Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Use cuco::static_set in JSON tree algorithm #13928

Merged
merged 5 commits into from
Aug 28, 2023
Merged

Use cuco::static_set in JSON tree algorithm #13928

Changes from 1 commit
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
f69188a
use static_set instead of static_map
karthikeyann Aug 21, 2023
80fcf53
replace cuco::static_map with static_set in field name hash
karthikeyann Aug 21, 2023
d3ca496
remove unused variable name
karthikeyann Aug 21, 2023
2127bd8
Merge branch 'branch-23.10' into enh-json_use_static_set
karthikeyann Aug 25, 2023
34839d7
Merge branch 'branch-23.10' into enh-json_use_static_set
karthikeyann Aug 27, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 19 additions & 26 deletions cpp/src/io/json/json_tree.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
#include <cub/device/device_radix_sort.cuh>

#include <cuco/static_map.cuh>
PointKernel marked this conversation as resolved.
Show resolved Hide resolved
#include <cuco/static_set.cuh>
karthikeyann marked this conversation as resolved.
Show resolved Hide resolved

#include <thrust/binary_search.h>
#include <thrust/copy.h>
Expand Down Expand Up @@ -528,7 +529,6 @@ std::pair<rmm::device_uvector<size_type>, rmm::device_uvector<size_type>> hash_n
{
CUDF_FUNC_RANGE();
auto const num_nodes = parent_node_ids.size();
rmm::device_uvector<size_type> col_id(num_nodes, stream, mr);

// array of arrays
NodeIndexT const row_array_children_level = is_enabled_lines ? 1 : 2;
Expand Down Expand Up @@ -560,17 +560,6 @@ std::pair<rmm::device_uvector<size_type>, rmm::device_uvector<size_type>> hash_n
list_indices.begin());
}

using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
using hash_map_type =
cuco::static_map<size_type, size_type, cuda::thread_scope_device, hash_table_allocator_type>;

constexpr size_type empty_node_index_sentinel = -1;
hash_map_type key_map{compute_hash_table_size(num_nodes), // TODO reduce oversubscription
cuco::empty_key{empty_node_index_sentinel},
cuco::empty_value{empty_node_index_sentinel},
cuco::erased_key{-2},
hash_table_allocator_type{default_allocator<char>{}, stream},
stream.value()};
// path compression is not used since extra writes make all map operations slow.
auto const d_hasher = [node_level = node_levels.begin(),
node_type = node_type.begin(),
Expand Down Expand Up @@ -632,23 +621,27 @@ std::pair<rmm::device_uvector<size_type>, rmm::device_uvector<size_type>> hash_n
return node_id1 == node_id2;
};

constexpr size_type empty_node_index_sentinel = -1;
using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
using hasher_type = decltype(d_hashed_cache);

auto key_set = cuco::experimental::static_set{
cuco::experimental::extent{compute_hash_table_size(num_nodes)},
cuco::empty_key<cudf::size_type>{empty_node_index_sentinel},
d_equal,
cuco::experimental::linear_probing<1, hasher_type>{d_hashed_cache},
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Why using linear_probing here? Do we have other options?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

used the same probing used in distinct_count. What other probing options are there?

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@PointKernel?

Copy link
Member

@PointKernel PointKernel Aug 21, 2023 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

We can switch between linear_probing and double_hashing. In general, linear_probing delivers better performance for set/map and double_hashing is preferred for multiset/multimap.

hash_table_allocator_type{default_allocator<char>{}, stream},
stream.value()};

// insert and convert node ids to unique set ids
auto const num_inserted = thrust::count_if(
rmm::exec_policy(stream),
thrust::make_counting_iterator<size_type>(0),
thrust::make_counting_iterator<size_type>(num_nodes),
[d_hashed_cache,
d_equal,
view = key_map.get_device_mutable_view(),
uq_node_id = col_id.begin()] __device__(auto node_id) mutable {
auto it = view.insert_and_find(cuco::make_pair(node_id, node_id), d_hashed_cache, d_equal);
uq_node_id[node_id] = (it.first)->first.load(cuda::std::memory_order_relaxed);
return it.second;
});
auto nodes_itr = thrust::make_counting_iterator<size_type>(0);
auto const num_inserted = key_set.insert(nodes_itr, nodes_itr + num_nodes, stream.value());
karthikeyann marked this conversation as resolved.
Show resolved Hide resolved

auto const num_columns = num_inserted; // key_map.get_size() is not updated.
auto const num_columns = num_inserted;
rmm::device_uvector<size_type> unique_keys(num_columns, stream);
key_map.retrieve_all(unique_keys.begin(), thrust::make_discard_iterator(), stream.value());
rmm::device_uvector<size_type> col_id(num_nodes, stream, mr);
key_set.find_async(nodes_itr, nodes_itr + num_nodes, col_id.begin(), stream.value());
std::ignore = key_set.retrieve_all(unique_keys.begin(), stream.value());
ttnghia marked this conversation as resolved.
Show resolved Hide resolved

return {std::move(col_id), std::move(unique_keys)};
}
Expand Down
Loading