diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py
index 8fbce66d9ef..0a63164d7ca 100644
--- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py
+++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -128,32 +128,32 @@ def nx_graph_from_dataset(dataset_obj):
 def get_legacy_backend_wrapper(backend_name):
     """
     Returns a callable that wraps an algo function with either the default
-    dispatch decorator, or the "testing" decorator which unconditionally
-    dispatches.
+    dispatcher (which dispatches based on input graph type), or the "testing"
+    dispatcher (which autoconverts and unconditionally dispatches).
     This is only supported for NetworkX <3.2
     """
     backends.plugin_name = "cugraph"
     orig_dispatch = backends._dispatch
     testing_dispatch = backends.test_override_dispatch
 
-    # Testing with the networkx <3.2 dispatch mechanism is based on decorating
-    # networkx APIs. The decorator is either one that only uses a backend if
-    # the input graph type is for that backend (the default decorator), or the
-    # "testing" decorator, which unconditionally converts a graph type to the
-    # type needed by the backend then calls the backend. If the cugraph backend
-    # is specified, create a callable that decorates the benchmarked function
-    # with the testing decorator.
-    #
-    # Because both the default and testing decorators assume they are only
-    # applied once and do bookkeeping to ensure algos are not registered
-    # multiple times, the callable also clears bookkeeping so the decorators
-    # can be reapplied multiple times. This is obviously a hack and networkx
-    # >=3.2 makes this use case properly supported.
-    backends._registered_algorithms = {}
     if backend_name == "cugraph":
-        wrap_callable_for_dispatch = testing_dispatch(*args, **kwargs)
+        dispatch = testing_dispatch
     else:
-        wrap_callable_for_dispatch = orig_dispatch(*args, **kwargs)
+        dispatch = orig_dispatch
+
+    def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False):
+        # Networkx <3.2 registers functions when the dispatch decorator is
+        # applied (called) and errors if re-registered, so clear bookkeeping to
+        # allow it to be called repeatedly.
+        backends._registered_algorithms = {}
+        actual_func = dispatch(func)  # returns the func the dispatcher picks
+        def wrapper(*args, **kwargs):
+            retval = actual_func(*args, **kwargs)
+            if exhaust_returned_iterator:
+                retval = list(retval)
+            return retval
+
+        return wrapper
 
     return wrap_callable_for_dispatch
 
@@ -164,11 +164,13 @@ def get_backend_wrapper(backend_name):
     "backend" kwarg on it.
     This is only supported for NetworkX >= 3.2
     """
-
-    def wrap_callable_for_dispatch(func):
+    def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False):
         def wrapper(*args, **kwargs):
             kwargs["backend"] = backend_name
-            return func(*args, **kwargs)
+            retval = func(*args, **kwargs)
+            if exhaust_returned_iterator:
+                retval = list(retval)
+            return retval
 
         return wrapper
 
@@ -436,15 +438,9 @@ def bench_single_target_shortest_path_length(benchmark, graph_obj, backend_wrapp
     node = max(degrees, key=lambda t: t[1])[0]
     G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
 
-    # Ensure the benchmark time includes computation of each result from the
-    # returned generator
-    def run_generator(*args, **kwargs):
-        func = backend_wrapper(nx.single_target_shortest_path_length)
-        results = func(*args, **kwargs)
-        return list(results)
-
     result = benchmark.pedantic(
-        target=run_generator,
+        target=backend_wrapper(nx.single_target_shortest_path_length,
+                               exhaust_returned_iterator=True),
         args=(G,),
         kwargs=dict(
             target=node,
@@ -453,4 +449,7 @@ def run_generator(*args, **kwargs):
         iterations=iterations,
         warmup_rounds=warmup_rounds,
     )
+    # exhaust_returned_iterator=True forces the result to a list, but is not
+    # needed for this algo in NX 3.3+ which returns a dict instead of an
+    # iterator. Forcing to a list does not change the benchmark timing.
     assert type(result) is list