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Merge branch 'develop' of https://github.com/paboyle/Grid into mbruno-eclover

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This commit is contained in:
Mattia Bruno 2022-03-22 15:31:37 +01:00
commit 9ca2c98882
4 changed files with 111 additions and 11 deletions
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25
Grid/lattice/Lattice_reduction.h
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@ -142,6 +142,15 @@ inline typename vobj::scalar_objectD sumD(const vobj *arg, Integer osites)
return sumD_cpu(arg,osites);
#endif
}
template<class vobj>
inline typename vobj::scalar_objectD sumD_large(const vobj *arg, Integer osites)
{
#if defined(GRID_CUDA)||defined(GRID_HIP)
return sumD_gpu_large(arg,osites);
#else
return sumD_cpu(arg,osites);
#endif
}
template<class vobj>
inline typename vobj::scalar_object sum(const Lattice<vobj> &arg)
@ -159,6 +168,22 @@ inline typename vobj::scalar_object sum(const Lattice<vobj> &arg)
return ssum;
}
template<class vobj>
inline typename vobj::scalar_object sum_large(const Lattice<vobj> &arg)
{
#if defined(GRID_CUDA)||defined(GRID_HIP)
autoView( arg_v, arg, AcceleratorRead);
Integer osites = arg.Grid()->oSites();
auto ssum= sum_gpu_large(&arg_v[0],osites);
#else
autoView(arg_v, arg, CpuRead);
Integer osites = arg.Grid()->oSites();
auto ssum= sum_cpu(&arg_v[0],osites);
#endif
arg.Grid()->GlobalSum(ssum);
return ssum;
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Deterministic Reduction operations
////////////////////////////////////////////////////////////////////////////////////////////////////

73
Grid/lattice/Lattice_reduction_gpu.h
View File

@ -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_small(const vobj *lat, Integer osites)
{
typedef typename vobj::scalar_objectD sobj;
typedef decltype(lat) Iterator;
@ -207,7 +207,9 @@ inline typename vobj::scalar_objectD sumD_gpu(const vobj *lat, Integer osites)
Integer size = osites*nsimd;
Integer numThreads, numBlocks;
getNumBlocksAndThreads(size, sizeof(sobj), numThreads, numBlocks);
int ok = getNumBlocksAndThreads(size, sizeof(sobj), numThreads, numBlocks);
assert(ok);
Integer smemSize = numThreads * sizeof(sobj);
Vector<sobj> buffer(numBlocks);
@ -218,6 +220,54 @@ 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_large(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);
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_small(tbuf,osites);
}
return ret;
}
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;
Integer nsimd= vobj::Nsimd();
Integer size = osites*nsimd;
Integer numThreads, numBlocks;
int ok = getNumBlocksAndThreads(size, sizeof(sobj), numThreads, numBlocks);
if ( ok ) {
ret = sumD_gpu_small(lat,osites);
} else {
ret = sumD_gpu_large(lat,osites);
}
return ret;
}
/////////////////////////////////////////////////////////////////////////////////////////////////////////
// Return as same precision as input performing reduction in double precision though
/////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -230,6 +280,13 @@ inline typename vobj::scalar_object sum_gpu(const vobj *lat, Integer osites)
return result;
}
template <class vobj>
inline typename vobj::scalar_object sum_gpu_large(const vobj *lat, Integer osites)
{
typedef typename vobj::scalar_object sobj;
sobj result;
result = sumD_gpu_large(lat,osites);
return result;
}
NAMESPACE_END(Grid);

7
Grid/threads/Accelerator.h
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@ -481,9 +481,10 @@ inline void acceleratorCopySynchronise(void) { hipStreamSynchronize(copyStream);
#define accelerator_for2d(iter1, num1, iter2, num2, nsimd, ... ) thread_for2d(iter1,num1,iter2,num2,{ __VA_ARGS__ });
accelerator_inline int acceleratorSIMTlane(int Nsimd) { return 0; } // CUDA specific
inline void acceleratorCopyToDevice(void *from,void *to,size_t bytes) { memcpy(to,from,bytes);}
inline void acceleratorCopyFromDevice(void *from,void *to,size_t bytes){ memcpy(to,from,bytes);}
inline void acceleratorCopyDeviceToDeviceAsynch(void *from,void *to,size_t bytes) { memcpy(to,from,bytes);}
inline void acceleratorCopyToDevice(void *from,void *to,size_t bytes) { thread_bcopy(from,to,bytes); }
inline void acceleratorCopyFromDevice(void *from,void *to,size_t bytes){ thread_bcopy(from,to,bytes);}
inline void acceleratorCopyDeviceToDeviceAsynch(void *from,void *to,size_t bytes) { thread_bcopy(from,to,bytes);}
inline void acceleratorCopySynchronise(void) {};
inline int acceleratorIsCommunicable(void *ptr){ return 1; }

17
Grid/threads/Threads.h
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@ -72,3 +72,20 @@ Author: paboyle <paboyle@ph.ed.ac.uk>
#define thread_region DO_PRAGMA(omp parallel)
#define thread_critical DO_PRAGMA(omp critical)
#ifdef GRID_OMP
inline void thread_bcopy(void *from, void *to,size_t bytes)
{
uint64_t *ufrom = (uint64_t *)from;
uint64_t *uto = (uint64_t *)to;
assert(bytes%8==0);
uint64_t words=bytes/8;
thread_for(w,words,{
uto[w] = ufrom[w];
});
}
#else
inline void thread_bcopy(void *from, void *to,size_t bytes)
{
bcopy(from,to,bytes);
}
#endif