diff --git a/cpp/bench/CMakeLists.txt b/cpp/bench/CMakeLists.txt
index 99606dd2e9..ccbf7b3ca8 100644
--- a/cpp/bench/CMakeLists.txt
+++ b/cpp/bench/CMakeLists.txt
@@ -81,6 +81,7 @@ if(BUILD_BENCH)
     bench/distance/distance_l1.cu
     bench/distance/distance_unexp_l2.cu
     bench/distance/fused_l2_nn.cu
+    bench/spatial/sparse_l2_nn.cu
     bench/distance/kernels.cu
     bench/main.cpp
     OPTIONAL
diff --git a/cpp/bench/distance/sparse_l2_nn.cu b/cpp/bench/distance/sparse_l2_nn.cu
new file mode 100644
index 0000000000..a505bcc996
--- /dev/null
+++ b/cpp/bench/distance/sparse_l2_nn.cu
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+
+#include <common/benchmark.hpp>
+#include <limits>
+#include <raft/distance/sparse_l2_nn.cuh>
+#include <raft/handle.hpp>
+#include <raft/linalg/norm.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#if defined RAFT_NN_COMPILED
+#include <raft/spatial/knn/specializations.hpp>
+#endif
+
+namespace raft::bench::spatial::sparse {
+
+// Introduce various sparsity patterns
+enum SparsityPattern {
+  checkerboard    = 0,
+  checkerboard_4  = 1,
+  checkerboard_64 = 2,
+  all_true        = 3,
+  all_false       = 4
+};
+
+struct sparse_l2_nn_inputs {
+  int m, n, k, num_groups;
+  SparsityPattern pattern;
+};  // struct sparse_l2_nn_inputs
+
+__global__ void init_adj(
+  int m, int n, int num_groups, SparsityPattern pattern, bool* adj, int* group_idxs)
+{
+  for (int i = blockIdx.y * blockDim.y + threadIdx.y; i < num_groups; i += blockDim.y * gridDim.y) {
+    for (int j = blockIdx.x * blockDim.x + threadIdx.x; j < m; j += blockDim.x * gridDim.x) {
+      switch (pattern) {
+        case checkerboard: adj[i * m + j] = (i + j) % 2; break;
+        case checkerboard_4: adj[i * m + j] = (i + (j / 4)) % 2; break;
+        case checkerboard_64: adj[i * m + j] = (i + (j / 64)) % 2; break;
+        case all_true: adj[i * m + j] = true; break;
+        case all_false: adj[i * m + j] = false; break;
+        default: assert(false && "unknown pattern");
+      }
+    }
+  }
+  // Each group is of size n / num_groups.
+  //
+  // - group_idxs[j] indicates the start of group j + 1 (i.e. is the inclusive
+  // scan of the group lengths)
+  //
+  // - The first group always starts at index zero, so we do not store it.
+  //
+  // - The group_idxs[num_groups - 1] should always equal n.
+
+  if (blockIdx.y == 0 && threadIdx.y == 0) {
+    for (int j = blockIdx.x * blockDim.x + threadIdx.x; j < num_groups;
+         j += blockDim.x * gridDim.x) {
+      group_idxs[j] = (j + 1) * (n / num_groups);
+    }
+    group_idxs[num_groups - 1] = n;
+  }
+}
+
+template <typename T>
+struct sparse_l2_nn : public fixture {
+  sparse_l2_nn(const sparse_l2_nn_inputs& p)
+    : params(p),
+      out(p.m, stream),
+      x(p.m * p.k, stream),
+      y(p.n * p.k, stream),
+      xn(p.m, stream),
+      yn(p.n, stream),
+      adj(p.m * p.num_groups, stream),
+      group_idxs(p.num_groups, stream),
+      workspace(p.m, stream)
+  {
+    raft::handle_t handle{stream};
+    raft::random::RngState r(123456ULL);
+
+    uniform(handle, r, x.data(), p.m * p.k, T(-1.0), T(1.0));
+    uniform(handle, r, y.data(), p.n * p.k, T(-1.0), T(1.0));
+    raft::linalg::rowNorm(xn.data(), x.data(), p.k, p.m, raft::linalg::L2Norm, true, stream);
+    raft::linalg::rowNorm(yn.data(), y.data(), p.k, p.n, raft::linalg::L2Norm, true, stream);
+    raft::distance::initialize<T, cub::KeyValuePair<int, T>, int>(
+      handle, out.data(), p.m, std::numeric_limits<T>::max(), op);
+
+    dim3 block(32, 32);
+    dim3 grid(10, 10);
+    init_adj<<<grid, block, 0, stream>>>(
+      p.m, p.n, p.num_groups, p.pattern, adj.data(), group_idxs.data());
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  void run_benchmark(::benchmark::State& state) override
+  {
+    loop_on_state(state, [this]() {
+      // It is sufficient to only benchmark the L2-squared metric
+      raft::distance::sparseL2NN<T, cub::KeyValuePair<int, T>, int>(out.data(),
+                                                                    x.data(),
+                                                                    y.data(),
+                                                                    xn.data(),
+                                                                    yn.data(),
+                                                                    adj.data(),
+                                                                    group_idxs.data(),
+                                                                    params.num_groups,
+                                                                    params.m,
+                                                                    params.n,
+                                                                    params.k,
+                                                                    (void*)workspace.data(),
+                                                                    op,
+                                                                    pairRedOp,
+                                                                    false,
+                                                                    false,
+                                                                    stream);
+    });
+  }
+
+ private:
+  sparse_l2_nn_inputs params;
+  rmm::device_uvector<T> x, y, xn, yn;
+  rmm::device_uvector<bool> adj;
+  rmm::device_uvector<int> group_idxs;
+  rmm::device_uvector<cub::KeyValuePair<int, T>> out;
+  rmm::device_uvector<int> workspace;
+  raft::distance::KVPMinReduce<int, T> pairRedOp;
+  raft::distance::MinAndDistanceReduceOp<int, T> op;
+};  // struct SparseL2NN
+
+// TODO: Consider thinning the list of benchmark cases..
+const std::vector<sparse_l2_nn_inputs> sparse_l2_nn_input_vecs = {
+  // Very fat matrices...
+  {32, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {64, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {128, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {256, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {512, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {1024, 16384, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 32, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 64, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 128, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 256, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 512, 16384, 32, SparsityPattern::checkerboard},
+  {16384, 1024, 16384, 32, SparsityPattern::checkerboard},
+
+  // Representative matrices...
+  {16384, 16384, 32, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 64, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 128, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 256, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 512, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 1024, 32, SparsityPattern::checkerboard},
+  {16384, 16384, 16384, 32, SparsityPattern::checkerboard},
+
+  {16384, 16384, 32, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 64, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 128, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 256, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 512, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 1024, 32, SparsityPattern::checkerboard_4},
+  {16384, 16384, 16384, 32, SparsityPattern::checkerboard_4},
+
+  {16384, 16384, 32, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 64, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 128, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 256, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 512, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 1024, 32, SparsityPattern::checkerboard_64},
+  {16384, 16384, 16384, 32, SparsityPattern::checkerboard_64},
+};
+
+RAFT_BENCH_REGISTER(sparse_l2_nn<float>, "", sparse_l2_nn_input_vecs);
+// Do not benchmark double.
+
+}  // namespace raft::bench::spatial::sparse