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Merge pull request #3953 from Tongdongq/tweak-opencl-launch-params

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Tweak opencl launch params
This commit is contained in:
Markus Blatt 2022-07-07 10:43:15 +02:00 committed by GitHub
commit f22d58a1d0
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7 changed files with 75 additions and 35 deletions
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18
opm/simulators/linalg/bda/opencl/BILU0.cpp
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@ -136,8 +136,8 @@ bool BILU0<block_size>::analyze_matrix(BlockedMatrix *mat, BlockedMatrix *jacMat
invDiagVals.resize(mat->Nb * bs * bs);
#if CHOW_PATEL
Lmat = std::make_unique<BlockedMatrix<block_size> >(mat->Nb, (mat->nnzbs - mat->Nb) / 2);
Umat = std::make_unique<BlockedMatrix<block_size> >(mat->Nb, (mat->nnzbs - mat->Nb) / 2);
Lmat = std::make_unique<BlockedMatrix>(mat->Nb, (mat->nnzbs - mat->Nb) / 2, block_size);
Umat = std::make_unique<BlockedMatrix>(mat->Nb, (mat->nnzbs - mat->Nb) / 2, block_size);
#endif
s.invDiagVals = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(double) * bs * bs * mat->Nb);
@ -223,9 +223,9 @@ bool BILU0<block_size>::create_preconditioner(BlockedMatrix *mat, BlockedMatrix
}
#if CHOW_PATEL
chowPatelIlu.decomposition(queue, context,
chowPatelIlu.decomposition(queue.get(), context.get(),
LUmat.get(), Lmat.get(), Umat.get(),
invDiagVals, diagIndex,
invDiagVals.data(), diagIndex,
s.diagIndex, s.invDiagVals,
s.Lvals, s.Lcols, s.Lrows,
s.Uvals, s.Ucols, s.Urows);
@ -273,7 +273,7 @@ bool BILU0<block_size>::create_preconditioner(BlockedMatrix *mat, BlockedMatrix
if (verbosity >= 5) {
out << "color " << color << ": " << firstRow << " - " << lastRow << " = " << lastRow - firstRow << "\n";
}
OpenclKernels::ILU_decomp(firstRow, lastRow, s.LUvals, s.LUcols, s.LUrows, s.diagIndex, s.invDiagVals, Nb, block_size);
OpenclKernels::ILU_decomp(firstRow, lastRow, s.LUvals, s.LUcols, s.LUrows, s.diagIndex, s.invDiagVals, rowsPerColor[color], block_size);
}
if (verbosity >= 3) {
@ -299,17 +299,17 @@ void BILU0<block_size>::apply(const cl::Buffer& y, cl::Buffer& x)
for (int color = 0; color < numColors; ++color) {
#if CHOW_PATEL
OpenclKernels::ILU_apply1(s.Lvals, s.Lcols, s.Lrows, s.diagIndex, y, x, s.rowsPerColor, color, Nb, block_size);
OpenclKernels::ILU_apply1(s.Lvals, s.Lcols, s.Lrows, s.diagIndex, y, x, s.rowsPerColor, color, rowsPerColor[color], block_size);
#else
OpenclKernels::ILU_apply1(s.LUvals, s.LUcols, s.LUrows, s.diagIndex, y, x, s.rowsPerColor, color, Nb, block_size);
OpenclKernels::ILU_apply1(s.LUvals, s.LUcols, s.LUrows, s.diagIndex, y, x, s.rowsPerColor, color, rowsPerColor[color], block_size);
#endif
}
for (int color = numColors - 1; color >= 0; --color) {
#if CHOW_PATEL
OpenclKernels::ILU_apply2(s.Uvals, s.Ucols, s.Urows, s.diagIndex, s.invDiagVals, x, s.rowsPerColor, color, Nb, block_size);
OpenclKernels::ILU_apply2(s.Uvals, s.Ucols, s.Urows, s.diagIndex, s.invDiagVals, x, s.rowsPerColor, color, rowsPerColor[color], block_size);
#else
OpenclKernels::ILU_apply2(s.LUvals, s.LUcols, s.LUrows, s.diagIndex, s.invDiagVals, x, s.rowsPerColor, color, Nb, block_size);
OpenclKernels::ILU_apply2(s.LUvals, s.LUcols, s.LUrows, s.diagIndex, s.invDiagVals, x, s.rowsPerColor, color, rowsPerColor[color], block_size);
#endif
}

1
opm/simulators/linalg/bda/opencl/kernels/ILU_apply1.cl
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@ -1,6 +1,7 @@
/// ILU apply part 1: forward substitution.
/// Solves L*x=y where L is a lower triangular sparse blocked matrix.
/// Here, L is it's own BSR matrix.
/// Only used with ChowPatelIlu.
__kernel void ILU_apply1(
__global const double *LUvals,
__global const unsigned int *LUcols,

2
opm/simulators/linalg/bda/opencl/kernels/ILU_apply1_fm.cl
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@ -14,7 +14,7 @@ __kernel void ILU_apply1(
const unsigned int block_size,
__local double *tmp)
{
const unsigned int warpsize = 32;
const unsigned int warpsize = get_local_size(0);
const unsigned int bs = block_size;
const unsigned int idx_t = get_local_id(0);
const unsigned int num_active_threads = (warpsize/bs/bs)*bs*bs;

1
opm/simulators/linalg/bda/opencl/kernels/ILU_apply2.cl
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@ -1,6 +1,7 @@
/// ILU apply part 2: backward substitution.
/// Solves U*x=y where U is an upper triangular sparse blocked matrix.
/// Here, U is it's own BSR matrix.
/// Only used with ChowPatelIlu.
__kernel void ILU_apply2(
__global const double *LUvals,
__global const int *LUcols,

2
opm/simulators/linalg/bda/opencl/kernels/ILU_apply2_fm.cl
View File

@ -14,7 +14,7 @@ __kernel void ILU_apply2(
const unsigned int block_size,
__local double *tmp)
{
const unsigned int warpsize = 32;
const unsigned int warpsize = get_local_size(0);
const unsigned int bs = block_size;
const unsigned int idx_t = get_local_id(0);
const unsigned int num_active_threads = (warpsize/bs/bs)*bs*bs;

85
opm/simulators/linalg/bda/opencl/openclKernels.cpp
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@ -41,6 +41,7 @@ int OpenclKernels::verbosity;
cl::CommandQueue *OpenclKernels::queue;
std::vector<double> OpenclKernels::tmp;
bool OpenclKernels::initialized = false;
size_t OpenclKernels::preferred_workgroup_size = 0;
std::unique_ptr<cl::KernelFunctor<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg> > OpenclKernels::dot_k;
std::unique_ptr<cl::KernelFunctor<cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg> > OpenclKernels::norm_k;
@ -140,6 +141,11 @@ void OpenclKernels::init(cl::Context *context, cl::CommandQueue *queue_, std::ve
isaiL_k.reset(new isaiL_kernel_type(cl::Kernel(program, "isaiL")));
isaiU_k.reset(new isaiU_kernel_type(cl::Kernel(program, "isaiU")));
// testing shows all kernels have the same preferred_workgroup_size_multiple
// 32 for NVIDIA
// 64 for AMD
cl::Kernel(program, "ILU_apply1").getWorkGroupInfo(devices[0], CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, &preferred_workgroup_size_multiple);
initialized = true;
} // end get_opencl_kernels()
@ -251,7 +257,8 @@ void OpenclKernels::vmul(const double alpha, cl::Buffer& in1, cl::Buffer& in2, c
}
}
void OpenclKernels::custom(cl::Buffer& p, cl::Buffer& v, cl::Buffer& r, const double omega, const double beta, int N)
void OpenclKernels::custom(cl::Buffer& p, cl::Buffer& v, cl::Buffer& r,
const double omega, const double beta, int N)
{
const unsigned int work_group_size = 32;
const unsigned int num_work_groups = ceilDivision(N, work_group_size);
@ -319,7 +326,9 @@ void OpenclKernels::prolongate_vector(const cl::Buffer& in, cl::Buffer& out, con
}
}
void OpenclKernels::spmv(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, const cl::Buffer& x, cl::Buffer& b, int Nb, unsigned int block_size, bool reset, bool add)
void OpenclKernels::spmv(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows,
const cl::Buffer& x, cl::Buffer& b, int Nb,
unsigned int block_size, bool reset, bool add)
{
const unsigned int work_group_size = 32;
const unsigned int num_work_groups = ceilDivision(Nb, work_group_size);
@ -330,15 +339,19 @@ void OpenclKernels::spmv(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, c
if (block_size > 1) {
if (add) {
event = (*spmv_blocked_add_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, b, block_size, cl::Local(lmem_per_work_group));
event = (*spmv_blocked_add_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, b, block_size, cl::Local(lmem_per_work_group));
} else {
event = (*spmv_blocked_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, b, block_size, cl::Local(lmem_per_work_group));
event = (*spmv_blocked_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, b, block_size, cl::Local(lmem_per_work_group));
}
} else {
if (reset) {
event = (*spmv_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, b, cl::Local(lmem_per_work_group));
event = (*spmv_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, b, cl::Local(lmem_per_work_group));
} else {
event = (*spmv_noreset_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, b, cl::Local(lmem_per_work_group));
event = (*spmv_noreset_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, b, cl::Local(lmem_per_work_group));
}
}
@ -350,7 +363,9 @@ void OpenclKernels::spmv(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, c
}
}
void OpenclKernels::residual(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, cl::Buffer& x, const cl::Buffer& rhs, cl::Buffer& out, int Nb, unsigned int block_size)
void OpenclKernels::residual(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows,
cl::Buffer& x, const cl::Buffer& rhs,
cl::Buffer& out, int Nb, unsigned int block_size)
{
const unsigned int work_group_size = 32;
const unsigned int num_work_groups = ceilDivision(Nb, work_group_size);
@ -360,9 +375,11 @@ void OpenclKernels::residual(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& row
cl::Event event;
if (block_size > 1) {
event = (*residual_blocked_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, rhs, out, block_size, cl::Local(lmem_per_work_group));
event = (*residual_blocked_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, rhs, out, block_size, cl::Local(lmem_per_work_group));
} else {
event = (*residual_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, Nb, x, rhs, out, cl::Local(lmem_per_work_group));
event = (*residual_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, Nb, x, rhs, out, cl::Local(lmem_per_work_group));
}
if (verbosity >= 4) {
@ -373,15 +390,22 @@ void OpenclKernels::residual(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& row
}
}
void OpenclKernels::ILU_apply1(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, cl::Buffer& diagIndex, const cl::Buffer& y, cl::Buffer& x, cl::Buffer& rowsPerColor, int color, int Nb, unsigned int block_size)
void OpenclKernels::ILU_apply1(cl::Buffer& vals, cl::Buffer& cols,
cl::Buffer& rows, cl::Buffer& diagIndex,
const cl::Buffer& y, cl::Buffer& x,
cl::Buffer& rowsPerColor, int color,
int rowsThisColor, unsigned int block_size)
{
const unsigned int work_group_size = 32;
const unsigned int num_work_groups = ceilDivision(Nb, work_group_size);
const unsigned int work_group_size = preferred_workgroup_size_multiple;
const unsigned int num_work_groups = rowsThisColor;
const unsigned int total_work_items = num_work_groups * work_group_size;
const unsigned int lmem_per_work_group = sizeof(double) * work_group_size;
Timer t_ilu_apply1;
cl::Event event = (*ILU_apply1_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, diagIndex, y, x, rowsPerColor, color, block_size, cl::Local(lmem_per_work_group));
cl::Event event = (*ILU_apply1_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, diagIndex, y, x,
rowsPerColor, color, block_size,
cl::Local(lmem_per_work_group));
if (verbosity >= 5) {
event.wait();
@ -391,15 +415,22 @@ void OpenclKernels::ILU_apply1(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& r
}
}
void OpenclKernels::ILU_apply2(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, cl::Buffer& diagIndex, cl::Buffer& invDiagVals, cl::Buffer& x, cl::Buffer& rowsPerColor, int color, int Nb, unsigned int block_size)
void OpenclKernels::ILU_apply2(cl::Buffer& vals, cl::Buffer& cols,
cl::Buffer& rows, cl::Buffer& diagIndex,
cl::Buffer& invDiagVals, cl::Buffer& x,
cl::Buffer& rowsPerColor, int color,
int rowsThisColor, unsigned int block_size)
{
const unsigned int work_group_size = 32;
const unsigned int num_work_groups = ceilDivision(Nb, work_group_size);
const unsigned int work_group_size = preferred_workgroup_size_multiple;
const unsigned int num_work_groups = rowsThisColor;
const unsigned int total_work_items = num_work_groups * work_group_size;
const unsigned int lmem_per_work_group = sizeof(double) * work_group_size;
Timer t_ilu_apply2;
cl::Event event = (*ILU_apply2_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)), vals, cols, rows, diagIndex, invDiagVals, x, rowsPerColor, color, block_size, cl::Local(lmem_per_work_group));
cl::Event event = (*ILU_apply2_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
vals, cols, rows, diagIndex, invDiagVals,
x, rowsPerColor, color, block_size,
cl::Local(lmem_per_work_group));
if (verbosity >= 5) {
event.wait();
@ -409,16 +440,22 @@ void OpenclKernels::ILU_apply2(cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& r
}
}
void OpenclKernels::ILU_decomp(int firstRow, int lastRow, cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows, cl::Buffer& diagIndex, cl::Buffer& invDiagVals, int Nb, unsigned int block_size)
void OpenclKernels::ILU_decomp(int firstRow, int lastRow,
cl::Buffer& vals, cl::Buffer& cols, cl::Buffer& rows,
cl::Buffer& diagIndex, cl::Buffer& invDiagVals,
int rowsThisColor, unsigned int block_size)
{
const unsigned int work_group_size2 = 128;
const unsigned int num_work_groups2 = 1024;
const unsigned int total_work_items2 = num_work_groups2 * work_group_size2;
const unsigned int num_hwarps_per_group = work_group_size2 / 16;
const unsigned int lmem_per_work_group2 = num_hwarps_per_group * block_size * block_size * sizeof(double); // each block needs a pivot
const unsigned int work_group_size = 128;
const unsigned int num_work_groups = rowsThisColor;
const unsigned int total_work_items = num_work_groups * work_group_size;
const unsigned int num_hwarps_per_group = work_group_size / 16;
const unsigned int lmem_per_work_group = num_hwarps_per_group * block_size * block_size * sizeof(double); // each block needs a pivot
Timer t_ilu_decomp;
cl::Event event = (*ilu_decomp_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items2), cl::NDRange(work_group_size2)), firstRow, lastRow, vals, cols, rows, invDiagVals, diagIndex, Nb, cl::Local(lmem_per_work_group2));
cl::Event event = (*ilu_decomp_k)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
firstRow, lastRow, vals, cols, rows,
invDiagVals, diagIndex, rowsThisColor,
cl::Local(lmem_per_work_group));
if (verbosity >= 4) {
event.wait();

1
opm/simulators/linalg/bda/opencl/openclKernels.hpp
View File

@ -64,6 +64,7 @@ private:
static cl::CommandQueue *queue;
static std::vector<double> tmp; // used as tmp CPU buffer for dot() and norm()
static bool initialized;
static size_t preferred_workgroup_size; // stores CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE
static std::unique_ptr<cl::KernelFunctor<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg> > dot_k;
static std::unique_ptr<cl::KernelFunctor<cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg> > norm_k;