Paul Khuong mostly on Lisp

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Mon, 13 Apr 2009


Interlude: Numerical experiments in hashing

EDIT: Added clarifications, modifed the wording a bit, and linked to the source (see bottom of post).

Cleaning up the SSE work, making it useful and using it in something interesting would take too much time given my current situation. SSE intrinsics, part 2, will have to wait. However, there's always time for a couple experiments induced by reading papers. d-left hashing seems to have gotten some attention in the last couple years. Unfortunately, I have a hard time mapping from the papers to real-world performance.

The idea behind d-left hashing is simple. Insertions work as follows:

  1. Split our hash table in d equal sections (typically two, the left and the right halves), and order the sections from left to right arbitrarily.
  2. Associate d independent hash values to the key, h_1, h_2, ... h_d.
  3. Use the d hash values to find a bucket in each section of the hash table, and put the key-value pair in the least loaded bucket; break ties not randomly, but by taking the bucket in the leftmost section (as per the arbitrary ordering established in 1.).

The procedure for lookups is easily derived from the above.

Surprisingly, breaking ties unfairly behaves slightly better than random tie-breaking. Mitzenmacher's, Richa's and Sitaraman's The Power of Two Random Choices: A Survey of Techniques and Results covers the topic fairly extensively.

Having a choice in which bucket to use gives a substantial (theoretic) advantage over having a single hash function (d = 1). Assuming ideal hash functions, we expect the greatest number of elements in the buckets of a regular bucketed hash table with n buckets in which we insert n elements to be around log n/log log n; for d > 1, that's log log n/log d + Theta(1). Clearly, setting d to 2 yields a great improvement over d = 1, and d > 2 only shaves a constant factor.

I don't doubt the theory, but "approximately" and "Theta(1)" aren't negligible for hash tables, where we're pretty much always fighting for better constant factors regarding space usage. Luckily, this idealised ball and bin setting is easily to simulate (the hash functions are just PRNGs here and in the sequel). I ran numerical experiments with n = 2^4, 2^5, ... 2^20, each time with 1000 repetition (I'll use the same number of independent repetitions throughout the post). The distribution of bucket sizes is fairly constant over the range, so I'll only show the results for n = 2^20.

Size  1-left (%)  2-left (%)
 0      36.8        22.8
 1      36.8        54.8
 2      18.4        21.9
 3       6.1         0.4
 4       1.5         0.0
 5       0.3          0
 6       0.1          0
         ...         ...
12       0.0          0
13        0           0

The theoretical results predict a maximal size of 5~6 for 1-left and 3~4 for 2-left, which is pretty close to what we observe here.

Unfortunately, I don't find that result satisfying: how will the buckets be implemented? If they're preallocated, we can only tell that the buckets would have to all be 3-entry deep (assuming a short overflow list to supplement the 6e-6% buckets with more than 3 entries) for all 2^20 entries to fit. That uses 3 times as much space as the actual data! Buckets implemented as linked lists or allocated adaptively might use less space, but, again, the constant factors are extremely important, since we expect to have relatively shallow buckets; the additional pointers might outweigh most or all the space savings. The results above are a good hint that 2-left hashing will perform much better than regular buckets, but otherwise don't seem particularly telling.

I found it interesting to compare the space efficiency of 2-left hash tables to that of a regular, even naive, hash table with linear open addressing. Again, the experiments fills 2^20 bins with 2^20 balls (the hash functions are again PRNGs), so the load is the inverse of the space usage. However, each bin can only contain up to 1 ball; when full, we try to put the ball in the next bin, up to a certain probe length limit (so we examine at most a small fixed number of bins). We won't be able to place all the balls (without a lot of luck, anyway), but it'll be interesting to see how much of the 2^20 balls will fit. I tried that with a maximal probe length of 16, and another one with again a maximal probe length of 16, but also an overflow list of at most 8 entries. The overflow list is used to host a small number of entries that exceed the probe length limit (or bucket size) instead of aborting immediately. Since very few entries hit the limit, a short list is often enough to substantially increase the expected load.

           Open       Open+Overflow
Load (%)  Freq. (%)  Freq. (%)
18           0         0
19          0.10       0
20          0.20       0
21          0.31       0
22          0.31       0
23          0.20       0
24          0.82       0
25          1.33       0
26          2.34       0
27          3.37       0
28          3.88       0
29          8.17       0
30          8.99       0
31         11.34       0
32         12.77       0
33         13.89       0
34         13.59      0.10
35         10.83      0.31
36          5.82      2.65
37          1.63      8.56
38          0.10     21.71
39           0       35.07
40           0       24.57
41           0        6.93
42           0        0.10
43           0         0

With linear open addressing, we can already expect to have approximately the same space overhead as 2-left tables with preallocated buckets if we wanted to guarantee that all 2^20 entries fit. The small overflow list has two effects: the worst case is greatly improved, and the mode (and average) is also considerably better. The expected space overhead is now even lower, probably close to what 2-left with buckets as linked lists would achieve! However, probing 16 cells might be a bit slow. Looking at the experiment's results show that most probes are very short (although this doesn't tell us much about lookups of missing keys), so it's not that bad. For the open addressing table with overflow list, we have:

Probe length  Frequency (%)
 1             80.55
 2             12.77
 3              3.84
 4              1.49
 5              0.66
 6              0.32
 7              0.16
 8              0.09
 9              0.05
10              0.03
11              0.02
12              0.01
13              0.01
14              0.00
15              0.00
16              0.00

Still, as a comparison point, 2-left hashing with 1/4 * 2^20 4-deep buckets and an 8-entry overflow list achieves much better loads (regular preallocated buckets are almost pitiful). Unlike the theoretical setting, buckets have a bounded size (at most 4), so the table uses 2^20 cells (+ the overflow list). The amount of space allocated is much more interesting, but it also means that it will take much fewer than 2^20 balls to have enough collisions to fill the overflow list. Note that for the sequel, I'll always use the same space budget: 2^20+8 key-value cells, whether they're arranged in buckets or not. Load is always computed as the fraction of 2^20 balls that could be placed before hitting the probe length or bucket size limits 8 times (enough to fill the overflow list). All the statistics are aggregated from 1000 independent runs.

           2-left, 4-buckets, 8-overflow
Load (%)  Frequency (%)
54           0
55          1.31
56         14.76
57         49.75
58         32.46
59          1.72
60           0

Bucket utilisation is also pretty good:

Bucket size  Frequency (%)
0              1.90
1             13.39
2             42.50
3             38.38
4              3.83

2-left hashing is clearly very interesting, even with preallocated buckets; that's much more obvious here than with the previous unbounded bucket experiment. Can we do better?

Having the choice of which table to use can clearly be very effective. A similar trick can be used on linear open addressing tables: split in two sub-tables, probe both sub-tables and stop as soon as one of them has an empty slot (again, breaking ties unfairly). I simulated such a scheme with 2 sub-tables of 1/2 * 2^20 cells, an 8-element overflow list, and maximal probe lengths of 8 and 16.

           Probe: 8
Load (%)   Freq. (%)
52            0
53           0.51
54           4.18
55          18.33
56          34.83
57          31.16
58          10.29
59           0.71
60            0
           Probe: 16
Load (%)   Freq. (%)
68            0
69           0.10
70           4.76
71          25.63
72          47.11
73          21.38
74           1.01
75            0

With a maximal probe length of 8, the 2-open addressing table already hits an expected load similar to that of 2-left table. When the maximal length is pushed to 16, load is much better, and, again, successful probes usually aren't very long: 98.9% of probes are of length at most 4 (in both tables, meaning that at most 8 bins are examined).

Allowing probes (of length greater than 1) has the advantage of letting buckets overflow into adjacent ones, so that lightly loaded buckets are better exploited in the rare cases when one overflows. Deeper buckets, on the other hand, avoid the clustering issues of linear open addressing; they're also slightly more cache-friendly. When data is moved into cache, it's loaded a whole cache line at a time. Linear addressing can exploit caches nicely since it will probe consecutive addresses. However, there's no guarantee that the probe will start at the beginning of the cache line. If we take care to align buckets cache lines will be used more efficiently.

Why not do both? Once we've settled on using multiple tables, short probes and small preallocated buckets don't really make the code more complex. For a first attempt, I simulated hash tables with 2 sub-tables, 4-deep buckets, up to 2, 3 or 4 -long probes and an overflow list of at most 8 entries. So, an insertion will compute two hash values to decide where to start probing, and insert in the least loaded bucket (breaking ties as usual), continuing to the next bucket in each table if both are empty. Load is pretty good, better than 2-left hashing:

Load (%)   Freq. (%)
66           0
67          0.91
68         17.00
69         51.11
70         30.77
71          0.20
72           0
Load (%)   Freq. (%)
72           0
73          0.20
74         10.82
75         53.79
76         34.78
77          0.40
78           0
Load (%)   Freq. (%)
76           0
77          0.10
78         11.36
79         62.27
80         26.17
81          0.10
82           0

The 2-table with 3-long probes and 4-deep buckets already performs better than the 2-open table with 16-long probes while examining fewer elements in the worst case. Longer probes on shallower buckets aren't really an improvement; deeper buckets, on the other hand, are. With 8-deep buckets (but still the same space budget) and no open addressing, we hit a load factor of ~78% (85% with probes of length up to 2), and 90% for 16-deep buckets (open addressing isn't that effective anymore). The problem with deeper buckets is that they take more time to search (since they're usually nearly full), while the vast majority of probes are of length 1 (> 99% for the last three parameter sets). Let's also keep in mind that we have to execute the search on two sub-tables; searching among 16 elements twice adds up to a substantial amount of work.

I've listed many possibilities. The following table recapitulates the modal load factor for all the simulations above. Keep in mind that the inverse, the space usage, is actually more relevant in practice. As load values get closer to 1, the difference in space overhead becomes smaller, so the 6% difference between open(16) without overflow and open(16) is much more important than the 5% difference between 2-combo(8,2) and 2-left(16).

Implementation   Modal load (%)
open(16) w/o OF      33         no overflow list
open(16)             39         16-probe at most
2-left(4)            57         4-buckets
2-open(8)            56         2 x 8-probe at most
2-open(16)           72
2-combo(4,2)         69         2-left, 4-buckets, 2-probes
2-combo(4,3)         75
2-combo(4,4)         79
2-left(8)            78
2-combo(8,2)         85
2-left(16)           90

Implementation-wise, 4-deep buckets are particularly interesting because four 32-bit values (e.g. keys or hash values) are exactly the size of an SSE register and can easily be searched in a single SSE instruction. 16-deep buckets, on the other hand, usually fill cache lines (often 64 bytes) exactly. 8-deep buckets still exploit cache lines fairly well, without being too long to search in the common case. Moreover, if we modify 2-combo(8, 2) so that probes always stay in the same 64 bytes segment (probe bucket i, then i xor 1), the load factor isn't affected (in simulations), but cache lines are used as well as with 16-deep buckets. That's a nice bunch of interesting structures to implement and test. I'll try and come back to the topic with real timings in a couple posts.

Unorganised source code for the simulations may be found at

posted at: 23:09 | / | permalink

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