Comments (3)
Which CPU architecture were the benchmarks running on? That probably makes a difference. The paper has some discussion about the number of instructions and such, so they may have been testing with systems that yielded the results they claimed. I'll also run some tests again. As I recall, when I tested with a 7gb file it was around 20% faster (on an ARM64 system).
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Did more testing and yep, the results are about 20% faster. The results may depend on the size of the input file and the size of the chunks. I was using larger files and larger chunks. I've updated the comment in the code to give some warning.
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Ah. I was not using mmap
in my benchmark - the fact that my benchmark is calling read() likely dilutes things a bit, since I'm now seeing a 10-12% speedup for a 2.5 GB file if I switch to mmap. I see from your comment you tested with an M1 processor, I guess maybe that might be accounting for the remaining difference between our results (or maybe I need a 7GB file to test with). I'm on Linux, x86_64 (Intel i7-9700K)
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Related Issues (18)
- doc: make it clear the iterator returns Chunks HOT 1
- Problems with right-shift in Gear rollsum. HOT 2
- FastCDC Iterator size_hint HOT 3
- Provide hash in chunk struct HOT 1
- [feature] Support stream of bytes HOT 2
- Please revert v1.0.7 due to breaking change in public interface HOT 1
- Anyone have a link to the original QuickCDC paper? HOT 1
- v2020 equivalent of FastCDC::with_eof HOT 4
- Extremely low MIN,AVG,MAX on v2020 HOT 1
- Purpose of Box in StreamCDC struct HOT 2
- Maybe size_hint() should return the estimated remaining chunk number HOT 2
- Implementing SuperCDC 2022 HOT 1
- Req: supply consume the byte as stream to avoid big buf as input HOT 12
- Test, example, API docs for AsyncStreamCDC HOT 5
- Version 3.1.0 breaks semver HOT 3
- Hash Collisions HOT 4
- Is there a im tool as https://gitter.im/dpc/rdedup or slack
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