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[Performance Improvement] Make GPU sampling and to_block use pinned memory to decrease required synchronization #5685

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merged 4 commits into from
May 17, 2023
Merged

[Performance Improvement] Make GPU sampling and to_block use pinned memory to decrease required synchronization #5685

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24d79ad
Make sampling and to_block use pinned memory
nv-dlasalle May 12, 2023
4116ebc
Clang format
nv-dlasalle May 13, 2023
1bbf3ea
Add alternative for non-pytorch runs
nv-dlasalle May 16, 2023
dcdefbc
Merge branch 'master' into async_gpu_sampling
nv-dlasalle May 16, 2023
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13 changes: 7 additions & 6 deletions src/array/cuda/rowwise_sampling.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -287,13 +287,13 @@ COOMatrix _CSRRowWiseSamplingUniform(
cudaEvent_t copyEvent;
CUDA_CALL(cudaEventCreate(&copyEvent));

// TODO(dlasalle): use pinned memory to overlap with the actual sampling, and
// wait on a cudaevent
IdType new_len;
auto new_len_tensor = NDArray::PinnedEmpty(
{1}, DGLDataTypeTraits<IdType>::dtype, DGLContext{kDGLCPU, 0});

// copy using the internal current stream
device->CopyDataFromTo(
out_ptr, num_rows * sizeof(new_len), &new_len, 0, sizeof(new_len), ctx,
DGLContext{kDGLCPU, 0}, mat.indptr->dtype);
CUDA_CALL(cudaMemcpyAsync(
new_len_tensor->data, out_ptr + num_rows, sizeof(IdType),
cudaMemcpyDeviceToHost, stream));
CUDA_CALL(cudaEventRecord(copyEvent, stream));

const uint64_t random_seed = RandomEngine::ThreadLocal()->RandInt(1000000000);
Expand Down Expand Up @@ -322,6 +322,7 @@ COOMatrix _CSRRowWiseSamplingUniform(
CUDA_CALL(cudaEventSynchronize(copyEvent));
CUDA_CALL(cudaEventDestroy(copyEvent));

const IdType new_len = static_cast<const IdType*>(new_len_tensor->data)[0];
picked_row = picked_row.CreateView({new_len}, picked_row->dtype);
picked_col = picked_col.CreateView({new_len}, picked_col->dtype);
picked_idx = picked_idx.CreateView({new_len}, picked_idx->dtype);
Expand Down
33 changes: 24 additions & 9 deletions src/graph/transform/cuda/cuda_to_block.cu
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Expand Up @@ -165,6 +165,9 @@ struct CUDAIdsMapper {
NewIdArray(maxNodesPerType[ntype], ctx, sizeof(IdType) * 8));
}
}

cudaEvent_t copyEvent;
NDArray new_len_tensor;
// Populate the mappings.
if (generate_lhs_nodes) {
int64_t* count_lhs_device = static_cast<int64_t*>(
Expand All @@ -174,13 +177,16 @@ struct CUDAIdsMapper {
src_nodes, rhs_nodes, &node_maps, count_lhs_device, &lhs_nodes,
stream);

device->CopyDataFromTo(
count_lhs_device, 0, num_nodes_per_type.data(), 0,
sizeof(*num_nodes_per_type.data()) * num_ntypes, ctx,
DGLContext{kDGLCPU, 0}, DGLDataType{kDGLInt, 64, 1});
device->StreamSync(ctx, stream);
CUDA_CALL(cudaEventCreate(&copyEvent));
new_len_tensor = NDArray::PinnedEmpty(
{num_ntypes}, DGLDataTypeTraits<int64_t>::dtype,
DGLContext{kDGLCPU, 0});
CUDA_CALL(cudaMemcpyAsync(
new_len_tensor->data, count_lhs_device,
sizeof(*num_nodes_per_type.data()) * num_ntypes,
cudaMemcpyDeviceToHost, stream));
CUDA_CALL(cudaEventRecord(copyEvent, stream));

// Wait for the node counts to finish transferring.
device->FreeWorkspace(ctx, count_lhs_device);
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} else {
maker.Make(lhs_nodes, rhs_nodes, &node_maps, stream);
Expand All @@ -189,14 +195,23 @@ struct CUDAIdsMapper {
num_nodes_per_type[ntype] = lhs_nodes[ntype]->shape[0];
}
}
// Resize lhs nodes.
// Map node numberings from global to local, and build pointer for CSR.
auto ret = MapEdges(graph, edge_arrays, node_maps, stream);

if (generate_lhs_nodes) {
// wait for the previous copy
CUDA_CALL(cudaEventSynchronize(copyEvent));
CUDA_CALL(cudaEventDestroy(copyEvent));

// Resize lhs nodes.
for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
num_nodes_per_type[ntype] =
static_cast<int64_t*>(new_len_tensor->data)[ntype];
lhs_nodes[ntype]->shape[0] = num_nodes_per_type[ntype];
}
}
// Map node numberings from global to local, and build pointer for CSR.
return MapEdges(graph, edge_arrays, node_maps, stream);

return ret;
}
};

Expand Down