diff --git a/tests/test_gpu_aamp_ostinato.py b/tests/test_gpu_aamp_ostinato.py
index 704c12262..098d7b7a5 100644
--- a/tests/test_gpu_aamp_ostinato.py
+++ b/tests/test_gpu_aamp_ostinato.py
@@ -36,11 +36,11 @@ def test_random_gpu_aamp_ostinato(runs):
     Ts = [rng.RNG.rand(n) for n in [64, 128, 256]]
 
     ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m)
-    comp_radius, comp_Ts_idx, comp_subseq_idx = gpu_aamp_ostinato(Ts, m)
+    cmp_radius, cmp_Ts_idx, cmp_subseq_idx = gpu_aamp_ostinato(Ts, m)
 
-    npt.assert_almost_equal(ref_radius, comp_radius)
-    npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
-    npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
+    npt.assert_allclose(cmp_radius, ref_radius, atol=1.5e-07)
+    npt.assert_allclose(cmp_Ts_idx, ref_Ts_idx, atol=1.5e-07)
+    npt.assert_allclose(cmp_subseq_idx, ref_subseq_idx, atol=1.5e-07)
 
 
 @pytest.mark.filterwarnings("ignore", category=NumbaPerformanceWarning)
@@ -54,11 +54,11 @@ def test_deterministic_gpu_aamp_ostinato(seed):
 
         for p in [1.0, 2.0, 3.0]:
             ref_radius, ref_Ts_idx, ref_subseq_idx = naive.aamp_ostinato(Ts, m, p=p)
-            comp_radius, comp_Ts_idx, comp_subseq_idx = gpu_aamp_ostinato(Ts, m, p=p)
+            cmp_radius, cmp_Ts_idx, cmp_subseq_idx = gpu_aamp_ostinato(Ts, m, p=p)
 
-            npt.assert_almost_equal(ref_radius, comp_radius)
-            npt.assert_almost_equal(ref_Ts_idx, comp_Ts_idx)
-            npt.assert_almost_equal(ref_subseq_idx, comp_subseq_idx)
+            npt.assert_allclose(cmp_radius, ref_radius, atol=1.5e-07)
+            npt.assert_allclose(cmp_Ts_idx, ref_Ts_idx, atol=1.5e-07)
+            npt.assert_allclose(cmp_subseq_idx, ref_subseq_idx, atol=1.5e-07)
 
 
 @pytest.mark.filterwarnings("ignore", category=NumbaPerformanceWarning)
@@ -77,8 +77,10 @@ def test_input_not_overwritten():
     gpu_aamp_ostinato(Ts_input, m)
     for i in range(len(Ts)):
         T_ref = Ts[i]
-        T_comp = Ts_input[i]
-        npt.assert_almost_equal(T_ref[np.isfinite(T_ref)], T_comp[np.isfinite(T_comp)])
+        T_cmp = Ts_input[i]
+        npt.assert_allclose(
+            T_cmp[np.isfinite(T_cmp)], T_ref[np.isfinite(T_ref)], atol=1.5e-07
+        )
 
 
 @pytest.mark.filterwarnings("ignore", category=NumbaPerformanceWarning)
@@ -88,18 +90,18 @@ def test_extract_several_consensus():
     # does not tamper with the original data.
     Ts = [rng.RNG.rand(n) for n in [64, 128]]
     Ts_ref = [T.copy() for T in Ts]
-    Ts_comp = [T.copy() for T in Ts]
+    Ts_cmp = [T.copy() for T in Ts]
 
     m = 20
 
     k = 2  # Get the first `k` consensus motifs
     for _ in range(k):
-        # Find consensus motif and its NN in each time series in Ts_comp
-        # Remove them from Ts_comp as well as Ts_ref, and assert that the
+        # Find consensus motif and its NN in each time series in Ts_cmp
+        # Remove them from Ts_cmp as well as Ts_ref, and assert that the
         # two time series are the same
-        radius, Ts_idx, subseq_idx = gpu_aamp_ostinato(Ts_comp, m)
-        consensus_motif = Ts_comp[Ts_idx][subseq_idx : subseq_idx + m].copy()
-        for i in range(len(Ts_comp)):
+        radius, Ts_idx, subseq_idx = gpu_aamp_ostinato(Ts_cmp, m)
+        consensus_motif = Ts_cmp[Ts_idx][subseq_idx : subseq_idx + m].copy()
+        for i in range(len(Ts_cmp)):
             if i == Ts_idx:
                 query_idx = subseq_idx
             else:
@@ -107,12 +109,14 @@ def test_extract_several_consensus():
 
             idx = np.argmin(
                 core.mass(
-                    consensus_motif, Ts_comp[i], normalize=False, query_idx=query_idx
+                    consensus_motif, Ts_cmp[i], normalize=False, query_idx=query_idx
                 )
             )
-            Ts_comp[i][idx : idx + m] = np.nan
+            Ts_cmp[i][idx : idx + m] = np.nan
             Ts_ref[i][idx : idx + m] = np.nan
 
-            npt.assert_almost_equal(
-                Ts_ref[i][np.isfinite(Ts_ref[i])], Ts_comp[i][np.isfinite(Ts_comp[i])]
+            npt.assert_allclose(
+                Ts_cmp[i][np.isfinite(Ts_cmp[i])],
+                Ts_ref[i][np.isfinite(Ts_ref[i])],
+                atol=1.5e-07,
             )