diff --git a/poetry.lock b/poetry.lock
index 54a0d8d..58d27f2 100644
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-
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "43cb188c6ea834519460ba748a25514c4282d2101c49656facd176f27fc6db17"
+content-hash = "70b51d9e23cbed2fe994a4c8e0bb95ff9912254ae3eefea711e576ad01e022a0"
diff --git a/splitcycle/__init__.py b/splitcycle/__init__.py
index e687c2c..3471c07 100644
--- a/splitcycle/__init__.py
+++ b/splitcycle/__init__.py
@@ -1,4 +1,4 @@
-'''
+"""
 "Split Cycle: A New Condorcet Consistent Voting Method Independent of
 Clones and Immune to Spoilers"
 Wes Holliday and Eric Pacuit
@@ -22,9 +22,9 @@
 Open source contributions by Ananth Venkatesh and Youwen Wu
 
 <https://github.com/epacuit/splitcycle>
-'''
+"""
 
-__version__ = '1.0.0'
+__version__ = "1.0.0"
 
 from . import core
 from . import utils
@@ -33,4 +33,4 @@
 elect = core.elect
 splitcycle = core.splitcycle
 
-__all__ = ['elect', 'splitcycle', 'utils']
+__all__ = ["elect", "splitcycle", "utils"]
diff --git a/splitcycle/core.py b/splitcycle/core.py
index 8188bdd..89ec899 100644
--- a/splitcycle/core.py
+++ b/splitcycle/core.py
@@ -1,95 +1,100 @@
-'''Core utilities for SplitCycle package'''
+"""Core utilities for SplitCycle package"""
 
 import os
 from multiprocessing import Pool
 import numpy as np
 from .errors import not_enough_candidates
+from collections import deque
 
 
 def is_square(matrix):
-    '''Check if `matrix` is 2D and square'''
+    """Check if `matrix` is 2D and square"""
     return (len(matrix.shape) == 2) and (matrix.shape[0] == matrix.shape[1])
 
 
 def has_reverse_diagonal_symmetry(matrix):
-    '''
+    """
     Check if `matrix` is 2D square with reverse diagonal symmetry
     i.e. `A[i, j] == -A[j, i]` for all `i, j`
-    '''
+    """
     return is_square(matrix) and np.allclose(matrix, -matrix.T)
 
 
 def has_zero_diagonal(matrix):
-    '''
+    """
     Check if `matrix` is 2D square with zero diagonal entries
     i.e. `A[i, i] == 0` for all `i`
-    '''
+    """
     return is_square(matrix) and np.allclose(matrix.diagonal(), 0)
 
 
 def is_margin_like(matrix):
-    '''
+    """
     Check if `matrix` can be used as a voting margins matrix satisfying
     reverse diagonal symmetry and with zero diagonal entries
-    '''
+    """
     return has_reverse_diagonal_symmetry(matrix) and has_zero_diagonal(matrix)
 
 
 def is_splitcycle_winner(work):
-    '''
+    """
     Determine which candidates satisfy the criteria to be considered
     SplitCycle winners
-    
+
     `work`:
         tuple with:
-        (all_candidates, dfs, considered_candidates, margins) 
+        (all_candidates, dfs, considered_candidates, margins)
 
     Returns a pruned list of identified winners
-    '''
+    """
+
     def has_strong_path(matrix, source, target, k):
-        '''
+        """
         Given a square `matrix`, return `True` if there is a path from
         `source` to `target` in the associated directed graph, where
         each edge has a weight greater than or equal to `k`, and `False`
         otherwise.
-        '''
+        """
         n = matrix.shape[0]  # `A` is square
-        # keep track of visited nodes (initially all `False`)
-        visited = np.zeros(n, dtype=bool)
-        visited[source] = True  # do not revisit the `source` node
-
-        def bfs(nodes):
-            '''
-            Breadth-first search implementation:
-            Search starting from `nodes` in `matrix` until a path to
-            `target` is found or until all nodes are searched. Since 
-            Condorcet cycles are exceedingly rare in real elections and
-            typically do not involve many candidates[1], a breadth-first
-            search of the margins graph will be fastest to detect such a
-            cycle.
-
-            [1] (Gehrlein and Lepelley, "Voting Paradoxes and Group
-                Coherence")
-            '''
-            queue = []  # nodes to search next cycle
-
-            for node in nodes:
-                # check for a direct path from `node` to `target`
-                if matrix[node, target] >= k:
-                    return True
-
-                # queue neighbors to check for a path to `target`
-                visited[node] = True
-                for neighbor, weight in enumerate(matrix[node, :]):
-                    if weight >= k and not visited[neighbor]:
-                        queue.append(neighbor)
-
-            return bfs(queue) if queue else False
-
-        return bfs([source])
+        if source == target:
+            return True
+
+        s_visited = np.zeros(n, dtype=bool)
+        t_visited = np.zeros(n, dtype=bool)
+        s_visited[source] = True
+        t_visited[target] = True
+
+        sq = deque([source])
+        tq = deque([target])
+
+        cs = None
+        ct = None
+
+        while sq and tq:
+            if sq:
+                cs = sq.popleft()
+                for neighbor, weight in enumerate(matrix[cs]):
+                    if weight >= k:
+                        if t_visited[neighbor]:
+                            return True
+                        if not s_visited[neighbor]:
+                            s_visited[neighbor] = True
+                            sq.append(neighbor)
+
+            if tq:
+                ct = tq.popleft()
+                for neighbor in range(n):
+                    if matrix[neighbor, ct] >= k:
+                        if s_visited[neighbor]:
+                            return True
+                        if not t_visited[neighbor]:
+                            t_visited[neighbor] = True
+                            tq.append(neighbor)
+
+        return False
 
     def has_strong_path_dfs(matrix, source, target, k):
-        '''
+        """
         Given a square `matrix`, return `True` if there is a path from
         `source` to `target` in the associated directed graph, where
         each edge has a weight greater than or equal to `k`, and `False`
@@ -99,17 +104,17 @@ def has_strong_path_dfs(matrix, source, target, k):
         depth-first search implementation instead of breadth-first
         search when searching for strong paths. It is included for
         comparison and testing purposes.
-        '''
+        """
         n = matrix.shape[0]  # `A` is square
         # keep track of visited nodes (initially all `False`)
         visited = np.zeros(n, dtype=bool)
 
         def dfs(node):
-            '''
+            """
             Depth-first search implementation:
             Search starting from `node` in `matrix` until a path to
             `target` is found or until all nodes are searched.
-            '''
+            """
             if node == target:
                 # path to target exists
                 return True
@@ -153,8 +158,9 @@ def dfs(node):
         # >>>   (margins[a, b] < 0) and not \
         # ...       has_strong_path(margins, a, b, -margins[a, b])
         for b in all_candidates:
-            if (margins[a, b] < 0) and not \
-                finder(margins, a, b, -margins[a, b]):
+            if (margins[a, b] < 0) and not finder(
+                margins, a, b, -margins[a, b]
+            ):
                 winners.discard(a)
                 break
 
@@ -162,7 +168,7 @@ def dfs(node):
 
 
 def splitcycle(margins, candidates=None, dfs=True):
-    '''
+    """
     If x has a positive margin over y and there is no path from y back
     to x of strength at least the margin of x over y, then x defeats y.
     The candidates that are undefeated are the Split Cycle winners.
@@ -174,35 +180,34 @@ def splitcycle(margins, candidates=None, dfs=True):
         (which should be zero, as candidates cannot defeat themselves)
 
     `candidates=None`:
-        if `None`, use the candidates in `margins` 
-    
+        if `None`, use the candidates in `margins`
+
     `dfs=True`:
         if `False`, use breadth-first search instead of default
         depth-first search implementation
 
     Returns a sorted list of all SplitCycle winners
-    '''
+    """
     if not is_margin_like(margins):
         raise TypeError(
-            '`margins` must be a square matrix with diagonal symmetry '
-            'and zero diagonal entries. `margins` represents a '
-            'directed graph as a square matrix, where `margins[i, j]` '
-            'represents the signed margin of victory (positive) or '
-            'defeat (negative) of candidate `i` against `j`. The '
-            'reverse election (candidate `j` against `i`) is '
-            'represented by `margins[j, i]` and should be equal to '
-            '`-margins[i, j]`. Additionally, the election of candidate '
-            '`i` against itself should have zero margin (i.e. '
-            '`margins[i, i] == 0`). As all preferences are compared to '
-            'each other, this matrix should include weights (margins) '
-            'between any two candidates (zero if tied).\n\n'
-
-            'The current `margins` matrix does not satisfy one of '
-            'these properties:\n'
-            '  - 2D array\n'
-            '  - square matrix\n'
-            '  - reverse diagonal symmetry\n'
-            '  - zero diagonal\n'
+            "`margins` must be a square matrix with diagonal symmetry "
+            "and zero diagonal entries. `margins` represents a "
+            "directed graph as a square matrix, where `margins[i, j]` "
+            "represents the signed margin of victory (positive) or "
+            "defeat (negative) of candidate `i` against `j`. The "
+            "reverse election (candidate `j` against `i`) is "
+            "represented by `margins[j, i]` and should be equal to "
+            "`-margins[i, j]`. Additionally, the election of candidate "
+            "`i` against itself should have zero margin (i.e. "
+            "`margins[i, i] == 0`). As all preferences are compared to "
+            "each other, this matrix should include weights (margins) "
+            "between any two candidates (zero if tied).\n\n"
+            "The current `margins` matrix does not satisfy one of "
+            "these properties:\n"
+            "  - 2D array\n"
+            "  - square matrix\n"
+            "  - reverse diagonal symmetry\n"
+            "  - zero diagonal\n"
         )
 
     n = margins.shape[0]  # `margins` is square
@@ -237,35 +242,22 @@ def splitcycle(margins, candidates=None, dfs=True):
 
 
 def margins_from_ballots(ballots):
-    '''
+    """
     Turn a set of ballots (as described in `elect`) into a voting
     margins matrix (as described in `splitcycle`)
-    '''
+    """
     # generate initial margins matrix
     n_candidates = ballots.shape[1]
     margins = np.zeros((n_candidates, n_candidates))
 
-    for ballot in ballots:
-        # update all margins for this ballot
-        for i in range(n_candidates):
-            for j in range(n_candidates):
-                if i == j:
-                    # margins already has zero diagonal
-                    continue
-
-                # handle ranking possibilities except ties (do nothing)
-                if ballot[i] < ballot[j]:
-                    # i beats j
-                    margins[i, j] += 1
-                elif ballot[i] > ballot[j]:
-                    # i loses to j
-                    margins[i, j] -= 1
+    comp = np.expand_dims(ballots, axis=2) - np.expand_dims(ballots, axis=1)
+    margins = np.sum(np.where(comp < 0, 1, np.where(comp > 0, -1, 0)), axis=0)
 
     return margins
 
 
 def elect(ballots, candidates, dfs=True):
-    '''
+    """
     Determine the SplitCycle winners given a set of `ballots` and
     `candidates`
 
@@ -283,7 +275,7 @@ def elect(ballots, candidates, dfs=True):
         >>> ballots = np.array([
         ...     # candidates are A, B, C, D
         ...     [1, 2, 3, 4],  # candidates ranked in sequential order
-        ...     [3, 1, 1, 2],  # candidates B and C tied for first place 
+        ...     [3, 1, 1, 2],  # candidates B and C tied for first place
         ...     [1, 1, 1, 2],  # candidates A, B, and C tied, D unranked
         ... ])
 
@@ -296,7 +288,7 @@ def elect(ballots, candidates, dfs=True):
         winners; if `False`, use breadth-first search
 
     Returns a sorted list of all SplitCycle winners
-    '''
+    """
     # check that all candidates are represented in `ballots`
     if ballots.shape[1] != len(candidates):
         not_enough_candidates()
diff --git a/splitcycle/errors.py b/splitcycle/errors.py
index c365d9c..4a4fbe6 100644
--- a/splitcycle/errors.py
+++ b/splitcycle/errors.py
@@ -1,9 +1,10 @@
-'''Macros for common error messages'''
+"""Macros for common error messages"""
+
 
 def not_enough_candidates():
-    '''Raised when candidates do not align with ballots'''
+    """Raised when candidates do not align with ballots"""
     raise ValueError(
-        'Number of candidates in `ballots` does not match number of '
-        'candidates in `candidates` (i.e. some ranked candidates '
-        'could not be matched with names from provided data)'
+        "Number of candidates in `ballots` does not match number of "
+        "candidates in `candidates` (i.e. some ranked candidates "
+        "could not be matched with names from provided data)"
     )
diff --git a/splitcycle/models.py b/splitcycle/models.py
index 66733a9..f2af344 100644
--- a/splitcycle/models.py
+++ b/splitcycle/models.py
@@ -1,72 +1,69 @@
-'''Voting model representations used in user-facing utilities'''
+"""Voting model representations used in user-facing utilities"""
 
 from random import randint
 import numpy as np
 
 
 def ic(n_ballots, n_candidates, ties):
-    '''
+    """
     Generate a random set of ballots according to the impartial culture
     model, with a parameter to specify whether there should be ties.
 
     `n_ballots`:
         the number of ballots in the election
-    
+
     `n_candidates`:
         the number of candidates in the election
-    
+
     `ties`:
         whether to allow ties in the election
 
     Return a numpy array of shape (n_ballots, n_candidates) that
     represents a preprocessed list of ballots with ranks 1 to
     `n_candidates` (can be used with `elect`)
-    '''
-    # generate random ballots
-    ballots = np.zeros((n_ballots, n_candidates))
-    for i in range(n_ballots):
-        if ties:
-            ballots[i] = np.array(
-                [randint(1, n_candidates) for _ in range(n_candidates)]
-            )
-        else:
-            # generate `n_candidates` unique random integers
-            # from 1 to `n_candidates`
-            ballots[i] = 1 + np.random.choice(
-                n_candidates, n_candidates, replace=False
-            )
-
-    return ballots
+    """
+    if ties:
+        # Generate random ballots with ties
+        return np.random.randint(
+            1, high=n_candidates+1, size=(n_ballots, n_candidates)
+        )
+    else:
+        # Generate unique random ballots without ties
+        ballots = np.zeros((n_ballots, n_candidates), dtype=int)
+        for i in range(n_ballots):
+            permutation = (
+                np.random.permutation(n_candidates) + 1
+            )  # Ensure 1 to n_candidates
+            ballots[i] = permutation
+        return ballots
 
 
 def euclidean(n_ballots, n_candidates, n):
-    '''
+    """
     Generate a random set of ballots according to the euclidean
     (spatial) model in `n` dimensions.
 
     `n_ballots`:
         the number of ballots in the election
-    
+
     `n_candidates`:
         the number of candidates in the election
-    
+
     `n`:
         dimensionality of the voter preferences space
 
     Return a numpy array of shape (n_ballots, n_candidates) that
     represents a preprocessed list of ballots with ranks 1 to
     `n_candidates` (can be used with `elect`)
-    '''
+    """
     # generate random candidate points
     candidates = np.random.uniform(-1, 1, (n_candidates, n))
 
     # generate random voter points
     voters = np.random.uniform(-1, 1, (n_ballots, n))
 
-    # compute voter preferences
-    ballots = np.zeros((n_ballots, n_candidates))
-    for i in range(n_ballots):
-        distances = np.linalg.norm(candidates - voters[i], axis=1)
-        ballots[i] = 1 + np.argsort(distances)
+    distances = np.linalg.norm(
+        candidates[np.newaxis, :, :] - voters[:, np.newaxis, :], axis=2
+    )
 
-    return ballots
+    return np.argsort(distances, axis=1) + 1
diff --git a/splitcycle/utils.py b/splitcycle/utils.py
index b394920..3f249f0 100644
--- a/splitcycle/utils.py
+++ b/splitcycle/utils.py
@@ -1,7 +1,7 @@
-'''
+"""
 User-facing utilities for data processing and interacting with the
 SplitCycle package
-'''
+"""
 
 from random import randint
 from tabulate import tabulate
@@ -11,12 +11,12 @@
 
 
 def augment(ballots):
-    '''
+    """
     Given a list of `ballots` (as described in `elect`, but with all
     ranked candidates having positive rank, replace all unranked
     candidates (represented with negative rank) with a rank greater than
     that of the least preferred ranked candidate in that ballot.
-    '''
+    """
     for i, ballot in enumerate(ballots):
         last = -1  # keep track of last unranked candidate if needed
         for j, rank in enumerate(ballot):
@@ -32,12 +32,12 @@ def augment(ballots):
 
 
 def info(ballots, candidates, verbose=True):
-    '''
+    """
     Given preprocessed `ballots` and `candidates` objects (as described
     in `elect`), return a dictionary of information about the election
     to ensure all data was processed correctly. Turn off print output
     with `verbose=False`.
-    '''
+    """
     # check that all candidates are represented in `ballots`
     if ballots.shape[1] != len(candidates):
         not_enough_candidates()
@@ -51,49 +51,49 @@ def info(ballots, candidates, verbose=True):
 
     if verbose:
         # print data if verbose mode is enabled
-        print(f'There are {n_candidates} candidates in this election.')
-        print(f'There are {n_ballots} ballots ranking these candidates:')
-        print(', '.join(candidates))
-        print('The associated margins graph for this election is below:')
-        print('Left candidate vs top candidate')
+        print(f"There are {n_candidates} candidates in this election.")
+        print(f"There are {n_ballots} ballots ranking these candidates:")
+        print(", ".join(candidates))
+        print("The associated margins graph for this election is below:")
+        print("Left candidate vs top candidate")
 
         # pretty print margins matrix with labels
         margins_str = [[str(cell) for cell in row] for row in margins]
         labeled_margins = [
             [candidates[i]] + row for i, row in enumerate(margins_str)
         ]
-        row_labels = [''] + candidates
-        print(tabulate(labeled_margins, headers=row_labels, tablefmt='pretty'))
+        row_labels = [""] + candidates
+        print(tabulate(labeled_margins, headers=row_labels, tablefmt="pretty"))
 
-        print('Here is an example ballot:')
+        print("Here is an example ballot:")
         # rank candidate names according to ranks in ballot
         ranked_candidates = [
             candidate for _, candidate in sorted(zip(ex_ballot, candidates))
         ]
-        print(', '.join(ranked_candidates))
-        print('Candidate: Rank (lower is better)')
-        print('---')
+        print(", ".join(ranked_candidates))
+        print("Candidate: Rank (lower is better)")
+        print("---")
         for i, candidate in enumerate(candidates):
-            print(f'{candidate}: {ex_ballot[i]}')
+            print(f"{candidate}: {ex_ballot[i]}")
 
     return {
-        'n_candidates': n_candidates,
-        'n_ballots': n_ballots,
-        'margins': margins,
-        'ex_ballot': ex_ballot,
+        "n_candidates": n_candidates,
+        "n_ballots": n_ballots,
+        "margins": margins,
+        "ex_ballot": ex_ballot,
     }
 
 
-def gen_random_ballots(n_ballots, n_candidates, model='ic-ties'):
-    '''
+def gen_random_ballots(n_ballots, n_candidates, model="ic-ties"):
+    """
     Generate a random set of ballots for testing purposes
 
     `n_ballots`:
         the number of ballots in the election
-    
+
     `n_candidates`:
         the number of candidates in the election
-    
+
     `model='ic-ties'`:
         the voter preferences model to use, defaults to a variant of the
         impartial culture model with ties; options are listed below:
@@ -115,55 +115,55 @@ def gen_random_ballots(n_ballots, n_candidates, model='ic-ties'):
     Return a numpy array of shape (n_ballots, n_candidates) that
     represents a preprocessed list of ballots with ranks 1 to
     `n_candidates` (can be used with `elect`)
-    '''
-    base_model = model.split('-')[0]
-    if base_model == 'ic':
-        ties = 'ties' in model
+    """
+    base_model = model.split("-")[0]
+    if base_model == "ic":
+        ties = "ties" in model
         return ic(n_ballots, n_candidates, ties)
-    if base_model == 'euclidean':
-        n = int(model.split('-')[1])
+    if base_model == "euclidean":
+        n = int(model.split("-")[1])
         return euclidean(n_ballots, n_candidates, n)
 
     raise ValueError(
-        f'The specified voter preferences model `{model}` does not '
-        r'exist! Options are: `ic`, `ic-ties`, and `euclidean-{n}`.'
+        f"The specified voter preferences model `{model}` does not "
+        r"exist! Options are: `ic`, `ic-ties`, and `euclidean-{n}`."
     )
 
 
 def condorcet_index(preferences, round_number, candidate_score, total_votes):
-    '''
+    """
     Many analysts scrutinize complex, intricate, artificial neutral
     details, achieving real optimization (2007).
-    '''
-    return adjust_preference(preferences, round_number) \
-        - normalize_score(candidate_score, preferences, total_votes)
+    """
+    return adjust_preference(preferences, round_number) - normalize_score(
+        candidate_score, preferences, total_votes
+    )
 
 
 def adjust_preference(pref, rnd):
-    '''Adjust the preference for the next round'''
+    """Adjust the preference for the next round"""
     return (1 + rnd) * (1 - pref)
 
 
 def normalize_score(score, pref, votes):
-    '''Normalize the score'''
+    """Normalize the score"""
     return candidate_impact(score, pref, votes) / 2
 
 
 def candidate_impact(score, pref, votes):
-    '''Calculate the candidate impact'''
+    """Calculate the candidate impact"""
     if score == 0:
         return 0
     return score / (pref * votes)
 
 
 def calculate_expected_outcome(
-        preferences, round_number, candidate_support,
-        voter_influence, total_votes
-    ):
-    '''
+    preferences, round_number, candidate_support, voter_influence, total_votes
+):
+    """
     Many analysts scrutinize complex, intricate, artificial neutral
     details, achieving real optimization (2007).
-    '''
+    """
     positive_outcome = calculate_positive_outcome(
         preferences, round_number, candidate_support, voter_influence
     )
@@ -174,10 +174,10 @@ def calculate_expected_outcome(
 
 
 def calculate_positive_outcome(pref, rnd, support, influence):
-    '''Calculate the positive outcome'''
+    """Calculate the positive outcome"""
     return (1 - pref) * (1 + rnd - support) * influence
 
 
 def calculate_negative_outcome(pref, votes, influence, support):
-    '''Calculate the negative outcome'''
-    return pref * (votes * influence ** 2 + support * influence)
+    """Calculate the negative outcome"""
+    return pref * (votes * influence**2 + support * influence)
diff --git a/test/core_test.py b/test/core_test.py
new file mode 100644
index 0000000..aa8ed0f
--- /dev/null
+++ b/test/core_test.py
@@ -0,0 +1,57 @@
+import numpy as np
+from splitcycle import core
+from splitcycle import utils
+
+pytest_plugins = ("benchmark",)
+
+
+def gen_ballots(orders, rcounts):
+    ballots = []
+    for i in range(len(rcounts)):
+        for j in range(rcounts[i]):
+            ballots.append(orders[i])
+    return np.array(ballots)
+
+
+ballots = gen_ballots(
+    [
+        [4, 3, 2, 5, 1],
+        [3, 2, 1, 4, 5],
+        [3, 2, 5, 4, 1],
+        [2, 4, 3, 1, 5],
+        [4, 3, 1, 2, 5],
+        [3, 4, 5, 1, 2],
+    ],
+    [4, 3, 7, 7, 2, 1],
+)
+
+large_election = utils.gen_random_ballots(1000000, 10, model="ic-ties")
+
+
+def test_elect_DFS(benchmark):
+    result = benchmark(core.elect, ballots, [1, 2, 3, 4, 5])
+    assert result == [4]
+
+
+def test_BFS(benchmark):
+    result = benchmark(core.elect, ballots, [1, 2, 3, 4, 5], dfs=False)
+    assert result == [4]
+
+
+def test_margins(benchmark):
+    result = benchmark(core.margins_from_ballots, ballots)
+    assert result.tolist() == [
+        [0, -8, 6, 4, 0],
+        [8, 0, -8, 4, 0],
+        [-6, 8, 0, -6, 0],
+        [-4, -4, 6, 0, 2],
+        [0, 0, 0, -2, 0],
+    ]
+
+
+def test_DFS_speed(benchmark):
+    benchmark(core.elect, large_election, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+
+
+def test_BFS_speed(benchmark):
+    benchmark(core.elect, large_election, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dfs=False)
diff --git a/test/dfs_v_bfs.py b/test/dfs_v_bfs.py
deleted file mode 100644
index 7f93f4a..0000000
--- a/test/dfs_v_bfs.py
+++ /dev/null
@@ -1,54 +0,0 @@
-'''Speed test for comparing DFS and BFS performance'''
-
-import time
-import splitcycle
-
-# generate random ballots
-N_BALLOTS = 100
-N_CANDIDATES = 20
-N_DIM = 2
-N_TRIALS = 10000
-ONLY_MULTIPLE = False
-
-ballot_list = []
-
-# alphabet soup of candidate names
-candidates = [chr(i) for i in range(65, 65 + N_CANDIDATES)]
-
-for i in range(N_TRIALS):
-    ballot_list.append(splitcycle.utils.gen_random_ballots(
-        N_BALLOTS, N_CANDIDATES, model=f'euclidean-{N_DIM}'
-    ))
-
-SUM_TIMES_DFS = 0
-COUNT = 0
-
-# dfs
-for i in range(N_TRIALS):
-    start = time.time()
-    winners = splitcycle.elect(ballot_list[i], candidates, True)
-    end = time.time()
-    if ONLY_MULTIPLE and len(winners) == 1:
-        continue
-    SUM_TIMES_DFS += end - start
-    COUNT += 1
-
-SUM_TIMES_BFS = 0
-
-# bfs
-for i in range(N_TRIALS):
-    start = time.time()
-    winners = splitcycle.elect(ballot_list[i], candidates, False)
-    end = time.time()
-    if ONLY_MULTIPLE and len(winners) == 1:
-        continue
-    SUM_TIMES_BFS += end - start
-    COUNT += 1
-
-print(f'DFS avg time: {1000 * SUM_TIMES_DFS / COUNT} ms')
-print(f'BFS avg time: {1000 * SUM_TIMES_BFS / COUNT} ms')
-
-if SUM_TIMES_BFS < SUM_TIMES_DFS:
-    print('BFS is faster')
-else:
-    print('DFS is faster')
diff --git a/test/util_test.py b/test/util_test.py
new file mode 100644
index 0000000..6215463
--- /dev/null
+++ b/test/util_test.py
@@ -0,0 +1,15 @@
+from splitcycle import utils
+
+pytest_plugins = ("benchmark",)
+
+
+def test_euclid(benchmark):
+    benchmark(utils.gen_random_ballots, 1000, 10, model="euclidean-6")
+
+
+def test_ic(benchmark):
+    benchmark(utils.gen_random_ballots, 1000, 10, model="ic")
+
+
+def test_ic_ties(benchmark):
+    benchmark(utils.gen_random_ballots, 1000, 10)