sizeof(unsigned short) * MAX_NUM_CANDIDATE);
}
new_p_boundary_list = new vector<int>;
Timer endInitTrie = getCurrentTime();
cout <<(endInitTrie-startInitTrie)<<" sec]"<<endl;
}
void printVec(vector<uint> v) {
cout << "vv - ";
for (int idx = 0; idx < v.size(); idx++) {
cout << v[idx] << " ";
}
cout << endl;
}
void printidxSetArray(int _numOfCandidates, int _maxDepth) {
cout << "printidxSetArray()" << endl;
for (int idx = 0; idx < _numOfCandidates; idx++) {
cout << "candidateIdx - " << idx << endl;
for (int lenIdx = 0; lenIdx < _maxDepth; lenIdx++) {
cout << idxSetArray[idx * _maxDepth + lenIdx] << " ";
}
cout << endl;
}
}
void printOutput(unsigned short** candidate_list, uint* supportArr, unsigned short* validate) {
map<int,int>::iterator m_it;
cout <<"candidate_list_length - " << candidate_list_length <<", candidate_list_width - " << candidate_list_width << ", numOfTransactions - " << numOfTransactions << endl;
for(long long int candidateIdx = 0; candidateIdx < candidate_list_length; candidateIdx++ ) {
for(long long int lenIdx = 0; lenIdx < candidate_list_width; lenIdx++) {
if(validate[candidateIdx] == 1) {
m_it = mapFrequentSingletons.find(candidate_list[lenIdx][candidateIdx]);
fout << m_it->second <<" ";
//cout << candidate_list[lenIdx][candidateIdx] <<" ";
}
}
if(validate[candidateIdx] == 1) {
fout<<"("<<(float)supportArr[candidateIdx] / (float)numOfTransactions<<")"<<endl;
}
}
}
// Performing an iteration composed of candidate generation using CPUs and support counting using GPUs
int nextLevel() {
level++;
int numOfCandidatesForLevel = -1;
int numOfFrequentForLevel = -1;
Timer startSupportCounting, endSupportCounting;
Timer startCandidateGeneration = getCurrentTime();
memset(supportArray, 0, sizeof(uint) * candidate_list_length);
// candidate generation
if (candidate_list_width % 2 == 1) {
candidateGenerationTrie( candidate_list,
new_candidate_list,
p_boundary_list,
new_p_boundary_list,
validate,
idxSetArray);
} else {
candidateGenerationTrie( new_candidate_list,
candidate_list,
new_p_boundary_list,
p_boundary_list,
validate,
idxSetArray);
}
// Storing the position sets of candidate itemsets in the current iteration
if(isMaterialization == true) {
Timer startRecord, endRecord;
startRecord = getCurrentTime();
cout <<"Recording the positionsets in the HIL strategy. [";
parallelRecordIndexes_fixed_width();
endRecord = getCurrentTime();
cout <<(endRecord-startRecord)<<" sec]" << endl;
}
Timer endCandidateGeneration = getCurrentTime();
elapsedTimeCandidateGeneration += (endCandidateGeneration - startCandidateGeneration);
numOfCandidatesForLevel = candidate_list_length;
startSupportCounting = getCurrentTime();
cout <<"Performing support counting. [";
if (isMaterialization == false) {
GMiner_Default
( DM,
this->initialPageset->pageVector,
saVec->SAVec,
idxSetArray,
supportArray,
numOfPages,
this->initialPageset->numOfCandidates,
numOfCandidatesForLevel,
verticalListLength,
level,
maxDepth);
} else {
GMiner_Default
( DM,
this->materialization->materialization->pageVector,
saVec->SAVec,
idxSetArray,
supportArray,
numOfPages,
this->materialization->indexCnt,
numOfCandidatesForLevel,
verticalListLength,
level,
maxDepth);
}
endSupportCounting = getCurrentTime();
cout <<(endSupportCounting-startSupportCounting)<< " sec]"<<endl;
// pruning infrequent itemsets
if (candidate_list_width % 2 == 0) {
numOfFrequentForLevel = this->parallelPruningCandidates(new_candidate_list);
} else {
numOfFrequentForLevel = this->parallelPruningCandidates(candidate_list);
}
// printing the outputs
if(isOutput == 1) {
cout <<"Printing frequent itemsets." << endl;
if(candidate_list_width % 2 == 1) {
printOutput(candidate_list, supportArray, validate);
}
else {
printOutput(new_candidate_list, supportArray, validate);
}
}
return numOfFrequentForLevel;
}
// Performing an iteration by different strategy in the paper
void GMiner_Default_replicate_positionset(
DeviceMemoryForStream* DM,
vector<PageTmp*> sourceVector,
//SAUnitVector* supportVector,
vector<SAUnit*> supportVector,
uint* idxSet,
uint* support,
int numOfPages,
int numOfFrequentItemsetsPreviousLevel,
int numOfCandidates,
int lenOfList,
int depth,
int depthMax) {
// if there is no candidate itemset, exit this function
if (numOfCandidates == 0) {
return;
}
cudaError_t cudaStatus;
uint firstActiveBlock = 0;
set<uint>::iterator activeBlkIterator = materialization->rb_mgr-> active_blk.begin();
firstActiveBlock = *activeBlkIterator;
cout <<"numOfCandidates - " << numOfCandidates <<", numOfGPUs - " << numOfGPUs << endl;
if(numOfCandidates < MAX_ITEMSET_PABUF) {
RUN_MAX_BLK = (numOfCandidates-1) / numOfGPUs + 1;
}
else {
RUN_MAX_BLK = MAX_ITEMSET_PABUF;
}
int numOfIAPs = (numOfCandidates - 1) / RUN_MAX_BLK + 1;
int IAPPos = 0;
bool inOuterLoop = true;
cout <<"RUN_MAX_BLK - " << RUN_MAX_BLK << ", numOfIAPs - " << numOfIAPs << endl;
for(uint outerLoopIdx = 0; outerLoopIdx < numOfIAPs; outerLoopIdx++ ) {
// replicate one of outer join operand to all gpus
for(uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++) {
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device[" << gpuIdx << "]" << endl;
}
uint* IAPAddr = idxSet + ( ( outerLoopIdx ) * RUN_MAX_BLK * MAX_DEPTH ) ;
//cout <<"offset(idxSet) - " <<( ( outerLoopIdx + gpuIdx ) * RUN_MAX_BLK * MAX_DEPTH) << endl;
cudaStatus = cudaMemcpy( DM[gpuIdx].firstIdxSet,
IAPAddr,
sizeof(uint) * MAX_DEPTH * RUN_MAX_BLK,
cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in cudaMemcpy for firstIdxSet of the GPU device["<< gpuIdx << "]" << endl;
}
}
// process an inner join operand
uint numOfPagesEachGPU = numOfPages / numOfGPUs;
uint remainNumOfPagesEachGPU = numOfPages % numOfGPUs;
for(uint pageIdx = 0; pageIdx < numOfPagesEachGPU; pageIdx++)
{
// distributing an inner join operand over GPUs
for (uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++)
{
uint pageOffset = pageIdx * numOfGPUs + gpuIdx;
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device[" << gpuIdx << "]" << endl;
}
uint streamOffset = pageIdx % numOfStreams;
cout <<"pageIdx - " << pageIdx <<", gpuIdx - " << gpuIdx <<", streamOffset - " << streamOffset<< endl;
if (isMaterialization == true) {
uint numOfElementsInFragment = 50;
uint fragmentIdx = 0;
uint fragmentIteration = (numOfFrequentItemsetsPreviousLevel) / numOfElementsInFragment;
uint fragmentRemain = numOfFrequentItemsetsPreviousLevel % numOfElementsInFragment;
uint numOfpartialBitmap = numOfFrequentItemsetsPreviousLevel - (materialization->rb_mgr->numOfItemsetsMaterialized * firstActiveBlock );
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
//sourceVector[pageIdx]->dataForPage
sourceVector[pageOffset]->dataForPage + (firstActiveBlock * verticalListLength * materialization->rb_mgr->numOfItemsetsMaterialized),
// full copy
//this->blks_buffer_vector[streamOffset],
//sizeof(uint) * numOfFrequentItemsetsPreviousLevel * lenOfList,
sizeof(uint) * (numOfpartialBitmap) * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
Kernel_HFI<<< RUN_MAX_BLK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depthMax,
((outerLoopIdx) * RUN_MAX_BLK),
RUN_MAX_BLK
);
}
else {
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
sourceVector[pageOffset]->dataForPage,
sizeof(uint) * numOfFrequentItemsetsPreviousLevel * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
Kernel_TFL<<<RUN_MAX_BLK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depth,
depthMax,
((outerLoopIdx) * RUN_MAX_BLK),
RUN_MAX_BLK
);
}
uint* partialSupportArr = supportVector[pageOffset]->supportArr + (outerLoopIdx) * RUN_MAX_BLK;
cudaStatus = cudaMemcpyAsync( partialSupportArr,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
sizeof(uint) * RUN_MAX_BLK,
cudaMemcpyDeviceToHost,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for supportArr" << endl;
}
if(pageIdx % numOfStreams == (numOfStreams-1))
{
cudaStatus = cudaDeviceSynchronize();
if(cudaStatus != cudaSuccess) {
cerr <<"Error in cudaDeviceSynchronize"<<endl;
}
}
}
}
for(uint pageIdx = numOfGPUs*numOfPagesEachGPU; pageIdx < numOfGPUs*numOfPagesEachGPU + remainNumOfPagesEachGPU; pageIdx++)
{
// distributing an inner join operand over GPUs
uint gpuIdx = pageIdx % numOfGPUs;
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device[" << gpuIdx << "]" << endl;
}
uint streamOffset = pageIdx % numOfStreams;
if (isMaterialization == true) {
uint numOfElementsInFragment = 50;
uint fragmentIdx = 0;
uint fragmentIteration = (numOfFrequentItemsetsPreviousLevel) / numOfElementsInFragment;
uint fragmentRemain = numOfFrequentItemsetsPreviousLevel % numOfElementsInFragment;
uint numOfpartialBitmap = numOfFrequentItemsetsPreviousLevel - (materialization->rb_mgr->numOfItemsetsMaterialized * firstActiveBlock );
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
//sourceVector[pageIdx]->dataForPage
sourceVector[pageIdx]->dataForPage + (firstActiveBlock * verticalListLength * materialization->rb_mgr->numOfItemsetsMaterialized),
// full copy
//this->blks_buffer_vector[streamOffset],
//sizeof(uint) * numOfFrequentItemsetsPreviousLevel * lenOfList,
sizeof(uint) * (numOfpartialBitmap) * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
//cudaThreadSynchronize();
Kernel_HFI<<< RUN_MAX_BLK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depthMax,
((outerLoopIdx) * RUN_MAX_BLK),
RUN_MAX_BLK
//(IAPIdx*numOfGPUs + gpuIdx) * MAX_BLOCK
);
}
else {
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
sourceVector[pageIdx]->dataForPage,
sizeof(uint) * numOfFrequentItemsetsPreviousLevel * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
Kernel_TFL<<<RUN_MAX_BLK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depth,
depthMax,
((outerLoopIdx) * RUN_MAX_BLK),
RUN_MAX_BLK
);
}
uint* partialSupportArr = supportVector[pageIdx]->supportArr + (outerLoopIdx) * RUN_MAX_BLK;
cudaStatus = cudaMemcpyAsync( partialSupportArr,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
sizeof(uint) * RUN_MAX_BLK,
cudaMemcpyDeviceToHost,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for supportArr" << endl;
}
// if(pageIdx % numOfStreams == (numOfStreams-1))
//{
//}
// }
}
cudaStatus = cudaDeviceSynchronize();
if(cudaStatus != cudaSuccess) {
cerr <<"Error in cudaDeviceSynchronize"<<endl;
}
}
for (uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++) {
// set the GPU device used at a time
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if ( cudaStatus != cudaSuccess) {
cerr <<"supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device["<<gpuIdx<<"]"<<endl;
}
cudaStatus = cudaThreadSynchronize();
if(cudaStatus != cudaSuccess) {
cerr <<"supportCountingIndexDelivery():: Error in cudaThreadSynchronize()" << endl;
}
}
for (int pageIdx = 0; pageIdx < numOfPages; pageIdx++) {
for (int candidateIdx = 0; candidateIdx < numOfCandidates; candidateIdx++) {
support[candidateIdx] += supportVector[pageIdx]->supportArr[candidateIdx];
}
}
cout <<"End func"<<endl;
}
// the GPU kernel function support counting 1-1
// the outer join operand - Index Array (IA)
// the inner join operand - transaction Bitmap (TB)
// the replication strategy - replicating the inner join operand
// the performance - the second
void GMiner_Default(
DeviceMemoryForStream* DM,
vector<PageTmp*> sourceVector,
vector<SAUnit*> supportVector,
uint* idxSet,
uint* support,
int numOfPages,
int numOfFrequentItemsetsPreviousLevel,
int numOfCandidates,
int lenOfList,
int depth,
int depthMax) {
if (numOfCandidates == 0) {
return;
}
cudaError_t cudaStatus;
uint firstActiveBlock = 0;
// finding the necessary blocks
if (isMaterialization == true) {
map<uint, uint>::iterator blkIterator = materialization->rb_mgr->mapSingletonToBlkID.find(minSingleton);
firstActiveBlock = blkIterator->second;//this->minSingleton;
}
if(numOfCandidates < MAX_ITEMSET_PABUF) {
RUN_MAX_BLK = (numOfCandidates-1) / numOfGPUs + 1;
}
else {
RUN_MAX_BLK = MAX_ITEMSET_PABUF;
}
int numOfIAPs = (numOfCandidates - 1) / RUN_MAX_BLK + 1;
int IAPPos = 0;
bool inOuterLoop = true;
for(uint outerLoopIdx = 0; outerLoopIdx < numOfIAPs; outerLoopIdx += numOfGPUs)
{
// replicate one of outer join operand to each GPU
for(uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++) {
if ((outerLoopIdx + gpuIdx) > numOfIAPs) {
inOuterLoop = false;
break;
}
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device[" << gpuIdx << "]" << endl;
}
long long int idxSetStartOffset = ((long long int) outerLoopIdx + (long long int) gpuIdx) * (long long int) RUN_MAX_BLK * (long long int) MAX_DEPTH;
uint* IAPAddr = idxSet + idxSetStartOffset;
cudaStatus = cudaMemcpy( DM[gpuIdx].firstIdxSet,
IAPAddr,
sizeof(uint) * MAX_DEPTH * RUN_MAX_BLK,
cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in cudaMemcpy for firstIdxSet of the GPU device["<< gpuIdx << "]" << endl;
}
}
if(inOuterLoop == false) {
break;
}
// process an inner join operand
for(uint pageIdx = 0; pageIdx < numOfPages; pageIdx++)
{
// replicating an inner join operand
for (uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++)
{
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device[" << gpuIdx << "]" << endl;
}
uint streamOffset = pageIdx % numOfStreams;
if (isMaterialization == true) {
uint numOfpartialBitmap = numOfFrequentItemsetsPreviousLevel - (materialization->rb_mgr->numOfItemsetsMaterialized * firstActiveBlock );
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
sourceVector[pageIdx]->dataForPage + (firstActiveBlock * verticalListLength * materialization->rb_mgr->numOfItemsetsMaterialized),
sizeof(uint) * (numOfpartialBitmap) * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
uint batch = (RUN_MAX_BLK - 1) / MAX_GPU_BLOCK + 1;
for(uint loopIdx = 0; loopIdx < batch ; loopIdx++) {
Kernel_HFI<<< MAX_GPU_BLOCK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].resultVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depthMax,
((outerLoopIdx+gpuIdx) * RUN_MAX_BLK) + (loopIdx * MAX_GPU_BLOCK),
RUN_MAX_BLK
);
}
}
else {
cudaStatus = cudaMemcpyAsync( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
sourceVector[pageIdx]->dataForPage,
sizeof(uint) * numOfFrequentItemsetsPreviousLevel * lenOfList,
cudaMemcpyHostToDevice,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for sourceVerticalList" << endl;
}
uint batch = (RUN_MAX_BLK - 1) / MAX_GPU_BLOCK + 1;
for(uint loopIdx = 0; loopIdx < batch ; loopIdx++) {
Kernel_TFL<<<MAX_GPU_BLOCK, MAX_THREAD, 0, DM[gpuIdx].mPageStreams[streamOffset]>>>
( DM[gpuIdx].MiningBuffers[streamOffset].sourceVerticalList,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
DM[gpuIdx].firstIdxSet,
numOfCandidates,
lenOfList,
depth,
depthMax,
((outerLoopIdx+gpuIdx) * RUN_MAX_BLK) + (loopIdx * MAX_GPU_BLOCK),
RUN_MAX_BLK
);
}
}
// copying the supports of candidate itemsets to the main memory
uint* partialSupportArr = supportVector[pageIdx]->supportArr + (outerLoopIdx + gpuIdx) * RUN_MAX_BLK;
cudaStatus = cudaMemcpyAsync( partialSupportArr,
DM[gpuIdx].MiningBuffers[streamOffset].supportList,
sizeof(uint) * RUN_MAX_BLK,
cudaMemcpyDeviceToHost,
DM[gpuIdx].mPageStreams[streamOffset]);
if (cudaStatus != cudaSuccess) {
cerr << "supportCountingIndexDelivery():: Error in cudaMemcpyAsync for supportArr" << endl;
}
}
}
}
// Synchronizing all the GPUs
for (uint gpuIdx = 0; gpuIdx < numOfGPUs; gpuIdx++) {
// set the GPU device used at a time
cudaStatus = DM[gpuIdx].setDevice(gpuIdx);
if ( cudaStatus != cudaSuccess) {
cerr <<"supportCountingIndexDeliveryChangeOperands():: Error in setting the GPU device["<<gpuIdx<<"]"<<endl;
}
cudaStatus = cudaThreadSynchronize();
if(cudaStatus != cudaSuccess) {
cerr <<"supportCountingIndexDelivery():: Error in cudaThreadSynchronize()" << endl;
}
}
// Aggregating partial supports
for (int pageIdx = 0; pageIdx < numOfPages; pageIdx++) {
for (int candidateIdx = 0; candidateIdx < numOfCandidates; candidateIdx++) {
support[candidateIdx] += supportVector[pageIdx]->supportArr[candidateIdx];
}
}
}
// Release the buffers on the main memory
void destroyFramework() {
saVec->destroySAUnitVector();
cudaError_t cudaStatus;
if (wholeVerticalList) {
for (int idx = 0; idx < numOfPages; idx++) {
delete[] wholeVerticalList[idx];
wholeVerticalList[idx] = NULL;
}
delete[] wholeVerticalList;
wholeVerticalList = NULL;
}
if (idxSetArray) {
delete[] idxSetArray;
}
// the original TBP
if (initialPageset) {
initialPageset->destroyPageset();
delete initialPageset;
initialPageset = NULL;
}
if (supportArray) {
//delete[] supportArray;
cudaStatus = cudaFreeHost(supportArray);
if (cudaStatus != cudaSuccess) {
cerr
<< "destroyFramework():: Error in cudaFreeHost for supportArray"
<< endl;
}
supportArray = NULL;
}
if (isMaterialization == true) {
materialization->destroyMaterialization(DM);
}
if (DM) {
for (uint loopIdx = 0; loopIdx < numOfGPUs; loopIdx++) {
this->DM[loopIdx].setDevice(loopIdx);
this->DM[loopIdx].destroyStreams();
this->DM[loopIdx].cudaFreeMiningBuffers();
}
delete[] this->DM;
this->DM = NULL;
}
if (candidate_list) {
for (int depthIdx = 0; depthIdx < maxDepth; depthIdx++) {
delete[] candidate_list[depthIdx];
candidate_list[depthIdx] = NULL;
}
delete[] candidate_list;
candidate_list = NULL;
}
if (validate) {
delete[] validate;
validate = NULL;
}
if (p_boundary_list) {
delete p_boundary_list;
p_boundary_list = NULL;
}
if (new_candidate_list) {
for (int depthIdx = 0; depthIdx < maxDepth; depthIdx++) {
delete[] new_candidate_list[depthIdx];
new_candidate_list[depthIdx] = NULL;
}
delete[] new_candidate_list;
new_candidate_list = NULL;
}
if (new_p_boundary_list) {
delete new_p_boundary_list;
new_p_boundary_list = NULL;
}
if (cntArr) {
delete cntArr;
cntArr = NULL;
}
}
public:
// the variables on the main memory
PagesetTmp* initialPageset;
vector<vector<unsigned> > frequentItemsets;
uint** wholeVerticalList;
// the materialization for fast support counting
Materialization* materialization;
bool isMaterialization;
SAUnitVector* saVec;
int numOfTransactions;
int numOfPages;
int numOfStreams;
int numOfGPUs;
int numOfCandidates;
int verticalListLength;
int wholeVerticalListLength;
int minsupAbsoulte;
int level;
int numOfSingletons;
int numOfPrefixes;
uint* idxSetArray;
uint* supportArray;
int maxDepth;
map<int, int> mapFrequentSingletons;
// the variables on the device memory
//buffer on the device memory
DeviceMemoryForStream* DM;
map<uint, uint> mapSingletonIndex;
// the variables from GPApriori to generate candidate itemsets using the trie data structure
unsigned short** candidate_list;
unsigned short* validate;
vector<int>* p_boundary_list;
int candidate_list_length;
int candidate_list_width;
// the variables for the pingpong execution
unsigned short** new_candidate_list;
vector<int>* new_p_boundary_list;
// the variables for the parallelization of recordIndexes()
int numOfCPUs;
int* cntArr;
vector<vector<uint> > positionVector;
uint minSingleton;
bool isOutput;
ofstream fout;
void cleanWholeVerticalList() {
if (wholeVerticalList) {
for (int idx = 0; idx < numOfCandidates; idx++) {
delete[] wholeVerticalList[idx];
wholeVerticalList[idx] = NULL;
}
delete[] wholeVerticalList;
wholeVerticalList = NULL;
}
}
// returning true if two itemsets are joinable
bool joinable(vector<unsigned> left, vector<unsigned> right) {
int vecSiz = left.size() - 1;
for (int vecIdx = 0; vecIdx < vecSiz; vecIdx++) {
if (left[vecIdx] != right[vecIdx]) {
return false;
}
}
return true;
}
// pruning infrequent itemsets among candidate itemsets checked in the current iteration
int parallelPruningCandidates(unsigned short** _candidate_list) {
int i, j;
int candidate_itemset_cnt = 0;
int block_length = (candidate_list_length) / CPU_THREAD;
int nthreads, tid;
int startCandidateIdx, endCandidateIdx;
memset(cntArr, 0, sizeof(int) * (CPU_THREAD));
omp_set_num_threads(CPU_THREAD);
#pragma omp parallel private(i, nthreads, tid, startCandidateIdx, endCandidateIdx)
{
tid = omp_get_thread_num();