Benchmark on Intel N100

A second benchmark was conducted using an Intel N100 CPU to evaluate performance on lower-power devices.

Exact Search Time (ms) (English)

Text Size	Linear	GPU	Bigram
538	1.04	1.02	0.20
829	1.47	1.55	0.28
1712	2.42	2.54	0.24
3001	4.26	4.32	0.37
3748	5.21	4.98	0.46

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Fuzzy Search Time (ms) (English)

Text Size	Linear	GPU	Bigram
538	21.14	8.24	0.56
829	31.67	10.06	0.88
1712	65.76	14.93	0.98
3001	115.56	20.40	1.16
3748	142.11	25.72	1.32

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Fuzzy Search Time (ms) (Japanese)

Text Size	Linear	GPU	Bigram
476	7.60	7.45	0.32
789	10.70	6.67	0.45
1305	17.43	7.25	0.44
2394	31.17	8.20	0.57
3020	38.89	9.06	0.75

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Indexing Time (ms) (English)

Text Size	Linear	GPU	Bigram
538	126.14	117.40	261.80
829	188.64	185.20	406.52
1712	346.98	362.18	862.26
3001	592.98	617.08	1,686.10
3748	744.78	737.06	1,892.40

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Index Size (Gzipped, kbyte) (English)

Text Size	Linear	GPU	Bigram
538	191	191	91
829	294	294	131
1712	607	607	247
3001	1058	1058	411
3748	1324	1324	506

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Index Size (Gzipped, kbyte) (Japanese)

Text Size	Linear	GPU	Bigram
476	165	165	136
789	272	272	229
1305	452	452	366
2394	837	837	644
3020	1053	1053	813

Conclusion

In summary, the benchmarks conducted on the Intel N100 CPU demonstrate the performance characteristics of different indexing strategies. While the LinearIndex provides reliable performance, the GPU and HybridBigramInvertedIndex indexes offer significant speed improvements, especially for larger datasets. When selecting an index type, consider the specific requirements of your application and the hardware capabilities available.