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Fixes some issues in plots
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-36
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‎bench/ndarray/jit-reduc-float64-plot.py

Lines changed: 101 additions & 36 deletions
Original file line numberDiff line numberDiff line change
@@ -16,20 +16,20 @@
1616
sizes = [1, 5, 10, 20, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120]
1717
sizes_GB = [n * 1000 * n * 1000 * 8 * 2 / 2**30 for n in sizes]
1818

19-
intel = False
2019
amd = True
20+
intel = False
2121
m2linux = False
2222

2323
# Load the data
2424
if amd:
2525
title_ = "AMD Ryzen 9 9800X3D (64 GB RAM)"
2626
title_ = "np.sum(((a ** 3 + np.sin(a * 2)) < c) & (b > 0), axis=1)"
2727

28-
create_clevel0 = [ 0.0325, 0.2709, 1.0339, 4.0489, 9.0849, 12.4154, 16.7818, 25.5946, 47.5691, 35.9919, 45.4295, 93.3075, 66.6529 ]
29-
compute_clevel0 = [ 0.0017, 0.0243, 0.0869, 0.3370, 0.7665, 1.0375, 1.3727, 1.7377, 2.1472, 2.6205, 3.0435, 18.5878, 28.0816 ]
28+
create_l0 = [ 0.0325, 0.2709, 1.0339, 4.0489, 9.0849, 12.4154, 16.7818, 25.5946, 47.5691, 35.9919, 45.4295, 93.3075, 66.6529 ]
29+
compute_l0 = [ 0.0017, 0.0243, 0.0869, 0.3370, 0.7665, 1.0375, 1.3727, 1.7377, 2.1472, 2.6205, 3.0435, 18.5878, 28.0816 ]
3030

31-
create_clevel0_ooc = [ 0.0305, 0.3371, 1.3249, 5.0602, 11.0410, 16.3685, 22.2012, 27.1348, 31.7409, 38.0690, 47.4424, 56.9335, 62.6965 ]
32-
compute_clevel0_ooc = [ 0.0019, 0.0243, 0.0885, 0.3434, 0.7761, 1.0724, 1.4082, 1.7373, 2.1827, 2.6124, 7.0940, 9.0734, 10.1089 ]
31+
create_l0_ooc = [ 0.0305, 0.3371, 1.3249, 5.0602, 11.0410, 16.3685, 22.2012, 27.1348, 31.7409, 38.0690, 47.4424, 56.9335, 62.6965 ]
32+
compute_l0_ooc = [ 0.0019, 0.0243, 0.0885, 0.3434, 0.7761, 1.0724, 1.4082, 1.7373, 2.1827, 2.6124, 7.0940, 9.0734, 10.1089 ]
3333

3434
create_LZ4_l1 = [ 0.0304, 0.2582, 1.0298, 3.9502, 8.8945, 11.9267, 16.3965, 20.2368, 24.6837, 29.3425, 36.2631, 42.1709, 48.0605, 52.3962, 61.5175, 68.6328, 80.1160, 85.4322, 97.1122, 106.9973, 114.8584 ]
3535
compute_LZ4_l1 = [ 0.0018, 0.0210, 0.0756, 0.3003, 0.6609, 0.8886, 1.1285, 1.4453, 1.7959, 2.1889, 2.6978, 3.1586, 3.4286, 3.9929, 4.4590, 5.3601, 5.6702, 6.4690, 6.9764, 7.8714, 8.6404 ]
@@ -51,43 +51,83 @@
5151

5252
elif intel:
5353
title_ = "Intel Core i9-13900K (32 GB RAM)"
54-
create_clevel0 = [ 0.1810, 0.3511, 1.1511, 4.4575, 10.3164, 17.4344, 24.4274, 37.7116, 36.6179, 53.7264 ]
55-
compute_clevel0 = [ 0.0045, 0.0133, 0.0506, 0.2086, 0.4603, 0.8689, 1.1458, 1.4150, 1.7656, 1.9475 ]
54+
create_l0 = [ 0.1810, 0.3511, 1.1511, 4.4575, 10.3164, 17.4344, 24.4274, 37.7116, 36.6179, 53.7264 ]
55+
compute_l0 = [ 0.0045, 0.0133, 0.0506, 0.2086, 0.4603, 0.8689, 1.1458, 1.4150, 1.7656, 1.9475 ]
56+
57+
create_l0_ooc = [0] * 10 # this crashed
58+
compute_l0_ooc = [0] * 10 # this crashed
5659

5760
create_LZ4_l1 = [ 0.1834, 0.3457, 1.1234, 4.3301, 10.0406, 16.9509, 22.1617, 26.3818, 32.4472, 39.3830, 41.9484, 52.6316 ]
5861
compute_LZ4_l1 = [ 0.0014, 0.0128, 0.0494, 0.1958, 0.4387, 0.8207, 1.0208, 1.2739, 1.5062, 1.7446, 2.1553, 2.4458 ]
5962

63+
create_LZ4_l1_ooc = [ 0.1222, 0.3705, 1.4912, 5.4410, 12.3593, 15.6122, 21.9754, 27.6554, 34.0044, 41.8007, 49.8841, 58.0062, 58.1169, 76.9802, 79.2385, 99.9344, 111.9739, 126.4542, 142.3726 ]
64+
compute_LZ4_l1_ooc = [ 0.0032, 0.0167, 0.1319, 0.3058, 0.7025, 0.9334, 1.2293, 1.5071, 1.8350, 2.4390, 2.8756, 3.3668, 3.8927, 4.5542, 5.0557, 6.2732, 6.5550, 7.4660, 8.0298 ]
65+
6066
create_ZSTD_l1 = [ 0.0362, 0.3734, 1.2009, 4.5362, 10.3706, 18.7104, 23.1148, 27.6572, 33.7207, 41.0326, 44.2322, 54.9467 ]
6167
compute_ZSTD_l1 = [ 0.0028, 0.0193, 0.0799, 0.2226, 0.4983, 0.9072, 1.1624, 1.4375, 1.8162, 2.0918, 2.5067, 2.7760 ]
6268

63-
create_numpy = [ 0.0046, 0.1160, 0.4327, 1.7166, 3.8661, 7.5005, 14.1090, 18.6720, 64.2425, 108.4532, 529.1393, 962.1662 ]
64-
compute_numpy = [ 0.0240, 0.1920, 0.7217, 2.9316, 6.5893, 14.0353, 47.4275, 99.0893, 187.8040, 202.3973, 460.5915, 551.2776 ]
69+
create_ZSTD_l1_ooc = [ 0.0547, 0.4150, 1.5916, 5.7187, 13.3500, 16.8552, 23.2673, 29.2232, 35.5580, 44.3726, 52.4742, 59.8893, 61.3350, 80.1619, 83.0139, 103.8481, 117.3893, 128.6241, 138.2671 ]
70+
compute_ZSTD_l1_ooc = [ 0.0031, 0.0213, 0.1465, 0.3784, 0.8567, 1.2848, 1.7557, 1.9248, 2.3045, 3.3080, 3.6730, 4.2439, 5.4268, 6.5462, 6.6983, 8.2491, 8.9797, 9.8748, 9.9348 ]
71+
72+
create_numpy = [ 0.0035, 0.0784, 0.3107, 1.2150, 2.7350, 3.7511 ]
73+
compute_numpy = [ 0.0327, 0.3483, 1.3650, 5.4224, 14.3476, 80.2920 ]
74+
75+
create_numpy_jit = [ 0.0035, 0.0785, 0.3088, 1.2377, 2.8435, 6.7555, 11.3731 ]
76+
compute_numpy_jit = [ 0.0043, 0.0164, 0.0564, 0.2203, 0.4830, 0.6645, 0.8571 ]
6577

6678
elif m2linux:
6779
title_ = "MacBook Air M2 (24 GB RAM)"
68-
create_LZ4_l1 = [0.021555185317993164, 0.2862977981567383, 0.8696625232696533, 3.4979920387268066, 8.235799789428711, 13.708781242370605, 20.74394702911377, 33.23137378692627]
69-
compute_LZ4_l1 = [0.0033464431762695312, 0.03627762794494629, 0.14009513854980468, 0.5438736915588379, 1.2493964672088622, 2.194223642349243, 3.5851136207580567, 5.067658472061157]
7080

71-
create_numpy = [0.0016903877258300781, 0.04910874366760254, 0.18264532089233398, 0.7124006748199463, 1.8350563049316406, 8.877023935317993, 101.2457287311554, 196.21723294258118]
72-
compute_numpy = [0.003887462615966797, 0.026979732513427734, 0.11047358512878418, 0.4213367462158203, 0.9288184165954589, 1.6470709323883057, 5.5601390361785885, 9.401740503311157]
81+
create_l0 = [ 0.0444, 0.7885, 2.3555, 8.4279, 18.9511, 27.8466, 38.0111, 48.6637 ]
82+
compute_l0 = [ 0.0030, 0.0503, 0.1845, 0.7183, 1.5504, 8.5181, 11.1162, 48.3423 ]
83+
84+
create_l0_ooc = [ 0.1204, 0.8043, 2.6619, 8.9401, 21.9047, 29.0938, 36.9753, 45.9740 ]
85+
compute_l0_ooc = [ 0.0038, 0.0733, 0.2713, 4.6407, 9.1592, 11.6989, 14.0608, 22.7236 ]
86+
87+
create_LZ4_l1 = [ 0.0435, 0.7986, 2.3867, 8.5209, 18.8881, 25.9945, 35.0841, 45.7843, 54.8631, 67.5644, 79.7407, 90.9488, 105.7526, 121.2143, 134.6952, 161.6108, 185.0409 ]
88+
compute_LZ4_l1 = [ 0.0032, 0.0509, 0.1880, 0.7155, 1.6209, 2.2104, 2.9327, 5.1928, 6.0526, 7.4635, 8.9645, 10.5490, 12.0207, 13.7969, 15.8644, 19.2798, 21.3784 ]
89+
90+
create_LZ4_l1_ooc = [ 0.2557, 0.7487, 2.4254, 7.8367, 19.1367, 25.1097, 31.3328, 39.4257, 52.3823, 62.2994, 73.4805, 84.3078, 96.3005, 110.9688, 118.3864, 159.4544, 157.3727 ]
91+
compute_LZ4_l1_ooc = [ 0.0037, 0.0590, 0.2268, 0.8837, 1.8008, 2.3744, 3.0909, 4.2624, 5.1138, 6.5483, 7.5345, 8.9750, 9.8907, 11.4285, 13.2415, 22.4300, 141.6707 ]
92+
93+
create_ZSTD_l1 = [ 0.0423, 0.8595, 2.5674, 8.9603, 19.7700, 27.7205, 36.6830, 47.5384, 59.1740, 71.9198, 84.9254, 94.0010, 108.5841, 124.1261, 138.5614, 164.8593, 182.1642 ]
94+
compute_ZSTD_l1 = [ 0.0039, 0.0744, 0.2804, 1.0776, 2.3171, 3.4378, 4.6290, 6.7199, 8.3764, 9.3376, 11.0436, 12.8701, 15.1084, 17.1096, 19.1325, 23.3127, 25.9506 ]
95+
96+
create_ZSTD_l1_ooc = [ 0.1132, 0.7658, 2.5113, 8.0048, 19.8691, 26.8448, 35.4817, 43.4521, 58.6422, 64.7345, 75.8568, 85.5629, 99.6076, 114.3310, 121.0300, 158.5408, 161.0909 ]
97+
compute_ZSTD_l1_ooc = [ 0.0043, 0.0813, 0.3313, 1.4464, 2.9211, 4.1365, 5.4587, 7.1266, 7.3236, 9.1663, 9.9776, 11.6081, 13.7075, 15.1375, 16.8231, 21.4002, 23.9236 ]
98+
99+
create_numpy = [ 0.0020, 0.0550, 0.2232, 0.9468, 2.1856, 2.9516, 12.0596, 27.6355 ]
100+
compute_numpy = [ 0.0128, 0.3144, 1.3380, 5.5749, 38.6210, 70.7284, 164.0349, 325.4615 ]
101+
102+
create_numpy_jit = [ 0.0024, 0.0603, 0.2329, 0.9657, 2.1673, 15.5171, 20.2344, 23.9815 ]
103+
compute_numpy_jit = [ 0.0050, 0.0393, 0.1333, 0.5318, 1.1473, 3.8321, 6.4264, 45.0717 ]
73104

74105
else:
75106
title_ = "Mac Mini M4 Pro (24 GB RAM)"
76107

77-
create_numpy = [ 0.0024, 0.0686, 0.2857, 1.1800, 3.5006, 13.7092, 21.9491, 30.5237, 101.3553, 363.5005, 446.5876, 1509.1826 ]
78-
compute_numpy = [ 0.0046, 0.1173, 0.5066, 2.0908, 5.6268, 13.0679, 16.7926, 20.9192, 25.5899, 34.5382, 46.0664, 1083.7046 ]
108+
create_numpy = [ 0.0016, 0.0415, 0.1631, 0.8974, 1.9819, 2.3129, 9.7300 ]
109+
compute_numpy = [ 0.0089, 0.2128, 0.9457, 5.7644, 36.5153, 63.8844, 137.9539 ]
79110

80-
create_numpy_jit = [ 0.0024, 0.0686, 0.2857, 1.1800, 3.5006, 13.7092, 21.9491, 30.5237, 101.3553, 363.5005, 446.5876, 1509.1826 ]
81-
compute_numpy_jit = [ ]
111+
create_numpy_jit = [ 0.0018, 0.0436, 0.1676, 0.7349, 1.6885, 12.5894, 16.5044, 20.0384 ]
112+
compute_numpy_jit = [ 0.0038, 0.0205, 0.0642, 0.2606, 0.5486, 3.3116, 5.9220, 29.1374 ]
82113

83-
create_clevel0 = [ 0.0321, 0.6742, 1.6750, 5.1412, 12.4538, 20.6695, 28.0185, 34.7422, 41.3935, 50.4275, 59.5572, 71.3740 ]
84-
compute_clevel0 = [ 0.0017, 0.0224, 0.0761, 0.2439, 0.5827, 0.9816, 1.2265, 6.8712, 9.6391, 11.2875, 13.1953, 15.4047 ]
114+
create_l0 = [ 0.0344, 0.5770, 1.8655, 5.8634, 15.5161, 21.1114, 26.4065, 32.8173 ]
115+
compute_l0 = [ 0.0021, 0.0300, 0.0936, 0.3474, 0.7027, 8.4870, 11.1171, 31.2273 ]
85116

86-
create_LZ4_l1 = [ 0.0316, 0.6974, 1.6957, 5.2077, 11.8975, 20.2346, 26.6419, 32.7686, 38.2088, 47.5221, 55.7224, 66.6138 ]
87-
compute_LZ4_l1 = [ 0.0019, 0.0234, 0.0763, 0.2421, 0.5533, 0.9384, 1.2476, 1.5299, 1.8564, 2.1836, 2.5633, 2.9245 ]
117+
create_l0_ooc = [ 0.0614, 0.5894, 1.9954, 6.4042, 16.9128, 21.5730, 26.9225, 33.8051 ]
118+
compute_l0_ooc = [ 0.0027, 0.0427, 0.1650, 0.6768, 5.7428, 7.7228, 8.2640, 14.4505 ]
88119

89-
create_ZSTD_l1 = [ 0.0354, 0.7105, 1.7217, 5.2663, 12.7385, 20.3693, 28.1353, 33.0310, 40.3843, 50.2020, 58.0643, 69.5190 ]
90-
compute_ZSTD_l1 = [ 0.0021, 0.0225, 0.0773, 0.2630, 0.8735, 1.0087, 1.9553, 1.5670, 3.1986, 3.3499, 4.0728, 4.6287 ]
120+
create_LZ4_l1 = [ 0.0361, 0.5804, 1.9389, 6.0536, 15.1991, 19.7225, 24.0663, 30.4482, 42.4730, 48.8970, 57.3124, 66.8990, 76.1380, 88.6604, 93.2565, 124.5175, 119.0430, 154.8972, 148.1766 ]
121+
compute_LZ4_l1 = [ 0.0021, 0.0303, 0.1018, 0.3595, 0.7678, 1.0191, 1.3130, 1.7165, 2.0468, 2.6400, 3.1438, 3.6971, 3.9760, 4.6626, 5.2315, 6.1437, 6.7120, 8.3231, 8.8490 ]
122+
123+
create_LZ4_l1_ooc = [ 0.1762, 0.5815, 1.9408, 6.6289, 16.4400, 20.2538, 25.0138, 31.3007, 43.0660, 49.9801, 58.6067, 67.7645, 77.3800, 89.2128, 95.8529, 126.9347, 122.4465 ]
124+
compute_LZ4_l1_ooc = [ 0.0027, 0.0379, 0.1470, 0.5730, 1.0309, 1.3231, 1.7013, 2.6991, 3.0829, 3.7675, 4.2371, 4.9816, 5.3848, 6.0163, 6.8497, 12.3994, 12.0842 ]
125+
126+
create_ZSTD_l1 = [ 0.0366, 0.5756, 1.9573, 6.1188, 15.5850, 19.9960, 24.9155, 30.7977, 42.7155, 49.7633, 58.7918, 67.7275, 77.1892, 88.9606, 116.8549, 180.0778, 140.9286, 209.7236, 1106.0708 ]
127+
compute_ZSTD_l1 = [ 0.0028, 0.0398, 0.1383, 0.5335, 1.0828, 1.6127, 2.2377, 2.7517, 3.2811, 4.3737, 4.6748, 5.3744, 6.2328, 6.6981, 9.7671, 12.4342, 29.5562, 37.8933, 19.2722 ]
128+
129+
create_ZSTD_l1_ooc = [ 0.1724, 0.6122, 2.0364, 6.4511, 16.3306, 20.9426, 25.9797, 32.1823, 45.2271, 51.2425, 59.8028, 68.1794, 78.3132, 90.4755, 96.8384, 129.1539, 125.2803 ]
130+
compute_ZSTD_l1_ooc = [ 0.0030, 0.0452, 0.1687, 0.6854, 1.2524, 1.8355, 2.5684, 3.2852, 3.9175, 5.0215, 5.3327, 6.0550, 6.9507, 7.4801, 8.4181, 10.1903, 11.7509 ]
91131

92132

93133
# Plot the data. There will be 2 plots: one for create times and another for compute times
@@ -96,9 +136,9 @@
96136
# Create the create times plot
97137
fig_create = go.Figure()
98138
fig_create.add_trace(
99-
go.Scatter(x=sizes_GB, y=create_clevel0, mode='lines+markers', name=labels[0]))
139+
go.Scatter(x=sizes_GB, y=create_l0, mode='lines+markers', name=labels[0]))
100140
fig_create.add_trace(
101-
go.Scatter(x=sizes_GB, y=create_clevel0_ooc, mode='lines+markers', name=labels[0] + "(ooc)"))
141+
go.Scatter(x=sizes_GB, y=create_l0_ooc, mode='lines+markers', name=labels[0] + "(ooc)"))
102142
fig_create.add_trace(
103143
go.Scatter(x=sizes_GB, y=create_LZ4_l1, mode='lines+markers', name=labels[1]))
104144
fig_create.add_trace(
@@ -113,14 +153,14 @@
113153

114154
# Create the compute times plot
115155
# Calculate the maximum y1 value
116-
y1_max = max(max(compute_clevel0), max(compute_clevel0_ooc), max(compute_LZ4_l1), max(compute_LZ4_l1_ooc),
156+
y1_max = max(max(compute_l0), max(compute_l0_ooc), max(compute_LZ4_l1), max(compute_LZ4_l1_ooc),
117157
max(compute_ZSTD_l1), max(compute_ZSTD_l1_ooc), max(compute_numpy), max(compute_numpy_jit))
118158

119159
fig_compute = go.Figure()
120160
fig_compute.add_trace(
121-
go.Scatter(x=sizes_GB, y=compute_clevel0, mode='lines+markers', name=labels[0]))
161+
go.Scatter(x=sizes_GB, y=compute_l0, mode='lines+markers', name=labels[0]))
122162
fig_compute.add_trace(
123-
go.Scatter(x=sizes_GB, y=compute_clevel0_ooc, mode='lines+markers', name=labels[0] + "(ooc)"))
163+
go.Scatter(x=sizes_GB, y=compute_l0_ooc, mode='lines+markers', name=labels[0] + "(ooc)"))
124164
fig_compute.add_trace(
125165
go.Scatter(x=sizes_GB, y=compute_LZ4_l1, mode='lines+markers', name=labels[1]))
126166
fig_compute.add_trace(
@@ -133,21 +173,46 @@
133173
fig_compute.add_trace(go.Scatter(x=sizes_GB, y=compute_numpy_jit, mode='lines+markers', name=labels[4]))
134174
fig_compute.update_layout(title=f'Compute times ({title_})', xaxis_title='Size (GB)', yaxis_title='Time (s)')
135175

136-
# Add a vertical line at 64 GB
137-
y1_max = 35
138-
fig_compute.add_shape(
139-
type="line", x0=64, y0=0, x1=64, y1=y1_max,
140-
line=dict(color="Gray", width=2, dash="dot"),
141-
)
142176
if amd:
177+
# Add a vertical line at 64 GB
178+
y1_max = 35
179+
fig_compute.add_shape(
180+
type="line", x0=64, y0=0, x1=64, y1=y1_max,
181+
line=dict(color="Gray", width=2, dash="dot"),
182+
)
143183
fig_compute.add_annotation(x=64, y=y1_max * .9, text="64 GB", showarrow=True, arrowhead=2, ax=40, ay=0)
184+
elif m2linux:
185+
# Add a vertical line at 24 GB
186+
y1_max = 100
187+
fig_compute.add_shape(
188+
type="line", x0=24, y0=0, x1=24, y1=y1_max,
189+
line=dict(color="Gray", width=2, dash="dot"),
190+
)
191+
fig_compute.add_annotation(x=24, y=y1_max * .9, text="24 GB", showarrow=True, arrowhead=2, ax=40, ay=0)
192+
elif intel:
193+
# Add a vertical line at 32 GB
194+
y1_max = 50
195+
fig_compute.add_shape(
196+
type="line", x0=32, y0=0, x1=32, y1=y1_max,
197+
line=dict(color="Gray", width=2, dash="dot"),
198+
)
199+
fig_compute.add_annotation(x=32, y=y1_max * .9, text="32 GB", showarrow=True, arrowhead=2, ax=40, ay=0)
200+
else:
201+
# Add a vertical line at 24 GB
202+
y1_max = 35
203+
fig_compute.add_shape(
204+
type="line", x0=24, y0=0, x1=24, y1=y1_max,
205+
line=dict(color="Gray", width=2, dash="dot"),
206+
)
207+
fig_compute.add_annotation(x=24, y=y1_max * .9, text="24 GB", showarrow=True, arrowhead=2, ax=40, ay=0)
208+
144209

145210
# Show the plots
146211
fig_create.show()
147212
fig_compute.show()
148213
else:
149214
plt.figure()
150-
plt.plot(sizes_GB, create_clevel0, "o-", label=labels[0])
215+
plt.plot(sizes_GB, create_l0, "o-", label=labels[0])
151216
plt.plot(sizes_GB, create_LZ4_l1, "o-", label=labels[1])
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plt.plot(sizes_GB, create_ZSTD_l1, "o-", label=labels[2])
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plt.plot(sizes_GB, create_numpy_jit, "o-", label=labels[3])
@@ -157,7 +222,7 @@
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plt.legend()
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# Now, the compute times
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plt.figure()
160-
plt.plot(sizes_GB, compute_clevel0, "o-", label=labels[0])
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plt.plot(sizes_GB, compute_l0, "o-", label=labels[0])
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plt.plot(sizes_GB, compute_LZ4_l1, "o-", label=labels[1])
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plt.plot(sizes_GB, compute_ZSTD_l1, "o-", label=labels[2])
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plt.plot(sizes_GB, compute_numpy, "o-", label=labels[3])

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