These results were run on an x86_64 Intel CPU to profile the performance of BurntSushi's memchr crate.
| name |
unit |
rust |
libc |
fallback |
naive |
| memchr1/huge/never |
μs |
10.957 |
10.817 |
48.203 |
356.29 |
| memchr1/huge/rare |
μs |
12.678 |
12.136 |
49.868 |
361.04 |
| memchr1/huge/uncommon |
μs |
107.72 |
98.615 |
203.96 |
416.35 |
| memchr1/huge/common |
μs |
285.06 |
368.47 |
846.11 |
702.37 |
| memchr1/small/never |
ns |
11.446 |
11.264 |
53.071 |
410.55 |
| memchr1/small/rare |
ns |
18.311 |
16.299 |
60.124 |
409.72 |
| memchr1/small/uncommon |
ns |
59.011 |
59.781 |
102.29 |
414.94 |
| memchr1/small/common |
ns |
288.42 |
295.03 |
301.34 |
491.56 |
| memchr1/tiny/never |
ns |
5.0710 |
4.7868 |
9.4404 |
44.495 |
| memchr1/tiny/rare |
ns |
7.7046 |
8.5632 |
13.021 |
46.659 |
| memchr1/tiny/uncommon |
ns |
25.900 |
24.364 |
39.374 |
55.385 |
| memchr1/tiny/common |
ns |
61.185 |
68.426 |
67.397 |
71.847 |
| memchr1/empty/never |
ns |
1.1142 |
2.5708 |
3.0289 |
3.5119 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
rust = np.array([10.957, 12.678, 107.72, 285.06, 11.446, 18.311, 59.011, 288.42, 5.0710, 7.7046, 25.900, 61.185, 1.1142])
libc = np.array([10.817, 12.136, 98.615, 368.47, 11.264, 16.299, 59.781, 295.03, 4.7868, 8.5632, 24.364, 68.426, 2.5708])
fallback = np.array([48.203, 49.868, 203.96, 846.11, 53.071, 60.124, 102.29, 301.34, 9.4404, 13.021, 39.374, 67.397, 3.0289])
naive = np.array([356.29, 361.04, 416.35, 702.37, 410.55, 409.72, 414.94, 491.56, 44.495, 46.659, 55.385, 71.847, 3.5119])
index = ["huge/never (μs)", "huge/rare (μs)", "huge/uncommon (μs)", "huge/common (μs)", "small/never (ns)", "small/rare (ns)", "small/uncommon (ns)", "small/common (ns)", "tiny/never (ns)", "tiny/rare (ns)", "tiny/uncommon (ns)", "tiny/common (ns)", "empty/never (ns)"]
df = pd.DataFrame({'rust': rust, 'libc': libc, 'fallback': fallback, 'naive': naive}, index = index, columns=['rust', 'libc', 'fallback', 'naive'])
ax = df.plot.bar(rot=0, figsize=(16, 8), fontsize=14, color=['#E24A33', '#988ED5', '#348ABD', '#8ABD34'])
ax.set_yscale("log")
ax.set_title("memchr1")
ax.legend(loc=2, prop={'size': 14})
plt.xticks(rotation=45)
ax.figure.tight_layout()
plt.show()
| name |
unit |
rust |
fallback |
naive |
| memchr2/huge/never |
μs |
14.593 |
109.81 |
236.80 |
| memchr2/huge/rare |
μs |
19.814 |
117.84 |
240.70 |
| memchr2/huge/uncommon |
μs |
217.67 |
425.65 |
388.89 |
| memchr2/huge/common |
μs |
592.77 |
1607.6 |
1118.4 |
| memchr2/small/never |
ns |
16.824 |
126.98 |
273.1 |
| memchr2/small/rare |
ns |
33.084 |
138.15 |
270.09 |
| memchr2/small/uncommon |
ns |
146.85 |
226.89 |
288.63 |
| memchr2/small/common |
ns |
605.28 |
587.78 |
370.93 |
| memchr2/tiny/never |
ns |
6.6808 |
17.530 |
30.426 |
| memchr2/tiny/rare |
ns |
14.427 |
27.77 |
33.182 |
| memchr2/tiny/uncommon |
ns |
73.553 |
74.021 |
43.895 |
| memchr2/empty/never |
ns |
1.2716 |
3.1986 |
3.3616 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
rust = np.array([14.593, 19.814, 217.67, 592.77, 16.824, 33.084, 146.85, 605.28, 6.6808, 14.427, 73.553, 1.2716])
fallback = np.array([109.81, 117.84, 425.65, 1607.6, 126.98, 138.15, 226.89, 587.78, 17.530, 27.77, 74.021, 3.1986])
naive = np.array([236.80, 240.70, 388.89, 1118.4, 273.1, 270.09, 288.63, 370.93, 30.426, 33.182, 43.895, 3.3616])
index = ["huge/never (μs)", "huge/rare (μs)", "huge/uncommon (μs)", "huge/common (μs)", "small/never (ns)", "small/rare (ns)", "small/uncommon (ns)", "small/common (ns)", "tiny/never (ns)", "tiny/rare (ns)", "tiny/uncommon (ns)", "empty/never (ns)"]
df = pd.DataFrame({'rust': rust, 'fallback': fallback, 'naive': naive}, index = index, columns=['rust', 'fallback', 'naive'])
ax = df.plot.bar(rot=0, figsize=(16, 8), fontsize=14, color=['#E24A33', '#348ABD', '#8ABD34'])
ax.set_yscale("log")
ax.set_title("memchr2")
ax.legend(loc=2, prop={'size': 14})
plt.xticks(rotation=45)
ax.figure.tight_layout()
plt.show()
| name |
unit |
rust |
fallback |
naive |
| memchr3/huge/never |
μs |
18.718 |
162.90 |
332.08 |
| memchr3/huge/rare |
μs |
25.664 |
173.87 |
337.07 |
| memchr3/huge/uncommon |
μs |
290.16 |
694.71 |
581.24 |
| memchr3/huge/common |
μs |
841.52 |
2335.9 |
1698.0 |
| memchr3/small/never |
ns |
21.926 |
199.05 |
382.96 |
| memchr3/small/rare |
ns |
46.59 |
216.28 |
376.17 |
| memchr3/small/uncommon |
ns |
220.43 |
392.96 |
407.55 |
| memchr3/small/common |
ns |
879.51 |
1035.8 |
525.44 |
| memchr3/tiny/never |
ns |
6.7064 |
24.503 |
41.457 |
| memchr3/tiny/rare |
ns |
25.857 |
44.448 |
45.944 |
| memchr3/tiny/uncommon |
ns |
103.79 |
130.45 |
62.810 |
| memchr3/empty/never |
ns |
1.2699 |
4.2200 |
3.1735 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
rust = np.array([18.718, 25.664, 290.16, 841.52, 21.926, 46.59 , 220.43, 879.51, 6.7064, 25.857, 103.79, 1.2699])
fallback = np.array([162.90, 173.87, 694.71, 2335.9, 199.05, 216.28, 392.96, 1035.8, 24.503, 44.448, 130.45, 4.2200])
naive = np.array([332.08, 337.07, 581.24, 1698.0, 382.96, 376.17, 407.55, 525.44, 41.457, 45.944, 62.810, 3.1735])
index = ["huge/never (μs)", "huge/rare (μs)", "huge/uncommon (μs)", "huge/common (μs)", "small/never (ns)", "small/rare (ns)", "small/uncommon (ns)", "small/common (ns)", "tiny/never (ns)", "tiny/rare (ns)", "tiny/uncommon (ns)", "empty/never (ns)"]
df = pd.DataFrame({'rust': rust, 'fallback': fallback, 'naive': naive}, index = index, columns=['rust', 'fallback', 'naive'])
ax = df.plot.bar(rot=0, figsize=(16, 8), fontsize=14, color=['#E24A33', '#348ABD', '#8ABD34'])
ax.set_yscale("log")
ax.set_title("memchr3")
ax.legend(loc=2, prop={'size': 14})
plt.xticks(rotation=45)
ax.figure.tight_layout()
plt.show()The CPU info is dumped below:
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 78
model name : Intel(R) Core(TM) i7-6560U CPU @ 2.20GHz
stepping : 3
microcode : 0xcc
cpu MHz : 3104.179
cache size : 4096 KB
physical id : 0
siblings : 4
core id : 0
cpu cores : 2
apicid : 0
initial apicid : 0
fpu : yes
fpu_exception : yes
cpuid level : 22
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d
bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds
bogomips : 4416.00
clflush size : 64
cache_alignment : 64
address sizes : 39 bits physical, 48 bits virtual
