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Better reduction for GPUs

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This commit is contained in:
Peter Boyle 2021-10-29 02:22:22 +01:00
parent fe9edf8526
commit 0b905a72dd
1 changed files with 42 additions and 6 deletions
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48
Grid/lattice/Lattice_reduction_gpu.h
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@ -23,7 +23,7 @@ unsigned int nextPow2(Iterator x) {
}
template <class Iterator>
void getNumBlocksAndThreads(const Iterator n, const size_t sizeofsobj, Iterator &threads, Iterator &blocks) {
int getNumBlocksAndThreads(const Iterator n, const size_t sizeofsobj, Iterator &threads, Iterator &blocks) {
int device;
#ifdef GRID_CUDA
@ -37,13 +37,13 @@ void getNumBlocksAndThreads(const Iterator n, const size_t sizeofsobj, Iterator
Iterator sharedMemPerBlock = gpu_props[device].sharedMemPerBlock;
Iterator maxThreadsPerBlock = gpu_props[device].maxThreadsPerBlock;
Iterator multiProcessorCount = gpu_props[device].multiProcessorCount;
/*
std::cout << GridLogDebug << "GPU has:" << std::endl;
std::cout << GridLogDebug << "\twarpSize = " << warpSize << std::endl;
std::cout << GridLogDebug << "\tsharedMemPerBlock = " << sharedMemPerBlock << std::endl;
std::cout << GridLogDebug << "\tmaxThreadsPerBlock = " << maxThreadsPerBlock << std::endl;
std::cout << GridLogDebug << "\tmultiProcessorCount = " << multiProcessorCount << std::endl;
*/
if (warpSize != WARP_SIZE) {
std::cout << GridLogError << "The warp size of the GPU in use does not match the warp size set when compiling Grid." << std::endl;
exit(EXIT_FAILURE);
@ -53,12 +53,12 @@ void getNumBlocksAndThreads(const Iterator n, const size_t sizeofsobj, Iterator
threads = warpSize;
if ( threads*sizeofsobj > sharedMemPerBlock ) {
std::cout << GridLogError << "The object is too large for the shared memory." << std::endl;
exit(EXIT_FAILURE);
return 0;
}
while( 2*threads*sizeofsobj < sharedMemPerBlock && 2*threads <= maxThreadsPerBlock ) threads *= 2;
// keep all the streaming multiprocessors busy
blocks = nextPow2(multiProcessorCount);
return 1;
}
template <class sobj, class Iterator>
@ -198,7 +198,7 @@ __global__ void reduceKernel(const vobj *lat, sobj *buffer, Iterator n) {
// Possibly promote to double and sum
/////////////////////////////////////////////////////////////////////////////////////////////////////////
template <class vobj>
inline typename vobj::scalar_objectD sumD_gpu(const vobj *lat, Integer osites)
inline typename vobj::scalar_objectD sumD_gpu_internal(const vobj *lat, Integer osites)
{
typedef typename vobj::scalar_objectD sobj;
typedef decltype(lat) Iterator;
@ -208,6 +208,7 @@ inline typename vobj::scalar_objectD sumD_gpu(const vobj *lat, Integer osites)
Integer numThreads, numBlocks;
getNumBlocksAndThreads(size, sizeof(sobj), numThreads, numBlocks);
Integer smemSize = numThreads * sizeof(sobj);
Vector<sobj> buffer(numBlocks);
@ -218,6 +219,41 @@ inline typename vobj::scalar_objectD sumD_gpu(const vobj *lat, Integer osites)
auto result = buffer_v[0];
return result;
}
template <class vobj>
inline typename vobj::scalar_objectD sumD_gpu(const vobj *lat, Integer osites)
{
typedef typename vobj::vector_type vector;
typedef typename vobj::scalar_typeD scalarD;
typedef typename vobj::scalar_objectD sobj;
sobj ret;
scalarD *ret_p = (scalarD *)&ret;
const int words = sizeof(vobj)/sizeof(vector);
Integer nsimd= vobj::Nsimd();
Integer size = osites*nsimd;
Integer numThreads, numBlocks;
int ok = getNumBlocksAndThreads(size, sizeof(sobj), numThreads, numBlocks);
if ( ok ) {
ret = sumD_gpu_internal(lat,osites);
} else {
std::cout << GridLogWarning << " dropping to summing word by word for large object size "<<sizeof(vobj)<<std::endl;
Vector<vector> buffer(osites);
vector *dat = (vector *)lat;
vector *buf = &buffer[0];
iScalar<vector> *tbuf =(iScalar<vector> *) &buffer[0];
for(int w=0;w<words;w++) {
accelerator_for(ss,osites,1,{
buf[ss] = dat[ss*words+w];
});
ret_p[w] = sumD_gpu_internal(tbuf,osites);
}
}
return ret;
}
/////////////////////////////////////////////////////////////////////////////////////////////////////////
// Return as same precision as input performing reduction in double precision though
/////////////////////////////////////////////////////////////////////////////////////////////////////////