NOTES ON OPTIMIZING DICTIONARIES

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Principal Use Cases for Dictionaries

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Passing keyword arguments

Typically, one read and one write for 1 to 3 elements.

Occurs frequently in normal python code.

Class method lookup

Dictionaries vary in size with 8 to 16 elements being common.

Usually written once with many lookups.

When base classes are used, there are many failed lookups

followed by a lookup in a base class.

Instance attribute lookup and Global variables

Dictionaries vary in size. 4 to 10 elements are common.

Both reads and writes are common.

Builtins

Frequent reads. Almost never written.

Size 126 interned strings (as of Py2.3b1).

A few keys are accessed much more frequently than others.

Uniquification

Dictionaries of any size. Bulk of work is in creation.

Repeated writes to a smaller set of keys.

Single read of each key.

Some use cases have two consecutive accesses to the same key.

* Removing duplicates from a sequence.

dict.fromkeys(seqn).keys()

* Counting elements in a sequence.

for e in seqn:

d[e] = d.get(e,0) + 1

* Accumulating references in a dictionary of lists:

for pagenumber, page in enumerate(pages):

for word in page:

d.setdefault(word, []).append(pagenumber)

Note, the second example is a use case characterized by a get and set

to the same key. There are similar use cases with a __contains__

followed by a get, set, or del to the same key. Part of the

justification for d.setdefault is combining the two lookups into one.

Membership Testing

Dictionaries of any size. Created once and then rarely changes.

Single write to each key.

Many calls to __contains__() or has_key().

Similar access patterns occur with replacement dictionaries

such as with the % formatting operator.

Dynamic Mappings

Characterized by deletions interspersed with adds and replacements.

Performance benefits greatly from the re-use of dummy entries.

Data Layout (assuming a 32-bit box with 64 bytes per cache line)

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Smalldicts (8 entries) are attached to the dictobject structure

and the whole group nearly fills two consecutive cache lines.

Larger dicts use the first half of the dictobject structure (one cache

line) and a separate, continuous block of entries (at 12 bytes each

for a total of 5.333 entries per cache line).

Tunable Dictionary Parameters

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* PyDict_MINSIZE. Currently set to 8.

Must be a power of two. New dicts have to zero-out every cell.

Each additional 8 consumes 1.5 cache lines. Increasing improves

the sparseness of small dictionaries but costs time to read in

the additional cache lines if they are not already in cache.

That case is common when keyword arguments are passed.

* Maximum dictionary load in PyDict_SetItem. Currently set to 2/3.

Increasing this ratio makes dictionaries more dense resulting

in more collisions. Decreasing it improves sparseness at the

expense of spreading entries over more cache lines and at the

cost of total memory consumed.

The load test occurs in highly time sensitive code. Efforts

to make the test more complex (for example, varying the load

for different sizes) have degraded performance.

* Growth rate upon hitting maximum load. Currently set to *2.

Raising this to *4 results in half the number of resizes,

less effort to resize, better sparseness for some (but not

all dict sizes), and potentially doubles memory consumption

depending on the size of the dictionary. Setting to *4

eliminates every other resize step.

* Maximum sparseness (minimum dictionary load). What percentage

of entries can be unused before the dictionary shrinks to

free up memory and speed up iteration? (The current CPython

code does not represent this parameter directly.)

* Shrinkage rate upon exceeding maximum sparseness. The current

CPython code never even checks sparseness when deleting a

key. When a new key is added, it resizes based on the number

of active keys, so that the addition may trigger shrinkage

rather than growth.

Tune-ups should be measured across a broad range of applications and

use cases. A change to any parameter will help in some situations and

hurt in others. The key is to find settings that help the most common

cases and do the least damage to the less common cases. Results will

vary dramatically depending on the exact number of keys, whether the

keys are all strings, whether reads or writes dominate, the exact

hash values of the keys (some sets of values have fewer collisions than

others). Any one test or benchmark is likely to prove misleading.

While making a dictionary more sparse reduces collisions, it impairs

iteration and key listing. Those methods loop over every potential

entry. Doubling the size of dictionary results in twice as many

non-overlapping memory accesses for keys(), items(), values(),

__iter__(), iterkeys(), iteritems(), itervalues(), and update().

Also, every dictionary iterates at least twice, once for the memset()

when it is created and once by dealloc().

Dictionary operations involving only a single key can be O(1) unless

resizing is possible. By checking for a resize only when the

dictionary can grow (and may *require* resizing), other operations

remain O(1), and the odds of resize thrashing or memory fragmentation

are reduced. In particular, an algorithm that empties a dictionary

by repeatedly invoking .pop will see no resizing, which might

not be necessary at all because the dictionary is eventually

discarded entirely.

Results of Cache Locality Experiments

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When an entry is retrieved from memory, 4.333 adjacent entries are also

retrieved into a cache line. Since accessing items in cache is *much*

cheaper than a cache miss, an enticing idea is to probe the adjacent

entries as a first step in collision resolution. Unfortunately, the

introduction of any regularity into collision searches results in more

collisions than the current random chaining approach.

Exploiting cache locality at the expense of additional collisions fails

to payoff when the entries are already loaded in cache (the expense

is paid with no compensating benefit). This occurs in small dictionaries

where the whole dictionary fits into a pair of cache lines. It also

occurs frequently in large dictionaries which have a common access pattern

where some keys are accessed much more frequently than others. The

more popular entries *and* their collision chains tend to remain in cache.

To exploit cache locality, change the collision resolution section

in lookdict() and lookdict_string(). Set i^=1 at the top of the

loop and move the i = (i << 2) + i + perturb + 1 to an unrolled

version of the loop.

This optimization strategy can be leveraged in several ways:

* If the dictionary is kept sparse (through the tunable parameters),

then the occurrence of additional collisions is lessened.

* If lookdict() and lookdict_string() are specialized for small dicts

and for largedicts, then the versions for large_dicts can be given

an alternate search strategy without increasing collisions in small dicts

which already have the maximum benefit of cache locality.

* If the use case for a dictionary is known to have a random key

access pattern (as opposed to a more common pattern with a Zipf's law

distribution), then there will be more benefit for large dictionaries

because any given key is no more likely than another to already be

in cache.

* In use cases with paired accesses to the same key, the second access

is always in cache and gets no benefit from efforts to further improve

cache locality.

Optimizing the Search of Small Dictionaries

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If lookdict() and lookdict_string() are specialized for smaller dictionaries,

then a custom search approach can be implemented that exploits the small

search space and cache locality.

* The simplest example is a linear search of contiguous entries. This is

simple to implement, guaranteed to terminate rapidly, never searches

the same entry twice, and precludes the need to check for dummy entries.

* A more advanced example is a self-organizing search so that the most

frequently accessed entries get probed first. The organization

adapts if the access pattern changes over time. Treaps are ideally

suited for self-organization with the most common entries at the

top of the heap and a rapid binary search pattern. Most probes and

results are all located at the top of the tree allowing them all to

be located in one or two cache lines.

* Also, small dictionaries may be made more dense, perhaps filling all

eight cells to take the maximum advantage of two cache lines.

Strategy Pattern

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Consider allowing the user to set the tunable parameters or to select a

particular search method. Since some dictionary use cases have known

sizes and access patterns, the user may be able to provide useful hints.

1) For example, if membership testing or lookups dominate runtime and memory

is not at a premium, the user may benefit from setting the maximum load

ratio at 5% or 10% instead of the usual 66.7%. This will sharply

curtail the number of collisions but will increase iteration time.

The builtin namespace is a prime example of a dictionary that can

benefit from being highly sparse.

2) Dictionary creation time can be shortened in cases where the ultimate

size of the dictionary is known in advance. The dictionary can be

pre-sized so that no resize operations are required during creation.

Not only does this save resizes, but the key insertion will go

more quickly because the first half of the keys will be inserted into

a more sparse environment than before. The preconditions for this

strategy arise whenever a dictionary is created from a key or item

sequence and the number of *unique* keys is known.

3) If the key space is large and the access pattern is known to be random,

then search strategies exploiting cache locality can be fruitful.

The preconditions for this strategy arise in simulations and

numerical analysis.

4) If the keys are fixed and the access pattern strongly favors some of

the keys, then the entries can be stored contiguously and accessed

with a linear search or treap. This exploits knowledge of the data,

cache locality, and a simplified search routine. It also eliminates

the need to test for dummy entries on each probe. The preconditions

for this strategy arise in symbol tables and in the builtin dictionary.

Readonly Dictionaries

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Some dictionary use cases pass through a build stage and then move to a

more heavily exercised lookup stage with no further changes to the

dictionary.

An idea that emerged on python-dev is to be able to convert a dictionary

to a read-only state. This can help prevent programming errors and also

provide knowledge that can be exploited for lookup optimization.

The dictionary can be immediately rebuilt (eliminating dummy entries),

resized (to an appropriate level of sparseness), and the keys can be

jostled (to minimize collisions). The lookdict() routine can then

eliminate the test for dummy entries (saving about 1/4 of the time

spent in the collision resolution loop).

An additional possibility is to insert links into the empty spaces

so that dictionary iteration can proceed in len(d) steps instead of

(mp->mask + 1) steps. Alternatively, a separate tuple of keys can be

kept just for iteration.

Caching Lookups

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The idea is to exploit key access patterns by anticipating future lookups

based on previous lookups.

The simplest incarnation is to save the most recently accessed entry.

This gives optimal performance for use cases where every get is followed

by a set or del to the same key.