400 karma · joined March 1, 2010
Reference. https://github.com/cksystemsgroup/scalloc https://github.com/kuszmaul/SuperMalloc
This problem is generally hard. See [Ensuring data reaches disk](https://lwn.net/Articles/457667/)
It's actually pretty funny. I didn't know Lemire's trick until I start to write this article. But I my first try on fixed point arithmetic was exactly the same as his work. Then figured out the lower bits would get omitted with fast range (I named it scaling). Finally I came up with this fast mod and scale idea.
I don't know for other peoples need on non power of 2 tables. My target is to build large scale hash tables and other data structures. The memory and data compactness is quite critical for this goal.
Speaking of cityhash, have you tried farmhash as well? I'm not sure what I did wrong, but farmhash's performance wasn't good as cityhash in my hash table. Did you experience the same problem?
The other comment pointed out MDBM, which I didn't know about. From their performance number I think this may show that why OPIC robin hood is quite optimal. https://yahooeng.tumblr.com/post/104861108931/mdbm-high-spee...
MDMB gives users raw access to the mmaped data and pointers. And from its benchmarks it results 10x faster than rocksdb and leveldb. The design of OPIC has even less overhead (may not be a good thing) than MDBM, and it also works on a mmaped file (or anonymous swap). There's no lock, transaction, or WAL in OPIC. OPIC just brings you the raw performance a hash table can gives you.
Also there's a finial mod in linear probing h(k, i) = (k + i) mod N. With this mod you may have different key/probe combination that messed up the order.
Robin hood hashing doesn't limit which probing scheme you use. I end up with quadratic probing which gives me both good cache locality and good probe distributions. The probing schemes I tried was omitted in this post because it would bring too much noise. But I can give you some quick summary here:
1. linear probing: probing distribution has high medium and high variance. Performance is not that great either because of the high probing numbers.
2. quadratic probing: probing distribution is not the best, but the medium sicks to 2 probes. Since the first two probes are very close in quadratic probing, its overall performance wins.
3. When probing, hash the key with the probe added to the seed. This gives very good hash distribution, but hash on each probe is very slow. Also you cannot do deletion using this scheme.
4. rotate(key, probe). This is somewhat like rehash, but way faster. The probe distribution is also very good, but items goes too far away so we lost the cache locality.
5. Combination of different schemes listed above. Still, the quadratic probing gives me best performance.
I also tried to use gcc/clang vector extension to speed up probing, but it actually slows down for 30%! I guess I have to hand tune the SSE intrinsics and measure it carefully with IACA to get the optimal performance.
Deletion itself is quite complicated and deserves its own post.
First of all: "I often need lots of small hash maps". Do you maintain multiple small hash maps at the same time? Then the data structure holding this many hash maps may be your bottleneck instead of the hash map itself. Another possibility is you have some small hash map that get created and dropped rapidly. In this case you might want to hold it in memory so you don't have much allocation overhead.
Other than these, the remaining optimization I can think of would be making your hash table compact (like what I did in this post). Reserving buckets with max load factor should give you a nice control of your hash table size. Reference: http://www.cplusplus.com/reference/unordered_map/unordered_m...
Some recorded video of how police beat people
We video taped and written a song for the occupation. Even compare to other protest in countries, we are so proud our people in protest is peaceful and well organized. People who have profession (like doctors, hackers, lawyers) setup stations to help people. Other volunteers got organized and pick up trash and send out food.
Still, we are terrified. The government sent out police to beat up people who don't have weapons. Even though we have videos to prove it, the government is still denying it.
This is the worst moment for us, but also the best. We see hope from people, and we're looking for your help.
Officially, Taiwan and China is in war. We never signed up armistice agreement. Taiwan politicians can be separated into two groups. One believe if we stay close to China, we can have better economics. A few of them even want to unite with China. The other group believe we should stay as a country of our own, and China government hates it.
With those background information you can see, making a economic agreement with china is sensitive to people, but the current government tried to pass it without standard procedure in congress. This is why people are so angry about this.
This is something that Taiwan people can't stand for. We need to fight for our democracy procedure. A law like this cannot be treated this way. The students in taiwan occupied the congress hall (Legislative Yuan). Following up we have so many volunteer from all professions joined us. The doctors started to help people who were injured; the lawyers defended for people who were caught by police; hackers who like you and me helped the wifi and real time streaming to be stable and robust, and also built this website for more visibility from the world.
It's 4am at Taiwan. WE NEED YOUR ATTENTION. WE NEED YOU to spread what happened in taiwan to the world. Take a look on those photos and videos. It's dark in Taiwan, but we believe the dawn will come.
Another reason is ember-data is one of the reasons that I want to use ember-js. I would like to develop a program entirely on fronted then switch to backend. Without ember-data ember.js seems not that attractive to me.
It just like...after watching fire-up-ember-js podcast and found out the core feature is not production ready. I'm a little bit upset that I really bought the video..
If Automatic reference counting (ARC) feature was enabled, the compiler would raise an error for the code `[NSClassFromString(@"WebView") _enableRemoteInspector];` : "No known class method for selector '_enableRemoteInspector.'"