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turtle-sort.cu
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turtle-sort.cu
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#include"turtle-sort.cuh"
namespace Turtle
{
// maximum number of elements going into brute-force chunks
constexpr int BRUTE_FORCE_LIMIT = 128;
// maximum number of elements directly computed without any task mechanics
// minimum allowed: 32 due to some warp-level computations
// maximum allowed: BRUTE_FORCE_LIMIT - 1
constexpr int DIRECTLY_COMPUTE_LIMIT = 32;
// task pattern:
// task 0 task 1 task 2 task 3 ---> array chunks to sort (no overlap)
// start stop start stop start stop start stop ---> tasks buffer
// block 0 block 1 block 2 block 3 ---> cuda blocks
template<typename Type>
__global__ void quickSortWithoutStreamCompaction(
const bool trackIdValues,
Type* __restrict__ arr, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
Type* __restrict__ arrTmp, int* __restrict__ idArrTmp);
template<typename Type>
__global__ void bruteSort(
const bool trackIdValues,
Type* __restrict__ arr, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
Type* __restrict__ arrTmp, int* __restrict__ idArrTmp);
template<typename Type>
__global__ void resetNumTasks(
const bool trackIdValues,
Type* __restrict__ data, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
Type* __restrict__ arrTmp, int* __restrict__ idArrTmp)
{
int numTask1 = 0;
int numTask2 = 0;
if (threadIdx.x == 0)
{
numTask1 = numTasks[0];
numTasks[2] = numTask1;
numTask2 = numTasks[1];
numTasks[3] = numTask2;
numTasks[0] = 0;
numTasks[1] = 0;
__syncthreads();
if (numTasks[3] > 0)
bruteSort << <numTasks[3], BRUTE_FORCE_LIMIT, 0, cudaStreamFireAndForget >> > (trackIdValues, data, numTasks, tasks, tasks2, tasks3, tasks4, idData, arrTmp, idArrTmp);
if (numTasks[2] > 0)
quickSortWithoutStreamCompaction << <numTasks[2], 1024, 0, cudaStreamTailLaunch >> > (trackIdValues, data, numTasks, tasks, tasks2, tasks3, tasks4, idData, arrTmp, idArrTmp);
}
}
template<typename Type>
__global__ void copyTasksBack(
const bool trackIdValues, Type* __restrict__ data, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
Type* __restrict__ arrTmp, int* __restrict__ idArrTmp)
{
const int id = threadIdx.x;
const int n = numTasks[0];
const int n2 = numTasks[1];
const int steps = 1 + n / 1024;
const int steps2 = 1 + n2 / 1024;
// make quick-sort tasks usable
for (int i = 0; i < steps; i++)
{
const int curId = id + i * 1024;
if (curId < n)
{
tasks[curId * 2] = tasks2[curId * 2];
tasks[curId * 2 + 1] = tasks2[curId * 2 + 1];
}
}
// make brute-force tasks usable
for (int i = 0; i < steps2; i++)
{
const int curId = id + i * 1024;
if (curId < n2)
{
tasks3[curId * 2] = tasks4[curId * 2];
tasks3[curId * 2 + 1] = tasks4[curId * 2 + 1];
}
}
if (id == 0)
{
resetNumTasks << <1, 1, 0, cudaStreamTailLaunch >> > (
trackIdValues, data, numTasks, tasks, tasks2, tasks3, tasks4, idData, arrTmp, idArrTmp);
}
}
#define compareSwap(a,x,y) if(a[y]<a[x]){auto t = a[x];a[x]=a[y];a[y]=t;}
#define compSw(a,x,y,b) if(a[y]<a[x]){ auto t = a[x];a[x]=a[y];a[y]=t; auto u = b[x];b[x]=b[y];b[y]=u;}
template<typename Type>
__global__ void bruteSort(
const bool trackIdValues,
Type* __restrict__ arr, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idArr,
Type* __restrict__ arrTmp, int* __restrict__ idArrTmp)
{
const int id = threadIdx.x;
const int gid = blockIdx.x;
__shared__ int taskIdCacheStart;
__shared__ int taskIdCacheStop;
if (id == 0)
{
taskIdCacheStart = tasks3[gid * 2];
taskIdCacheStop = tasks3[gid * 2 + 1];
tasks3[gid * 2] = 0;
tasks3[gid * 2 + 1] = 0;
}
__syncthreads();
const int startIncluded = taskIdCacheStart;
const int stopIncluded = taskIdCacheStop;
const int num = stopIncluded - startIncluded + 1;
if (startIncluded == 0 && stopIncluded == 0)
{
if (id == 0)
printf("\n brute-force task id error: %i \n", gid);
return;
}
__shared__ Type cache[BRUTE_FORCE_LIMIT];
__shared__ int idTracker[BRUTE_FORCE_LIMIT];
if (startIncluded + id <= stopIncluded)
{
cache[id] = arr[startIncluded + id];
if(trackIdValues)
idTracker[id] = idArr[startIncluded + id];
}
__syncthreads();
for (int i = 0; i < num; i++)
{
if (id + 1 < num)
{
if ((id % 2 == 0))
{
compSw(cache, id, id + 1, idTracker);
}
}
__syncthreads();
if (id + 1 < num)
{
if ((id % 2 == 1))
{
compSw(cache, id, id + 1, idTracker);
}
}
__syncthreads();
}
if (startIncluded + id <= stopIncluded)
{
arr[startIncluded + id] = cache[id];
if (trackIdValues)
idArr[startIncluded + id] = idTracker[id];
}
}
// call this log2n times while doubling size on each call
// merge 2 chunks into single array
// single block or even single
template<typename Type>
__global__ void mergeSortedChunks(
const bool trackIdValues,
int* __restrict__ tasks,
Type* __restrict__ arr, Type* __restrict__ arrTmp,
int* __restrict__ idArr, int* __restrict__ idArrTmp
)
{
const int id = threadIdx.x + blockIdx.x * blockDim.x;
// inclusive values
const int startChunk1 = tasks[0];
const int stopChunk1 = tasks[1];
const int startChunk2 = tasks[2];
const int stopChunk2 = tasks[3];
const int sizeChunk1 = stopChunk1 - startChunk1 + 1;
const int sizeChunk2 = stopChunk2 - startChunk2 + 1;
// binary-search
if(id< sizeChunk2)
{
Type val = arr[startChunk2+id];
int idTracked = 0;
if (trackIdValues)
idTracked = arr[startChunk2 + id];
int l = startChunk1;
int r = stopChunk1;
int m = (r - l) / 2 + l;
while (r >= l)
{
// if bigger, go right
if (!(val < arr[m]))
{
l = m + 1;
}
else
{
r = m - 1;
}
m = (r - l) / 2 + l;
}
arrTmp[m + id] = val;
if(trackIdValues)
idArrTmp[m + id] = idTracked;
}
if(id<sizeChunk1)
{
Type val = arr[startChunk1 + id];
int idTracked = 0;
if (trackIdValues)
idTracked = arr[startChunk1 + id];
int l = startChunk2;
int r = stopChunk2;
int m = (r - l) / 2 + l;
while (r >= l)
{
// if bigger, go right
if (val > arr[m])
{
l = m + 1;
}
else
{
r = m - 1;
}
m = (r - l) / 2 + l;
}
arrTmp[m + id - startChunk2 + startChunk1] = val;
if (trackIdValues)
idArrTmp[m + id - startChunk2 + startChunk1] = idTracked;
}
}
template<typename Type>
__global__ void copyMergedChunkBack(
const bool trackIdValues,
const int n,
Type* __restrict__ arr, Type* __restrict__ arrTmp,
int* __restrict__ idArr, int* __restrict__ idArrTmp
)
{
const int id = threadIdx.x + blockIdx.x * blockDim.x;
if (id < n)
{
arr[id] = arrTmp[id];
if(trackIdValues)
idArr[id] = idArrTmp[id];
}
}
__device__ void reductionWithBlock7x(
const int count, int* countCache,
const int count2, int* countCache2,
const int count3, int* countCache3,
const int count4, int* countCache4,
const int count5, int* countCache5,
const int count6, int* countCache6,
const int count7, int* countCache7,
const int id, const int bd,
int* output, int* output2,
int* output3, int* output4,
int* output5, int* output6,
int* output7)
{
int wSum = count;
int wSum2 = count2;
int wSum3 = count3;
int wSum4 = count4;
int wSum5 = count5;
int wSum6 = count6;
int wSum7 = count7;
for (unsigned int wOfs = 16; wOfs > 0; wOfs >>= 1)
{
wSum += __shfl_down_sync(0xffffffff, wSum, wOfs);
wSum2 += __shfl_down_sync(0xffffffff, wSum2, wOfs);
wSum3 += __shfl_down_sync(0xffffffff, wSum3, wOfs);
wSum4 += __shfl_down_sync(0xffffffff, wSum4, wOfs);
wSum5 += __shfl_down_sync(0xffffffff, wSum5, wOfs);
wSum6 += __shfl_down_sync(0xffffffff, wSum6, wOfs);
wSum7 += __shfl_down_sync(0xffffffff, wSum7, wOfs);
}
__syncthreads();
if (id % 32 == 0)
{
countCache[id / 32] = wSum;
countCache2[id / 32] = wSum2;
countCache3[id / 32] = wSum3;
countCache4[id / 32] = wSum4;
countCache5[id / 32] = wSum5;
countCache6[id / 32] = wSum6;
countCache7[id / 32] = wSum7;
}
__syncthreads();
if (id < 32)
{
wSum = countCache[id];
wSum2 = countCache2[id];
wSum3 = countCache3[id];
wSum4 = countCache4[id];
wSum5 = countCache5[id];
wSum6 = countCache6[id];
wSum7 = countCache7[id];
for (unsigned int wOfs = 16; wOfs > 0; wOfs >>= 1)
{
wSum += __shfl_down_sync(0xffffffff, wSum, wOfs);
wSum2 += __shfl_down_sync(0xffffffff, wSum2, wOfs);
wSum3 += __shfl_down_sync(0xffffffff, wSum3, wOfs);
wSum4 += __shfl_down_sync(0xffffffff, wSum4, wOfs);
wSum5 += __shfl_down_sync(0xffffffff, wSum5, wOfs);
wSum6 += __shfl_down_sync(0xffffffff, wSum6, wOfs);
wSum7 += __shfl_down_sync(0xffffffff, wSum7, wOfs);
}
}
__syncthreads();
if (id == 0)
{
countCache[0] = wSum;
countCache2[0] = wSum2;
countCache3[0] = wSum3;
countCache4[0] = wSum4;
countCache5[0] = wSum5;
countCache6[0] = wSum6;
countCache7[0] = wSum7;
}
__syncthreads();
*output = countCache[0];
*output2 = countCache2[0];
*output3 = countCache3[0];
*output4 = countCache4[0];
*output5 = countCache5[0];
*output6 = countCache6[0];
*output7 = countCache7[0];
return;
}
// all sorting networks taken from: https://bertdobbelaere.github.io/sorting_networks.html#N32L185D14
// run by single thread
__constant__ int sortingNetwork4[5][2] = {
{0,2},{1,3 },
{0,1},{2,3 },
{1,2}
};
__constant__ int snCols4[3] = { 2,2,1 };
// run by warp
__constant__ int sortingNetwork8[19][2] = {
{0,2},{1,3},{4,6},{5,7},
{0,4},{1,5},{2,6},{3,7},
{0,1},{2,3},{4,5},{6,7},
{2,4},{3,5},
{1,4},{3,6},
{1,2},{3,4},{5,6 }
};
__constant__ int snCols8[6] = { 4,4,4,2,2,3 };
// run by warp
__constant__ int sortingNetwork16[60][2] = {
{0,13},{1,12},{2,15},{3,14},{4,8},{5,6},{7,11},{9,10 },
{0,5},{1,7},{2,9},{3,4},{6,13},{8,14},{10,15},{11,12 },
{0,1},{2,3},{4,5},{6,8},{7,9},{10,11},{12,13},{14,15 },
{0,2},{1,3},{4,10},{5,11},{6,7},{8,9},{12,14},{13,15 },
{1,2},{3,12},{4,6},{5,7},{8,10},{9,11},{13,14 },
{1,4},{2,6},{5,8},{7,10},{9,13},{11,14 },
{2,4},{3,6},{9,12},{11,13 },
{3,5},{6,8},{7,9},{10,12 },
{3,4},{5,6},{7,8},{9,10},{11,12 },
{6,7},{8,9}
};
__constant__ int snCols16[10] = {8,8,8,8,7,6,4,4,5,2};
// should fit inside constant cache
// all sorting networks taken from: https://bertdobbelaere.github.io/sorting_networks.html#N32L185D14
__constant__ int sortingNetwork32[185][2] = {
{0,1},{2,3},{4,5},{6,7},{8,9},{10,11},{12,13},{14,15},{16,17},{18,19},{20,21},{22,23},{24,25},{26,27},{28,29},{30,31},
{0,2},{1,3},{4,6},{5,7},{8,10},{9,11},{12,14},{13,15},{16,18},{17,19},{20,22},{21,23},{24,26},{25,27},{28,30},{29,31},
{0,4},{1,5},{2,6},{3,7},{8,12},{9,13},{10,14},{11,15},{16,20},{17,21},{18,22},{19,23},{24,28},{25,29},{26,30},{27,31},
{0,8},{1,9},{2,10},{3,11},{4,12},{5,13},{6,14},{7,15},{16,24},{17,25},{18,26},{19,27},{20,28},{21,29},{22,30},{23,31},
{0,16},{1,8},{2,4},{3,12},{5,10},{6,9},{7,14},{11,13},{15,31},{17,24},{18,20},{19,28},{21,26},{22,25},{23,30},{27,29},
{1,2},{3,5},{4,8},{6,22},{7,11},{9,25},{10,12},{13,14},{17,18},{19,21},{20,24},{23,27},{26,28},{29,30},
{1,17},{2,18},{3,19},{4,20},{5,10},{7,23},{8,24},{11,27},{12,28},{13,29},{14,30},{21,26},
{3,17},{4,16},{5,21},{6,18},{7,9},{8,20},{10,26},{11,23},{13,25},{14,28},{15,27},{22,24},
{1,4},{3,8},{5,16},{7,17},{9,21},{10,22},{11,19},{12,20},{14,24},{15,26},{23,28},{27,30},
{2,5},{7,8},{9,18},{11,17},{12,16},{13,22},{14,20},{15,19},{23,24},{26,29},
{2,4},{6,12},{9,16},{10,11},{13,17},{14,18},{15,22},{19,25},{20,21},{27,29},
{5,6},{8,12},{9,10},{11,13},{14,16},{15,17},{18,20},{19,23},{21,22},{25,26},
{3,5},{6,7},{8,9},{10,12},{11,14},{13,16},{15,18},{17,20},{19,21},{22,23},{24,25},{26,28},
{3,4},{5,6},{7,8},{9,10},{11,12},{13,14},{15,16},{17,18},{19,20},{21,22},{23,24},{25,26},{27,28}
};
__constant__ int snCols32[14] = {16, 16, 16, 16, 16, 14, 12, 12, 12, 10, 10, 10, 12, 13};
template<int ROWS, int COLS,int NUM, typename Type>
inline
__device__ void sortByNetwork(
int (&sortingNetwork) [ROWS][COLS] , int (&snCols)[NUM],
Type * arr, int * idArr, Type * cacheData, int * cacheId,
const int id, const int startIncluded, const bool trackIdValues, const int num)
{
if (!trackIdValues)
{
if (id < num)
{
cacheData[id] = arr[startIncluded + id];
}
__syncwarp();
int ofs = 0;
for (int i = 0; i < NUM; i++)
{
const int cLim = snCols[i];
if (id < cLim)
{
const int comp0 = sortingNetwork[id + ofs][0]; // this should be optimized. id values are different per thread.
const int comp1 = sortingNetwork[id + ofs][1]; // but different blocks need similar id's
if (comp0 < num && comp1 < num)
{
auto arr1 = cacheData[comp0];
auto arr2 = cacheData[comp1];
if (arr2 < arr1)
{
cacheData[comp0] = arr2;
cacheData[comp1] = arr1;
}
}
}
ofs += cLim;
__syncwarp();
}
if (id < num)
{
arr[startIncluded + id] = cacheData[id];
}
__syncwarp();
}
else
{
if (id < num)
{
cacheData[id] = arr[startIncluded + id];
cacheId[id] = idArr[startIncluded + id];
}
__syncwarp();
int ofs = 0;
for (int i = 0; i < NUM; i++)
{
const int cLim = snCols[i];
if (id < cLim)
{
const int comp0 = sortingNetwork[id + ofs][0];
const int comp1 = sortingNetwork[id + ofs][1];
if (comp0 < num && comp1 < num)
{
auto arr1 = cacheData[comp0];
auto arr2 = cacheData[comp1];
auto id1 = cacheId[comp0];
auto id2 = cacheId[comp1];
if (arr2 < arr1)
{
cacheData[comp0] = arr2;
cacheData[comp1] = arr1;
cacheId[comp0] = id2;
cacheId[comp1] = id1;
}
}
}
ofs += cLim;
__syncwarp();
}
if (id < num)
{
arr[startIncluded + id] = cacheData[id];
idArr[startIncluded + id] = cacheId[id];
}
__syncwarp();
}
}
template<typename Type>
inline
__device__ const bool equals(const Type e1, const Type e2)
{
return !(e1 < e2) && !(e1 > e2);
}
// task pattern:
// task 0 task 1 task 2 task 3 ---> array chunks to sort (no overlap)
// start stop start stop start stop start stop ---> tasks buffer
// block 0 block 1 block 2 block 3 ---> cuda blocks
template<typename Type>
__global__ void quickSortWithoutStreamCompaction(
const bool trackIdValues,
Type* __restrict__ arr, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idArr,
Type* __restrict__ arrTmp, int*__restrict__ idArrTmp)
{
// 1 block = 1 chunk of data
const int gid = blockIdx.x;
const int id = threadIdx.x;
const int bd = blockDim.x;
if (id == 0 && gid == 0)
copyTasksBack << <1, 1024, 0, cudaStreamTailLaunch >> > (trackIdValues, arr, numTasks, tasks, tasks2, tasks3, tasks4, idArr, arrTmp, idArrTmp);
__shared__ int taskIdCacheStart;
__shared__ int taskIdCacheStop;
if (id == 0)
{
taskIdCacheStart = tasks[gid * 2];
taskIdCacheStop = tasks[gid * 2 + 1];
tasks[gid * 2] = 0;
tasks[gid * 2 + 1] = 0;
}
__syncthreads();
const int startIncluded = taskIdCacheStart;
const int stopIncluded = taskIdCacheStop;
const int num = stopIncluded - startIncluded + 1;
if (startIncluded == 0 && stopIncluded == 0)
{
if (id == 0)
printf("\n quicksort task id error: %i \n", gid);
return;
}
if (num < 2)
return;
__shared__ Type cacheData[DIRECTLY_COMPUTE_LIMIT];
__shared__ int cacheId[DIRECTLY_COMPUTE_LIMIT];
// if data is suitable for 32-input sorting-network, sort directly
if (num <= DIRECTLY_COMPUTE_LIMIT && bd >= DIRECTLY_COMPUTE_LIMIT)
{
if(num<=4)
sortByNetwork<5, 2, 3, Type>(sortingNetwork4, snCols4, arr, idArr, cacheData, cacheId, id, startIncluded, trackIdValues, num);
else if(num<=8)
sortByNetwork<19, 2, 6, Type>(sortingNetwork8, snCols8, arr, idArr, cacheData, cacheId, id, startIncluded, trackIdValues, num);
else if(num<=16)
sortByNetwork<60, 2, 10,Type>(sortingNetwork16, snCols16, arr, idArr, cacheData, cacheId, id, startIncluded, trackIdValues, num);
else if(num<=32)
sortByNetwork<185,2,14,Type>(sortingNetwork32,snCols32,arr,idArr,cacheData,cacheId,id,startIncluded,trackIdValues,num);
return;
}
__shared__ int indexLeftLeft;
__shared__ int indexPivotLeft;
__shared__ int indexLeft;
__shared__ int indexPivot;
__shared__ int indexRight;
__shared__ int indexPivotRight;
__shared__ int indexRightRight;
__shared__ Type pivotLoad[3];
if (id == 0)
{
pivotLoad[0] = arr[startIncluded + (stopIncluded - startIncluded + 1) / 2];
pivotLoad[1] = arr[startIncluded];
pivotLoad[2] = arr[stopIncluded];
compareSwap(pivotLoad, 0, 1);
compareSwap(pivotLoad, 0, 2);
compareSwap(pivotLoad, 1, 2);
}
__syncthreads();
const Type pivotLeft = pivotLoad[0];
const Type pivot = pivotLoad[1];
const Type pivotRight = pivotLoad[2];
if (id == 0)
{
indexLeftLeft = 0;
indexPivotLeft = 0;
indexLeft = 0;
indexPivot = 0;
indexRight = 0;
indexPivotRight = 0;
indexRightRight = 0;
}
__syncthreads();
// two-pass
// 1: counting to find borders of chunks
// 2: moving to a temporary array & copying back to original
int countPivotLeft=0;
int countPivot=0;
int countPivotRight=0;
int countLeftLeft=0;
int countLeft=0;
int countRight=0;
int countRightRight=0;
const int stepsArray = (num / bd) + 1;
for (int i = 0; i < stepsArray; i++)
{
const int curId = i * bd + id;
if (curId < num)
{
const Type data = arr[curId + startIncluded];
if (equals(data,pivotLeft))
{
countPivotLeft++;
}
else if (equals(data,pivot))
{
countPivot++;
}
else if (equals(data,pivotRight))
{
countPivotRight++;
}
else
{
if (data < pivotLeft)
{
countLeftLeft++;
}
else if (data < pivot)
{
countLeft++;
}
else if (data < pivotRight)
{
countRight++;
}
else if (data > pivotRight)
{
countRightRight++;
}
}
}
}
__syncthreads();
__shared__ int countCache[32];
__shared__ int countCache2[32];
__shared__ int countCache3[32];
__shared__ int countCache4[32];
__shared__ int countCache5[32];
__shared__ int countCache6[32];
__shared__ int countCache7[32];
// sum of all counts (reducing number of atomicAdd calls)
int nLeftLeft = 0;
int nPivotLeft = 0;
int nLeft = 0;
int nPivot = 0;
int nRight = 0;
int nPivotRight = 0;
int nRightRight = 0;
reductionWithBlock7x(
countLeftLeft, countCache,
countPivotLeft, countCache2,
countLeft, countCache3,
countPivot,countCache4,
countRight, countCache5,
countPivotRight, countCache6,
countRightRight, countCache7,
id, bd,
&nLeftLeft, &nPivotLeft,
&nLeft, &nPivot,
&nRight, &nPivotRight,
&nRightRight);
const int offsetLeftLeft = startIncluded;
const int offsetPivotLeft = offsetLeftLeft+nLeftLeft;
const int offsetLeft = offsetPivotLeft + nPivotLeft;
const int offsetPivot = offsetLeft + nLeft;
const int offsetRight = offsetPivot + nPivot;
const int offsetPivotRight = offsetRight + nRight;
const int offsetRightRight = offsetPivotRight + nPivotRight;
__syncthreads();
for (int i = 0; i < stepsArray; i++)
{
const int curId = i * bd + id;
if (curId < num)
{
const Type data = arr[curId + startIncluded];
int dataId = 0;
if(trackIdValues)
dataId = idArr[curId + startIncluded];
// todo: pivot values are known so they are "counting", no need to copy values anywhere
// but id values will be required
if (equals(data,pivotLeft))
{
auto idx = atomicAdd(&indexPivotLeft, 1);
if (trackIdValues)
idArrTmp[offsetPivotLeft+idx] = dataId;
}
else if (equals(data,pivot))
{
auto idx = atomicAdd(&indexPivot, 1);
if (trackIdValues)
idArrTmp[offsetPivot + idx] = dataId;
}
else if (equals(data,pivotRight))
{
auto idx = atomicAdd(&indexPivotRight, 1);
if (trackIdValues)
idArrTmp[offsetPivotRight + idx] = dataId;
}
else
{
if (data < pivotLeft)
{
auto idx = atomicAdd(&indexLeftLeft, 1);
if (trackIdValues)
idArrTmp[offsetLeftLeft + idx] = dataId;
arrTmp[offsetLeftLeft + idx] = data;
}
else if (data < pivot)
{
auto idx = atomicAdd(&indexLeft, 1);
if (trackIdValues)
idArrTmp[offsetLeft + idx] = dataId;
arrTmp[offsetLeft + idx] = data;
}
else if (data < pivotRight)
{
auto idx = atomicAdd(&indexRight, 1);
if (trackIdValues)
idArrTmp[offsetRight + idx] = dataId;
arrTmp[offsetRight + idx] = data;
}
else if (data > pivotRight)
{
auto idx = atomicAdd(&indexRightRight, 1);
if (trackIdValues)
idArrTmp[offsetRightRight + idx] = dataId;
arrTmp[offsetRightRight + idx] = data;
}
}
}
}
__syncthreads();
// copying from temporary to real data
for (int i = 0; i < stepsArray; i++)
{
const int curId = i * bd + id;
if (curId < num)
{
if (curId >= nLeftLeft + nPivotLeft + nLeft && curId < nLeftLeft + nPivotLeft + nLeft + nPivot)
arr[startIncluded + curId] = pivot;
else if (curId >= nLeftLeft && curId < nLeftLeft + nPivotLeft)
arr[startIncluded + curId] = pivotLeft;
else if (curId >= nLeftLeft + nPivotLeft + nLeft + nPivot + nRight && curId < nLeftLeft + nPivotLeft + nLeft + nPivot + nRight + nPivotRight)
arr[startIncluded + curId] = pivotRight;
else
arr[startIncluded + curId] = arrTmp[startIncluded + curId];
if (trackIdValues)
idArr[startIncluded + curId] = idArrTmp[startIncluded + curId];
}
}
__syncthreads();
if (id == 0)
{
if (nLeftLeft + nPivotLeft + nLeft + nPivot + nRight + nPivotRight + nRightRight != num)
printf(" @@ ERROR: wrong partition values!! @@");
if (nLeftLeft > 1)
{
if (nLeftLeft <= BRUTE_FORCE_LIMIT && nLeftLeft > DIRECTLY_COMPUTE_LIMIT) // push new "brute-force" task
{
const int index = atomicAdd(&numTasks[1], 1);
tasks4[index * 2] = startIncluded;
tasks4[index * 2 + 1] = startIncluded + nLeftLeft - 1;
}
else// push new "quick" task
{
const int index = atomicAdd(&numTasks[0], 1);
tasks2[index * 2] = startIncluded;
tasks2[index * 2 + 1] = startIncluded + nLeftLeft - 1;
}
}
if (nLeft > 1)
{
if (nLeft <= BRUTE_FORCE_LIMIT && nLeft > DIRECTLY_COMPUTE_LIMIT) // push new "brute-force" task
{
const int index = atomicAdd(&numTasks[1], 1);
tasks4[index * 2] = startIncluded + nLeftLeft + nPivotLeft;
tasks4[index * 2 + 1] = startIncluded + nLeftLeft + nPivotLeft + nLeft - 1;
}
else// push new "quick" task
{
const int index = atomicAdd(&numTasks[0], 1);
tasks2[index * 2] = startIncluded + nLeftLeft + nPivotLeft;
tasks2[index * 2 + 1] = startIncluded + nLeftLeft + nPivotLeft + nLeft - 1;
}
}
if (nRight > 1)
{
if (nRight <= BRUTE_FORCE_LIMIT && nRight > DIRECTLY_COMPUTE_LIMIT) // push new "brute-force" task
{
const int index = atomicAdd(&numTasks[1], 1);
tasks4[index * 2] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot;
tasks4[index * 2 + 1] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot + nRight - 1;
}
else // push new "quick" task
{
const int index = atomicAdd(&numTasks[0], 1);
tasks2[index * 2] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot;
tasks2[index * 2 + 1] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot + nRight - 1;
}
}
if (nRightRight > 1)
{
if (nRightRight <= BRUTE_FORCE_LIMIT && nRightRight > DIRECTLY_COMPUTE_LIMIT) // push new "brute-force" task
{
const int index = atomicAdd(&numTasks[1], 1);
tasks4[index * 2] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot + nRight + nPivotRight;
tasks4[index * 2 + 1] = stopIncluded;
}
else // push new "quick" task
{
const int index = atomicAdd(&numTasks[0], 1);
tasks2[index * 2] = startIncluded + nLeftLeft + nPivotLeft + nLeft + nPivot + nRight + nPivotRight;
tasks2[index * 2 + 1] = stopIncluded;
}
}
}
}
__global__ void resetTasks(int* tasks, int* tasks2, int* tasks3, int* tasks4, const int n)
{
const int id = threadIdx.x + blockIdx.x * blockDim.x;
if (id < n)
{
tasks[id] = 0;
tasks2[id] = 0;
tasks3[id] = 0;
tasks4[id] = 0;
}
}
void resetTasksHost(cudaStream_t & stream0, int* tasks, int* tasks2, int* tasks3, int* tasks4, const int n)
{
resetTasks <<<1 + n / 1024, 1024, 0, stream0 >>> (tasks, tasks2, tasks3, tasks4, n);
}
// int data
template
__global__ void copyTasksBack(
const bool trackIdValues,
int* __restrict__ data, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
int* __restrict__ arrTmp, int* __restrict__ idArrTmp);
template
__global__ void mergeSortedChunks(
const bool trackIdValues,int* __restrict__ tasks,int* __restrict__ arr,
int* __restrict__ arrTmp, int* __restrict__ idArr, int* __restrict__ idArrTmp);
template
__global__ void copyMergedChunkBack(
const bool trackIdValues, const int n, int* __restrict__ arr,
int* __restrict__ arrTmp, int* __restrict__ idArr, int* __restrict__ idArrTmp);
// short data
template
__global__ void copyTasksBack(
const bool trackIdValues,
short* __restrict__ data, int* __restrict__ numTasks,
int* __restrict__ tasks, int* __restrict__ tasks2, int* __restrict__ tasks3, int* __restrict__ tasks4,
int* __restrict__ idData,
short* __restrict__ arrTmp, int* __restrict__ idArrTmp);
template
__global__ void mergeSortedChunks(
const bool trackIdValues, int* __restrict__ tasks, short* __restrict__ arr,
short* __restrict__ arrTmp, int* __restrict__ idArr, int* __restrict__ idArrTmp);
template
__global__ void copyMergedChunkBack(
const bool trackIdValues, const int n, short* __restrict__ arr,
short* __restrict__ arrTmp, int* __restrict__ idArr, int* __restrict__ idArrTmp);
// char data
template