frr
Klib
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frr | Klib | |
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21 | 23 | |
2,976 | 4,021 | |
2.6% | - | |
10.0 | 4.3 | |
4 days ago | 13 days ago | |
C | C | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
frr
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Advice for a high school networking lab
FRR https://frrouting.org/ is another open source tool for a more Cisco style CLI syntax. You’d install that on top of most Linux distros.
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Flush BGP table faster
Use a decent PC or three, use https://frrouting.org/ and see fast bgp convergence 8-)
- I think I suck at Networking
- Uthash – C macros for hash tables and more
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Recommendations on Software-based Traffic Management and Routing Platforms
Open source, check out freerouter (https://wiki.geant.org/display/RARE/Home) and FRR (https://frrouting.org/)
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Pulling My Hair Out Over Netplan
u/GamerLymx The setup goal was, originally, to set this up as a desktop testing router using FRRouting so I could experiment with some technologies I work with, namely OpenFabric without having to buy a $6k switch (Extreme Networks) as well as OSPF and EIGRP. However, I ran into some issues with setting up FRRouting and could not find sufficient information out there to overcome those. So I decided to just turn it into a plain router using NFTables, etc., when I ran into the issues with Netplan.
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FRRouting and NordVPN
Has anyone ever setup FRRouting to connect to a consumer VPN such as NordVPN? I have been Googling but have come up empty so I am assuming it is not something that can be done. Just the same, I thought I would ask in case I am asking the question wrong in my searches.
- VPN for dynamic routing
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10G Peering on L3 Switch (Cisco, maybe N9K?)
Linux machine with 96G RAM, can do full internet table IPv4/IPv6 (dual stack) with FRR (https://frrouting.org)
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OSPFv3 with opnsense and cisco 3925
Right, so it turns out that FRR (the routing plugin for opnsense) doesn't support multiple address familys in ospfv3. It is down as a feature request here and is closed as issue #303.
Klib
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Factor is faster than Zig
In my example the table stores the hash codes themselves instead of the keys (because the hash function is invertible)
Oh, I see, right. If determining the home bucket is trivial, then the back-shifting method is great. The issue is just that it’s not as much of a general-purpose solution as it may initially seem.
“With a different algorithm (Robin Hood or bidirectional linear probing), the load factor can be kept well over 90% with good performance, as the benchmarks in the same repo demonstrate.”
I’ve seen the 90% claim made several times in literature on Robin Hood hash tables. In my experience, the claim is a bit exaggerated, although I suppose it depends on what our idea of “good performance” is. See these benchmarks, which again go up to a maximum load factor of 0.95 (Although boost and Absl forcibly grow/rehash at 0.85-0.9):
https://strong-starlight-4ea0ed.netlify.app/
Tsl, Martinus, and CC are all Robin Hood tables (https://github.com/Tessil/robin-map, https://github.com/martinus/robin-hood-hashing, and https://github.com/JacksonAllan/CC, respectively). Absl and Boost are the well-known SIMD-based hash tables. Khash (https://github.com/attractivechaos/klib/blob/master/khash.h) is, I think, an ordinary open-addressing table using quadratic probing. Fastmap is a new, yet-to-be-published design that is fundamentally similar to bytell (https://www.youtube.com/watch?v=M2fKMP47slQ) but also incorporates some aspects of the aforementioned SIMD maps (it caches a 4-bit fragment of the hash code to avoid most key comparisons).
As you can see, all the Robin Hood maps spike upwards dramatically as the load factor gets high, becoming as much as 5-6 times slower at 0.95 vs 0.5 in one of the benchmarks (uint64_t key, 256-bit struct value: Total time to erase 1000 existing elements with N elements in map). Only the SIMD maps (with Boost being the better performer) and Fastmap appear mostly immune to load factor in all benchmarks, although the SIMD maps do - I believe - use tombstones for deletion.
I’ve only read briefly about bi-directional linear probing – never experimented with it.
- A simple hash table in C
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So what's the best data structures and algorithms library for C?
It could be that the cost of the function calls, either directly or via a pointer, is drowned out by the cost of the one or more cache misses inevitably invoked with every hash table lookup. But I don't want to say too much before I've finished my benchmarking project and published the results. So let me just caution against laser-focusing on whether the comparator and hash function are/can be inlined. For example stb_ds uses a hardcoded hash function that presumably gets inlined, but in my benchmarking (again, I'll publish it here in coming weeks) shows it to be generally a poor performer (in comparison to not just CC, the current version of which doesn't necessarily inline those functions, but also STC, khash, and the C++ Robin Hood hash tables I tested).
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Generic dynamic array in 60 lines of C
Not an entirely uncommon idea. I've written one.
There's also a well-known one here, in klib: https://github.com/attractivechaos/klib/blob/master/kvec.h
- C_dictionary: A simple dynamically typed and sized hashmap in C - feedback welcome
- Inside boost::unordered_flat_map
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The New Ghostscript PDF Interpreter
Code reuse is achievable by (mis)using the preprocessor system. It is possible to build a somewhat usable API, even for intrusive data structures. (eg. the linux kernel and klib[1])
I do agree that generics are required for modern programming, but for some, the cost of complexity of modern languages (compared to C) and the importance of compatibility seem to outweigh the benefits.
[1]: http://attractivechaos.github.io/klib
- C LIBRARY
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boost::unordered map is a new king of data structures
Unordered hash map shootout CMAP = https://github.com/tylov/STC KMAP = https://github.com/attractivechaos/klib PMAP = https://github.com/greg7mdp/parallel-hashmap FMAP = https://github.com/skarupke/flat_hash_map RMAP = https://github.com/martinus/robin-hood-hashing HMAP = https://github.com/Tessil/hopscotch-map TMAP = https://github.com/Tessil/robin-map UMAP = std::unordered_map Usage: shootout [n-million=40 key-bits=25] Random keys are in range [0, 2^25). Seed = 1656617916: T1: Insert/update random keys: KMAP: time: 1.949, size: 15064129, buckets: 33554432, sum: 165525449561381 CMAP: time: 1.649, size: 15064129, buckets: 22145833, sum: 165525449561381 PMAP: time: 2.434, size: 15064129, buckets: 33554431, sum: 165525449561381 FMAP: time: 2.112, size: 15064129, buckets: 33554432, sum: 165525449561381 RMAP: time: 1.708, size: 15064129, buckets: 33554431, sum: 165525449561381 HMAP: time: 2.054, size: 15064129, buckets: 33554432, sum: 165525449561381 TMAP: time: 1.645, size: 15064129, buckets: 33554432, sum: 165525449561381 UMAP: time: 6.313, size: 15064129, buckets: 31160981, sum: 165525449561381 T2: Insert sequential keys, then remove them in same order: KMAP: time: 1.173, size: 0, buckets: 33554432, erased 20000000 CMAP: time: 1.651, size: 0, buckets: 33218751, erased 20000000 PMAP: time: 3.840, size: 0, buckets: 33554431, erased 20000000 FMAP: time: 1.722, size: 0, buckets: 33554432, erased 20000000 RMAP: time: 2.359, size: 0, buckets: 33554431, erased 20000000 HMAP: time: 0.849, size: 0, buckets: 33554432, erased 20000000 TMAP: time: 0.660, size: 0, buckets: 33554432, erased 20000000 UMAP: time: 2.138, size: 0, buckets: 31160981, erased 20000000 T3: Remove random keys: KMAP: time: 1.973, size: 0, buckets: 33554432, erased 23367671 CMAP: time: 2.020, size: 0, buckets: 33218751, erased 23367671 PMAP: time: 2.940, size: 0, buckets: 33554431, erased 23367671 FMAP: time: 1.147, size: 0, buckets: 33554432, erased 23367671 RMAP: time: 1.941, size: 0, buckets: 33554431, erased 23367671 HMAP: time: 1.135, size: 0, buckets: 33554432, erased 23367671 TMAP: time: 1.064, size: 0, buckets: 33554432, erased 23367671 UMAP: time: 5.632, size: 0, buckets: 31160981, erased 23367671 T4: Iterate random keys: KMAP: time: 0.748, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 CMAP: time: 0.627, size: 23367671, buckets: 33218751, repeats: 8, sum: 4465059465719680 PMAP: time: 0.680, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 FMAP: time: 0.735, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 RMAP: time: 0.464, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 HMAP: time: 0.719, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 TMAP: time: 0.662, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 UMAP: time: 6.168, size: 23367671, buckets: 31160981, repeats: 8, sum: 4465059465719680 T5: Lookup random keys: KMAP: time: 0.943, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 CMAP: time: 0.863, size: 23367671, buckets: 33218751, lookups: 34235332, found: 29040438 PMAP: time: 1.635, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 FMAP: time: 0.969, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 RMAP: time: 1.705, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 HMAP: time: 0.712, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 TMAP: time: 0.584, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 UMAP: time: 1.974, size: 23367671, buckets: 31160981, lookups: 34235332, found: 29040438
- C++ containers but in C
What are some alternatives?
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