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kernel.cu
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#include <assert.h>
#include <cuda.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "ConstDefine.h"
#include "gpukernel.h"
//#include "util.h"
#include "cusparse.h"
extern std::vector<float> cpuonethreadtimes;
extern std::vector<float> cpumthreadtimes;
extern std::vector<float> gpucoarsetimes;
extern std::vector<float> gpufinetimes;
extern std::vector<float> gpuonefinetimes;
extern std::vector<float> gpufinenoFliptimes;
extern std::vector<float> gpufinenoSortingtimes;
//using namespace std;
//#define CUDA_CALL(x) { const cudaError_t a = (x); if (a!= cudaSuccess) { printf("\nCUDA Error: %s(err_num=%d)\n", cudaGetErrorString(a), a); cudaDeviceReset(); assert(0);}}
#define ERR_NE(X,Y) do { if ((X) != (Y)) { \
fprintf(stderr,"Error in %s at %s:%d\n",__func__,__FILE__,__LINE__); \
assert(-1);}} while(0)
#define CUDA_CALL(X) ERR_NE((X),cudaSuccess)
#define CUSPARSE_CALL(X) ERR_NE((X),CUSPARSE_STATUS_SUCCESS)
/*
GPU function definition.
All functions of GPU are defined here.
*/
__device__ inline float SSimGPU(float lat1, float lon1, float lat2, float lon2) {
//float Pi = 3.1415926;
//float R = 6371004;
float MLatA, MLatB, MLonA, MLonB;
MLatA = 90 - lat1;
MLatB = 90 - lat2;
MLonA = lon1;
MLonB = lon2;
float C = sin(MLatA)*sin(MLatB)*cos(MLonA - MLonB) + cos(MLatA)*cos(MLatB);
float Distance = 6371004 *acos(C)*3.1415926 / 180;
return (1 - Distance / MAX_DIST);
}
__device__ inline float TSimGPU(int* textDataIndexPi, int* textDataIndexQj, float* textDataValuePi, float* textDataValueQj,
int numWordP, int numWordQ){
// 单个thread：读取不平衡！！
// 内存读取不符合常规操作，无法合并，同时每个thread取的个数互不相同
// choice1: each time fetch
// choice2: reg. store!! register. booming ? max: 1502 min : 0 not recommended!!!
// Q: uint32_t 和 int 比较出错？？ ：注意size()返回值是uint32_t即可 粗暴全部换成int!!!
// may cause divergency may optimization Qj.keywordcnt=0??
// i think is okay!!
if (numWordP == 0 || numWordQ == 0) { return 0; }
float tsimresult = 0;
// if (numWordP > numWordQ) //each time fetch: no need !! divergency cache的存在 不确定的顺序！！
// calc tsim value
for (size_t m = 0; m < numWordP; m++) {
int tmpipim = textDataIndexPi[m]; // 编译器优化应该会有cache!! 不引入也一样？
float tmpvpim = textDataValuePi[m];
for (size_t n = 0; n < numWordQ; n++) {
if (tmpipim == textDataIndexQj[n]) {
tsimresult += tmpvpim*textDataValueQj[n];
break;// 单个点不会出现重复的keyword whether 优化 不确定？？ GPU可以识别break continue 不过就是指令而已
}
}
}
return tsimresult;
}
__device__ void warpReduce(volatile float* sdata,int tid ){
sdata[tid] += sdata[tid + 32];
sdata[tid] += sdata[tid + 16];
sdata[tid] += sdata[tid + 8];
sdata[tid] += sdata[tid + 4];
sdata[tid] += sdata[tid + 2];
sdata[tid] += sdata[tid + 1];
}
__global__ void computeSimGPU(float* latDataPGPU1, float* latDataQGPU1, float* lonDataPGPU1, float* lonDataQGPU1,
int* textDataPIndexGPU1, int* textDataQIndexGPU1, float* textDataPValueGPU1, float* textDataQValueGPU1,
int* textIdxPGPU1, int* textIdxQGPU1, int* numWordPGPU1, int* numWordQGPU1,
StatInfoTable* stattableGPU, float* keypmqnGPU, float* keypmqGPU, float* keypqGPU, float* SimResultGPU
) {
int bId = blockIdx.x;
int tId = threadIdx.x;
// 1-D 没采用2-D 可自定义存储方式
__shared__ float tmpSim[THREADNUM];
__shared__ float maxSimRow[MAXTRAJLEN];
__shared__ float maxSimColumn[MAXTRAJLEN];
//__shared__ int tid_row;
//__shared__ int tid_column;
__shared__ StatInfoTable task;
__shared__ int pointIdP, pointNumP, pointIdQ, pointNumQ;
//__shared__ size_t pmqnid, pmqid, pqid;
//__shared__ int keycntP, keycntQ, textPid, textQid;
// seems not important!
// merely for P-Q exchanging
__shared__ float *latDataPGPU, *latDataQGPU, *lonDataPGPU, *lonDataQGPU, *textDataPValueGPU, *textDataQValueGPU;
__shared__ int *textDataPIndexGPU, *textDataQIndexGPU, *textIdxPGPU, *textIdxQGPU, *numWordPGPU, *numWordQGPU;
//fetch task info
if (tId == 0) {
task = stattableGPU[bId];
latDataPGPU = latDataPGPU1;
latDataQGPU = latDataQGPU1;
lonDataPGPU = lonDataPGPU1;
lonDataQGPU = lonDataQGPU1;
textDataPIndexGPU = textDataPIndexGPU1;
textDataQIndexGPU = textDataQIndexGPU1;
textDataPValueGPU = textDataPValueGPU1;
textDataQValueGPU = textDataQValueGPU1;
textIdxPGPU = textIdxPGPU1;
textIdxQGPU = textIdxQGPU1;
numWordPGPU = numWordPGPU1;
numWordQGPU = numWordQGPU1;
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
//// not used in kernel-V1
//pmqnid = task.keywordpmqnMatrixId;
//pmqid = task.keywordpmqMatrixId;
//pqid = task.keywordpqMatrixId;
//keycntP = task.keycntP;
//keycntQ = task.keycntQ;
//textPid = task.textIdxP;
//textQid = task.textIdxQ;
}
/*
if (tId == 0) {
task = stattableGPU[bId];
// for cache task！
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
keycntP = task.keycntP;
keycntQ = task.keycntQ;
textPid = task.textIdxP;
textQid = task.textIdxQ;
// task.others have been processed in Host
pmqnid = task.keywordpmqnMatrixId;
pmqid = task.keywordpmqMatrixId;
pqid = task.keywordpqMatrixId;
if (pointNumP > pointNumQ) {
latDataPGPU = latDataPGPU1;
latDataQGPU = latDataQGPU1;
lonDataPGPU = lonDataPGPU1;
lonDataQGPU = lonDataQGPU1;
textDataPIndexGPU = textDataPIndexGPU1;
textDataQIndexGPU = textDataQIndexGPU1;
textDataPValueGPU = textDataPValueGPU1;
textDataQValueGPU = textDataQValueGPU1;
textIdxPGPU = textIdxPGPU1;
textIdxQGPU = textIdxQGPU1;
numWordPGPU = numWordPGPU1;
numWordQGPU = numWordQGPU1;
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
keycntP = task.keycntP;
keycntQ = task.keycntQ;
textPid = task.textIdxP;
textQid = task.textIdxQ;
}
else {
latDataQGPU = latDataPGPU1;
latDataPGPU = latDataQGPU1;
lonDataQGPU = lonDataPGPU1;
lonDataPGPU = lonDataQGPU1;
textDataQIndexGPU = textDataPIndexGPU1;
textDataPIndexGPU = textDataQIndexGPU1;
textDataQValueGPU = textDataPValueGPU1;
textDataPValueGPU = textDataQValueGPU1;
textIdxQGPU = textIdxPGPU1;
textIdxPGPU = textIdxQGPU1;
numWordQGPU = numWordPGPU1;
numWordPGPU = numWordQGPU1;
pointIdQ = task.latlonIdxP;
pointIdP = task.latlonIdxQ;
pointNumQ = task.pointNumP;
pointNumP = task.pointNumQ;
keycntQ = task.keycntP;
keycntP = task.keycntQ;
textQid = task.textIdxP;
textPid = task.textIdxQ;
}
}
*/
__syncthreads();
__shared__ int height, width;
// 不妨设 numP > numQ
// initialize maxSimRow maxSimColumn
/*
for (size_t i = 0; i < ((MAXTRAJLEN - 1) / THREADNUM) + 1; i++) {
maxSimRow[tId + i*THREADNUM] = 0;
maxSimColumn[tId + i*THREADNUM] = 0;
}
*/
/*
// STEP-0: GET the text-sim matrix(global memory)
// pmqn
height = keycntP, width = keycntQ;
for (size_t i = 0; i < keycntP; i += THREADROW) {
int tmpflagi = i + tId % THREADROW;
int pmindex, pmvalue;
if (tmpflagi < keycntP) {
pmindex = textDataPIndexGPU[textPid + tmpflagi];
pmvalue = textDataPValueGPU[textPid + tmpflagi];
}
for (size_t j = 0; j < keycntQ; j += THREADCOLUMN) {
int tmpflagj = j + tId / THREADROW;
int qnindex, qnvalue;
if (tmpflagj < keycntQ) {
qnindex = textDataQIndexGPU[textQid + tmpflagj];
qnvalue = textDataQValueGPU[textQid + tmpflagj];
}
// in such loop, can only index in this way!!
keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = 0;
if ((tmpflagi < keycntP) && (tmpflagj < keycntQ) && (pmindex == qnindex)) {
keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = pmvalue*qnvalue;
}
}
}
__syncthreads();
// pmq
// 16*16 方阵加速 -> 转置(~3x)
// __shared__ int pointnumq, textidq;
__shared__ float tmppmq[THREADROW2][THREADCOLUMN2];
height = keycntP, width = pointNumQ;
// two-layer loop similar to block-net
for (size_t i = 0; i < keycntP; i += THREADROW2) {
int tmpflagi = i + tId % THREADROW2;
int tmpflagi2 = i + tId / THREADROW2;
for (size_t j = 0; j < pointNumQ; j += THREADCOLUMN2) {
int tmpflagj = j + tId / THREADROW2;
int tmpflagj2 = j + tId % THREADROW2;
// similar to transpose
// tmppmq[tId / THREADCOLUMN2][tId % THREADCOLUMN2] = 0; // 行方式
tmppmq[tId % THREADROW2][tId / THREADROW2] = 0; // 列方式
if ((tmpflagi < keycntP) && (tmpflagj < pointNumQ)) { // thread filtering
int pointnumq, textidq;
pointnumq = numWordQGPU[pointIdQ + tmpflagj];
textidq = textIdxQGPU[pointIdQ + tmpflagj];
for (size_t k = 0; k < pointnumq; k++) {
// just (textidq + k) needs some effort
tmppmq[tId % THREADROW2][tId / THREADROW2] += keypmqnGPU[pmqnid + (textidq + k)*height + tmpflagi];
}
}
__syncthreads();
// bounding problem!
if ((tmpflagi2 < keycntP) && (tmpflagj2 < pointNumQ)) { // thread filtering
keypmqGPU[pmqid + tmpflagi2*width + tmpflagj2] = tmppmq[tId / THREADROW2][tId % THREADROW2];
}
}
}
// pq
height = pointNumQ, width = pointNumP;
for (size_t i = 0; i < pointNumQ; i += THREADROW2) {
int tmpflagi = i + tId%THREADROW2;
int tmpflagi2 = i + tId / THREADROW2;
for (size_t j = 0; j < pointNumP; j += THREADCOLUMN2) {
int tmpflagj = j + tId / THREADROW2;
int tmpflagj2 = j + tId % THREADROW2;
tmppmq[tId % THREADROW2][tId / THREADROW2] = 0;
if ((tmpflagi < pointNumQ) && (tmpflagj < pointNumP)) {
int pointnump, textidp;
pointnump = numWordPGPU[pointIdP + tmpflagj];
textidp = textIdxPGPU[pointIdP + tmpflagj];
for (size_t k = 0; k < pointnump; k++) {
tmppmq[tId % THREADROW2][tId / THREADROW2] += keypmqGPU[pqid + (textidp + k)*height + tmpflagi];
}
}
__syncthreads();
if ((tmpflagi2 < pointNumQ) && (tmpflagj2 < pointNumP)) {
keypqGPU[pqid + tmpflagi2*width + tmpflagj2] = tmppmq[tId / THREADROW2][tId % THREADROW2];
}
}
}
*/
// STEP-1: GET the final sim result: SimResultGPU
// 潜在debug:
// only correct when THREADNUM > MAXTRAJLEN;
// initilize shared memory
if (tId < MAXTRAJLEN) {
maxSimRow[tId] = 0;
maxSimColumn[tId] = 0;
}
__syncthreads();
height = pointNumP, width = pointNumQ;
// doesnot matter !!
for (size_t i = 0; i < pointNumP; i += THREADROW) {
// simply because of THREADROW = 32, THREADROW = 8, 32 > 8
// here 列方式
// not real 128 -> 32倍近似？？
// but there is cache ??
int tmpflagi = i + tId % THREADROW;
float latP, latQ, lonP, lonQ;
int textIdP, textIdQ, numWordP, numWordQ;
if (tmpflagi < pointNumP) {
latP = latDataPGPU[pointIdP + tmpflagi];
lonP = lonDataPGPU[pointIdP + tmpflagi];
textIdP = textIdxPGPU[pointIdP + tmpflagi];
numWordP = numWordPGPU[pointIdP + tmpflagi];
//printf("%f,%f \n", latP, lonP);
}
for (size_t j = 0; j < pointNumQ; j += THREADCOLUMN) {
int tmpflagj = j + tId / THREADROW;
if (tmpflagj < pointNumQ) {
latQ = latDataQGPU[pointIdQ + tmpflagj];
lonQ = lonDataQGPU[pointIdQ + tmpflagj];
textIdQ = textIdxQGPU[pointIdQ + tmpflagj];
numWordQ = numWordQGPU[pointIdQ + tmpflagj];
}
tmpSim[tId] = -1;//技巧，省去下面的tID=0判断
// debug: 边界条件错误！！ 逻辑错误 太慢！！ nearly 2 days
// if (tmpflagi && pointNumQ)
if ((tmpflagi < pointNumP) && (tmpflagj < pointNumQ)) { // bound condition
//// not recommended! divergency!!
//float tsim = 0;
//if (numWordP > numWordQ) {
//}
//else {
//}
float tsim = 0;
// way1: fool
tsim = TSimGPU(&textDataPIndexGPU[textIdP], &textDataQIndexGPU[textIdQ], &textDataPValueGPU[textIdP], &textDataQValueGPU[textIdQ], numWordP, numWordQ);
// way2: store way -> fetch way fetch from global memory!!
//tsim = keypqGPU[pqid + tmpflagj*height + tmpflagi];
float ssim = SSimGPU(latP, lonP, latQ, lonQ);
tmpSim[tId] = ALPHA * ssim + (1 - ALPHA) * tsim;
}
// else {
//
// }
// block 同步
// 很有必要
__syncthreads();
////
//// //优化
////if (tId == 0) {
//// tid_row = i + THREADROW > pointNumP ? pointNumP - i : THREADROW;
//// tid_column = j + THREADCOLUMN > pointNumP ? pointNumQ - j : THREADCOLUMN;
////}
////__syncthreads();
////
// ************--shared_mem process--************
// very naive process
// get tmp-row-max: full warp active
//tmpmaxsimRow[tId % THREADROW];
float tmpmaxSim = -1;
if (tId / THREADROW == 0) {
for (size_t k = 0; k < THREADCOLUMN; k++) {
if (tmpSim[k*THREADROW + tId] > tmpmaxSim) {
tmpmaxSim = tmpSim[k*THREADROW + tId];
}
}
maxSimRow[i + tId] = (maxSimRow[i + tId] > tmpmaxSim ? maxSimRow[i + tId] : tmpmaxSim);
}
__syncthreads(); // still need!
// get tmp-column-max: 1/32 warp active
//tmpmaxsimColumn[tId / THREADROW];
tmpmaxSim = -1;
if (tId%THREADROW == 0) {
for (size_t k = 0; k < THREADROW; k++) {
if (tmpSim[k + tId] > tmpmaxSim) {
tmpmaxSim = tmpSim[k + tId];
}
}
maxSimColumn[j + tId / THREADROW] = (maxSimColumn[j + tId / THREADROW] > tmpmaxSim ? maxSimColumn[j + tId / THREADROW] : tmpmaxSim);
}
__syncthreads(); // still need!
}
}
// sum reduction
// 潜在debug:
// 前提：
// THREADNUM > MAX-MAXTRAJLEN
//for (size_t activethread = THREADNUM / 2; activethread > 32; activethread >>= 1) {
for (size_t activethread = MAXTRAJLEN / 2; activethread > 32; activethread >>= 1) {
if (tId < activethread) {
maxSimRow[tId] += maxSimRow[tId + activethread];
__syncthreads();
}
}
if (tId < 32) warpReduce(maxSimRow, tId);
// }
//for (size_t activethread = THREADNUM / 2; activethread > 32; activethread >>= 1) {
for (size_t activethread = MAXTRAJLEN / 2; activethread > 32; activethread >>= 1) {
if (tId < activethread) {
maxSimColumn[tId] += maxSimColumn[tId + activethread];
__syncthreads();
}
}
if (tId < 32) warpReduce(maxSimColumn, tId);
//}
if (tId == 0) {
SimResultGPU[bId] = maxSimRow[0] / pointNumP + maxSimColumn[0] / pointNumQ;
}
}
/*****
computeTSimpmqn dependency:
|_textDataPIndexGPU,textDataQIndexGPU,textDataPValueGPU,textDataQValueGPU --->|
| |_textPid,textQid |
|_keypmqnGPU <----------------------------------------------------------------| this is pm-qn p-major
|_pmqnid
|_keycntP,keycntQ
****/
__global__ void computeTSimpmqn(float* latDataPGPU1, float* latDataQGPU1, float* lonDataPGPU1, float* lonDataQGPU1,
int* textDataPIndexGPU1, int* textDataQIndexGPU1, float* textDataPValueGPU1, float* textDataQValueGPU1,
int* textIdxPGPU1, int* textIdxQGPU1, int* numWordPGPU1, int* numWordQGPU1,
StatInfoTable* stattableGPU, float* keypmqnGPU, float* keypmqGPU, float* keypqGPU, float* SimResultGPU
) {
int bId = blockIdx.x; // bId is the only index for block to determine where to fetch data, and is 0 ~ MAX_BLOCKNUM-1
int tId = threadIdx.x;
// 1-D 没采用2-D 可自定义存储方式
__shared__ float tmpSim[THREADNUM];
__shared__ float maxSimRow[MAXTRAJLEN];
__shared__ float maxSimColumn[MAXTRAJLEN];
//__shared__ int tid_row;
//__shared__ int tid_column;
__shared__ StatInfoTable task;
__shared__ int pointIdP, pointNumP, pointIdQ, pointNumQ;
//debug: 数据类型 big int !! -> int , size_t
//__shared__ int pmqnid, pmqid, pqid;
__shared__ size_t pmqnid, pmqid, pqid;
__shared__ int keycntP, keycntQ, textPid, textQid;
// seems not important!
// merely for P-Q exchanging
__shared__ float *latDataPGPU, *latDataQGPU, *lonDataPGPU, *lonDataQGPU, *textDataPValueGPU, *textDataQValueGPU;
__shared__ int *textDataPIndexGPU, *textDataQIndexGPU, *textIdxPGPU, *textIdxQGPU, *numWordPGPU, *numWordQGPU;
//fetch task info
if (tId == 0) {
task = stattableGPU[bId];
latDataPGPU = latDataPGPU1;
latDataQGPU = latDataQGPU1;
lonDataPGPU = lonDataPGPU1;
lonDataQGPU = lonDataQGPU1;
textDataPIndexGPU = textDataPIndexGPU1;
textDataQIndexGPU = textDataQIndexGPU1;
textDataPValueGPU = textDataPValueGPU1;
textDataQValueGPU = textDataQValueGPU1;
textIdxPGPU = textIdxPGPU1;
textIdxQGPU = textIdxQGPU1;
numWordPGPU = numWordPGPU1;
numWordQGPU = numWordQGPU1;
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
// debug: wrong silly code mistake!
//pmqnid = task.keywordpmqMatrixId;
//pmqid = task.keywordpmqnMatrixId;
pmqnid = task.keywordpmqnMatrixId; // starting ID in GPU for each block, accumulated
pmqid = task.keywordpmqMatrixId;
pqid = task.keywordpqMatrixId;
keycntP = task.keycntP;
keycntQ = task.keycntQ;
textPid = task.textIdxP; // starting position of text data for each task / block, accumulated
textQid = task.textIdxQ;
}
__syncthreads();
// STEP-0: GET the text-sim matrix(global memory)
__shared__ int height, width;
// pmqn
// keycntP including all the padding
height = keycntP, width = keycntQ;
for (size_t i = 0; i < keycntP; i += THREADROW) {
int tmpflagi = i + tId % THREADROW;
//debug: float -> int 精度问题 数据类型定义出错
// int pmindex,pmvalue;
int pmindex;
float pmvalue;
//if (tmpflagi < keycntP) {
// pmindex = textDataPIndexGPU[textPid + tmpflagi];
// //if (pmindex == -1) continue;
// pmvalue = textDataPValueGPU[textPid + tmpflagi];
//}
for (size_t j = 0; j < keycntQ; j += THREADCOLUMN) {
int tmpflagj = j + tId / THREADROW;
int qnindex;
float qnvalue;
//if (tmpflagj < keycntQ) {
// qnindex = textDataQIndexGPU[textQid + tmpflagj];
// //if (qnindex == -1) continue;
// qnvalue = textDataQValueGPU[textQid + tmpflagj];
//}
// in such loop, can only index in this way!!
// int -> size_t 兼容
// debug: initialize:overlap among blocks
//keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = 0;
if ((tmpflagi < keycntP) && (tmpflagj < keycntQ)) { // avoid overlapping of keypmqnGPU among blocks !!
pmindex = textDataPIndexGPU[textPid + tmpflagi];
//if (pmindex == -1) continue;
pmvalue = textDataPValueGPU[textPid + tmpflagi];
qnindex = textDataQIndexGPU[textQid + tmpflagj];
//if (qnindex == -1) continue;
qnvalue = textDataQValueGPU[textQid + tmpflagj];
keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = 0;
// debug: excluding padding here!
if ((pmindex != -1) && (qnindex != -1) && (pmindex == qnindex)) {
keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = pmvalue*qnvalue;
//printf("pmqn-> blockId:%d threadId:%d startpos:%d index:%zu value:%.5f\n", bId, tId, pmqnid, pmqnid + tmpflagj*height + tmpflagi, pmvalue*qnvalue);
//printf("pmqn s1 -> blockId:%d threadId:%d startpos:%d value:%.5f\n", bId, tId, pmqnid, pmvalue*qnvalue);
}
//printf("pmqn confirm -> blockId:%d threadId:%d startpos:%d value:%.5f\n", bId, tId, pmqnid, keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi]);
}
/*
// debug: excluding padding here!
if ((pmindex != -1) && (qnindex != -1) && (tmpflagi < keycntP) && (tmpflagj < keycntQ) && (pmindex == qnindex)) {
keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi] = pmvalue*qnvalue;
//printf("pmqn-> blockId:%d threadId:%d startpos:%d index:%zu value:%.5f\n", bId, tId, pmqnid, pmqnid + tmpflagj*height + tmpflagi, pmvalue*qnvalue);
//printf("pmqn-> blockId:%d threadId:%d startpos:%d value:%.5f\n", bId, tId, pmqnid, pmvalue*qnvalue);
}
*/
// block同步！ maybe not necessary because no overlap of memory write and read here, is register reused? 决定是否需要同步
__syncthreads();
}
}
}
__global__ void computeTSimpmqnGridlevel(int* textDataPIndexGPU, int* textDataQIndexGPU, float* textDataPValueGPU, float* textDataQValueGPU,
int textPid, int textQid, int keycntP, int keycntQ, float* tmpdensepmqnGPU
) {
const unsigned int idx = (blockIdx.x*blockDim.x) + threadIdx.x;
const unsigned int idy = (blockIdx.y*blockDim.y) + threadIdx.y;
//debug:有问题 Gridlevel传入dinm3时存在padding
//const unsigned int thread_id = ((gridDim.x*blockDim.x)*idy) + idx;
const unsigned int thread_id = ((keycntP)*idy) + idx;
int pmindex, qnindex;
float pmvalue, qnvalue;
if ((idx < keycntP) && (idy < keycntQ)) { // filtering threads
pmindex = textDataPIndexGPU[textPid + idx];
pmvalue = textDataPValueGPU[textPid + idx];
qnindex = textDataQIndexGPU[textQid + idy];
qnvalue = textDataQValueGPU[textQid + idy];
tmpdensepmqnGPU[thread_id] = (float)0.0;
if ((pmindex != -1) && (qnindex != -1) && (pmindex == qnindex)) {
tmpdensepmqnGPU[thread_id] = pmvalue*qnvalue;
//printf("tmpdensepmqnGPU[%d] = %f\n", thread_id, pmvalue*qnvalue);
}
}
}
/******
computeTSimpmq dependency
|_keypmqnGPU ---------------------->| this is pm-qn p-major
| |_pmqnid |
| |_numWordQGPU,textIdxQGPU |
| |_pointIdQ |
|_keypmqGPU <----------------------| this is q-pm q-major
|_pmqid
|_keycntP,pointNumQ
*******/
__global__ void computeTSimpmq(float* latDataPGPU1, float* latDataQGPU1, float* lonDataPGPU1, float* lonDataQGPU1,
int* textDataPIndexGPU1, int* textDataQIndexGPU1, float* textDataPValueGPU1, float* textDataQValueGPU1,
int* textIdxPGPU1, int* textIdxQGPU1, int* numWordPGPU1, int* numWordQGPU1,
StatInfoTable* stattableGPU, float* keypmqnGPU, float* keypmqGPU, float* keypqGPU, float* SimResultGPU
) {
int bId = blockIdx.x;
int tId = threadIdx.x;
// 1-D 没采用2-D 可自定义存储方式
__shared__ float tmpSim[THREADNUM];
__shared__ float maxSimRow[MAXTRAJLEN];
__shared__ float maxSimColumn[MAXTRAJLEN];
//__shared__ int tid_row;
//__shared__ int tid_column;
__shared__ StatInfoTable task;
__shared__ int pointIdP, pointNumP, pointIdQ, pointNumQ;
__shared__ size_t pmqnid, pmqid, pqid;
__shared__ int keycntP, keycntQ, textPid, textQid;
// seems not important!
// merely for P-Q exchanging
__shared__ float *latDataPGPU, *latDataQGPU, *lonDataPGPU, *lonDataQGPU, *textDataPValueGPU, *textDataQValueGPU;
__shared__ int *textDataPIndexGPU, *textDataQIndexGPU, *textIdxPGPU, *textIdxQGPU, *numWordPGPU, *numWordQGPU;
//fetch task info
if (tId == 0) {
task = stattableGPU[bId];
latDataPGPU = latDataPGPU1;
latDataQGPU = latDataQGPU1;
lonDataPGPU = lonDataPGPU1;
lonDataQGPU = lonDataQGPU1;
textDataPIndexGPU = textDataPIndexGPU1;
textDataQIndexGPU = textDataQIndexGPU1;
textDataPValueGPU = textDataPValueGPU1;
textDataQValueGPU = textDataQValueGPU1;
textIdxPGPU = textIdxPGPU1;
textIdxQGPU = textIdxQGPU1;
numWordPGPU = numWordPGPU1;
numWordQGPU = numWordQGPU1;
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
// debug: wrong silly code mistake!
//pmqnid = task.keywordpmqMatrixId;
//pmqid = task.keywordpmqnMatrixId;
pmqnid = task.keywordpmqnMatrixId;
pmqid = task.keywordpmqMatrixId;
pqid = task.keywordpqMatrixId;
keycntP = task.keycntP;
keycntQ = task.keycntQ;
textPid = task.textIdxP;
textQid = task.textIdxQ;
}
__syncthreads();
// STEP-0: GET the text-sim matrix(global memory)
__shared__ int height, width;
// check pmqnMatrix
//height = keycntP, width = keycntQ;
//for (size_t i = 0; i < keycntP; i += THREADROW) {
// int tmpflagi = i + tId % THREADROW;
// for (size_t j = 0; j < keycntQ; j += THREADCOLUMN) {
// int tmpflagj = j + tId / THREADROW;
// if ((tmpflagi < keycntP) && (tmpflagj < keycntQ)) {
// printf("pmqn check -> blockId:%d threadId:%d startpos:%d value:%.5f\n", bId, tId, pmqnid, keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi]);
// }
// }
//}
// pmq
// 16*16 方阵加速 -> 转置(~3x)
// __shared__ int pointnumq, textidq;
__shared__ float tmppmq[THREADROW2][THREADCOLUMN2];
height = keycntP, width = pointNumQ;
// two-layer loop similar to block-net
for (size_t i = 0; i < keycntP; i += THREADROW2) {
int tmpflagi = i + tId % THREADROW2;
int tmpflagi2 = i + tId / THREADROW2;
for (size_t j = 0; j < pointNumQ; j += THREADCOLUMN2) {
int tmpflagj = j + tId / THREADROW2;
int tmpflagj2 = j + tId % THREADROW2;
// similar to transpose
// tmppmq[tId / THREADCOLUMN2][tId % THREADCOLUMN2] = 0; // 行方式
// initialization of shared mem: tmppmq, must be here, or we have uninitialized tmppmq
tmppmq[tId % THREADROW2][tId / THREADROW2] = 0; // 列方式
if ((tmpflagi < keycntP) && (tmpflagj < pointNumQ)) { // thread filtering
int keywordnumq, textidq;
// ABOUT PADDING problem:
keywordnumq = numWordQGPU[pointIdQ + tmpflagj]; // this is real # of keyword for each point without padding!
textidq = textIdxQGPU[pointIdQ + tmpflagj]; // this is the keyword starting id for each point after padding, be careful!
// but attention: the padding is traj-level, so the padding is always patched to the last point!!
// as long as the textidq and textQid accordant! as they make subtraction!
for (size_t k = 0; k < keywordnumq; k++) {
// just (textidq + k) needs some effort
//if (bId == 60){
// printf("************ special pmq-> k:%d blockId:%d threadId:%d value:%0.5f\n", k, bId, tId, keypmqnGPU[pmqnid + (textidq + k)*height + tmpflagi]);
//}
// debug: fecthing wrong keypmqnGPU here! data structure!
tmppmq[tId % THREADROW2][tId / THREADROW2] += keypmqnGPU[pmqnid + (textidq - textQid + k)*height + tmpflagi];
//tmppmq[tId % THREADROW2][tId / THREADROW2] += keypmqnGPU[pmqnid + (textidq + k)*height + tmpflagi];
//if (bId == 60){
// printf("************ special pmq-> k:%d blockId:%d threadId:%d value:%0.5f\n", k, bId, tId, keypmqnGPU[pmqnid + (textidq + k)*height + tmpflagi]);
//}
}
//printf("pmq s1 -> blockId:%d threadId:%d keywordnumq:%d textidq:%d xindex:%d yindex:%d value:%.5f\n", bId, tId, keywordnumq, textidq, tId%THREADROW2, tId / THREADROW2, tmppmq[tId % THREADROW2][tId / THREADROW2]);
}
// this is necessary ! because of tmppmq;
__syncthreads();
// this is not a propriate place for printf as no thread filtering
//printf("pmq-> blockId:%d threadId:%d xindex:%d yindex:%d value:%.5f\n", bId, tId, tId%THREADROW2, tId / THREADROW2, tmppmq[tId % THREADROW2][tId / THREADROW2]);
// bounding problem!
if ((tmpflagi2 < keycntP) && (tmpflagj2 < pointNumQ)) { // thread filtering
keypmqGPU[pmqid + tmpflagi2*width + tmpflagj2] = tmppmq[tId / THREADROW2][tId % THREADROW2];
//printf("pmq s2-> blockId:%d threadId:%d xindex:%d yindex:%d value:%.5f\n", bId, tId, tId / THREADROW2, tId % THREADROW2, tmppmq[tId / THREADROW2][tId % THREADROW2]);
}
// this is necessary ! because of tmppmq;
__syncthreads();
}
}
}
__global__ void computeTSimpmqNoF(float* latDataPGPU1, float* latDataQGPU1, float* lonDataPGPU1, float* lonDataQGPU1,
int* textDataPIndexGPU1, int* textDataQIndexGPU1, float* textDataPValueGPU1, float* textDataQValueGPU1,
int* textIdxPGPU1, int* textIdxQGPU1, int* numWordPGPU1, int* numWordQGPU1,
StatInfoTable* stattableGPU, float* keypmqnGPU, float* keypmqGPU, float* keypqGPU, float* SimResultGPU
) {
int bId = blockIdx.x;
int tId = threadIdx.x;
// 1-D 没采用2-D 可自定义存储方式
__shared__ float tmpSim[THREADNUM];
__shared__ float maxSimRow[MAXTRAJLEN];
__shared__ float maxSimColumn[MAXTRAJLEN];
//__shared__ int tid_row;
//__shared__ int tid_column;
__shared__ StatInfoTable task;
__shared__ int pointIdP, pointNumP, pointIdQ, pointNumQ;
__shared__ size_t pmqnid, pmqid, pqid;
__shared__ int keycntP, keycntQ, textPid, textQid;
// seems not important!
// merely for P-Q exchanging
__shared__ float *latDataPGPU, *latDataQGPU, *lonDataPGPU, *lonDataQGPU, *textDataPValueGPU, *textDataQValueGPU;
__shared__ int *textDataPIndexGPU, *textDataQIndexGPU, *textIdxPGPU, *textIdxQGPU, *numWordPGPU, *numWordQGPU;
//fetch task info
if (tId == 0) {
task = stattableGPU[bId];
latDataPGPU = latDataPGPU1;
latDataQGPU = latDataQGPU1;
lonDataPGPU = lonDataPGPU1;
lonDataQGPU = lonDataQGPU1;
textDataPIndexGPU = textDataPIndexGPU1;
textDataQIndexGPU = textDataQIndexGPU1;
textDataPValueGPU = textDataPValueGPU1;
textDataQValueGPU = textDataQValueGPU1;
textIdxPGPU = textIdxPGPU1;
textIdxQGPU = textIdxQGPU1;
numWordPGPU = numWordPGPU1;
numWordQGPU = numWordQGPU1;
pointIdP = task.latlonIdxP;
pointIdQ = task.latlonIdxQ;
pointNumP = task.pointNumP;
pointNumQ = task.pointNumQ;
// debug: wrong silly code mistake!
//pmqnid = task.keywordpmqMatrixId;
//pmqid = task.keywordpmqnMatrixId;
pmqnid = task.keywordpmqnMatrixId;
pmqid = task.keywordpmqMatrixId;
pqid = task.keywordpqMatrixId;
keycntP = task.keycntP;
keycntQ = task.keycntQ;
textPid = task.textIdxP;
textQid = task.textIdxQ;
}
__syncthreads();
// STEP-0: GET the text-sim matrix(global memory)
__shared__ int height, width;
// check pmqnMatrix
//height = keycntP, width = keycntQ;
//for (size_t i = 0; i < keycntP; i += THREADROW) {
// int tmpflagi = i + tId % THREADROW;
// for (size_t j = 0; j < keycntQ; j += THREADCOLUMN) {
// int tmpflagj = j + tId / THREADROW;
// if ((tmpflagi < keycntP) && (tmpflagj < keycntQ)) {
// printf("pmqn check -> blockId:%d threadId:%d startpos:%d value:%.5f\n", bId, tId, pmqnid, keypmqnGPU[pmqnid + tmpflagj*height + tmpflagi]);
// }
// }
//}
// pmq
// 16*16 方阵加速 -> 转置(~3x)
// __shared__ int pointnumq, textidq;
//__shared__ float tmppmq[THREADROW2][THREADCOLUMN2];
height = keycntP, width = pointNumQ;
// two-layer loop similar to block-net
for (size_t i = 0; i < keycntP; i += THREADROW2) {
int tmpflagi = i + tId % THREADROW2;
int tmpflagi2 = i + tId / THREADROW2;
for (size_t j = 0; j < pointNumQ; j += THREADCOLUMN2) {
int tmpflagj = j + tId / THREADROW2;
int tmpflagj2 = j + tId % THREADROW2;
// similar to transpose
// tmppmq[tId / THREADCOLUMN2][tId % THREADCOLUMN2] = 0; // 行方式
// initialization of shared mem: tmppmq, must be here, or we have uninitialized tmppmq
//tmppmq[tId % THREADROW2][tId / THREADROW2] = 0; // 列方式
if ((tmpflagi < keycntP) && (tmpflagj < pointNumQ)) { // thread filtering
int keywordnumq, textidq;
// ABOUT PADDING problem:
keywordnumq = numWordQGPU[pointIdQ + tmpflagj]; // this is real # of keyword for each point without padding!
textidq = textIdxQGPU[pointIdQ + tmpflagj]; // this is the keyword starting id for each point after padding, be careful!
// but attention: the padding is traj-level, so the padding is always patched to the last point!!
// as long as the textidq and textQid accordant! as they make subtraction!
for (size_t k = 0; k < keywordnumq; k++) {
// just (textidq + k) needs some effort
//if (bId == 60){
// printf("************ special pmq-> k:%d blockId:%d threadId:%d value:%0.5f\n", k, bId, tId, keypmqnGPU[pmqnid + (textidq + k)*height + tmpflagi]);
//}
// debug: fecthing wrong keypmqnGPU here! data structure!
//tmppmq[tId % THREADROW2][tId / THREADROW2] += keypmqnGPU[pmqnid + (textidq - textQid + k)*height + tmpflagi];