vectorfunctorlazy alternatives and similar packages
Based on the "vector" category.
Alternatively, view vectorfunctorlazy alternatives based on common mentions on social networks and blogs.

vector
An efficient implementation of Intindexed arrays (both mutable and immutable), with a powerful loop optimisation framework . 
vectorspace
Vector & affine spaces, linear maps, and derivatives 
vectorthunbox
Deriver for unboxed vectors using Template Haskell 
vectorbinaryinstances
Instances for the Haskell Binary class, for the types defined in the popular vector package. 
vectorinstances
Additional Instances for Data.Vector 
vectorspacepoints
A type for points, as distinct from vectors. 
vectorbytestring
ByteStrings as type synonyms of Vectors 
vectorheterogenous
Arbitrary size tuples in Haskell 
vectorspacemap
vectorspace operations for finite maps using Data.Map
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README
Functorlazy vectors
Functorlazy vectors are boxed vectors that support a fast fmap
operation. Calling fmap
on a functorlazy vector takes O(1) time, but calling fmap
on a standard boxed vector takes O(n) time [*]. The downside for functorlazy vectors is that slicing cannot be handled efficiently. But this is not important in many applications; for example, functorlazy vectors are used internally by the HLearn library to provide a clean, fast interface for certain machine learning tasks. (I'll have a detailed writeup later.) Functorlazy vectors are easy to use because they implement the same interface as the boxed and unboxed vectors in the vector
module (see the hackage documentation). All that stream fusion goodness still works!
* Actually, it takes more like O(n1.5) time as seen in the figure below. (It's a loglog scale, so the slope of the line is the exponent.) This is very weird.
Another downside is that the current implementation is not as efficient as boxed vectors. For some applications, the functorlazy vector is about 4x slower than boxed vectors. I believe this is mostly due to cache misses (see below), and that a more efficient implementation could avoid this problem.
In the picture above, the hashed green line represents a functorlazy vector that has had a lazy fmap
application before running the sorting algorithm (as implemented in the vectoralgorithms package). Sorting this vector requires strictly more work than sorting the functorlazy vector without fmap
applied, but it still runs faster. This is one of the reasons I believe someone more familiar with CPUlevel optimizations could make this data structure much more efficient.
How they work
The easiest way to see the difference between boxed and functorlazy vectors is by example. I've drawn the two data structures below.
In the boxed vector, every element is really a pointer. They might point to values or unevaluated expressions; many elements can even point to the same thing. The functorlazy vector, in contrast, has a boxed vector inside of it (called vecAny ), an unboxed vector ( vecInt ), and a list of functions ( funcList ).
Now let's run the following code:
fmap (*2) vector
Changes in the diagrams are highlighted below.
The boxed vector must visit every single element and apply the (*2)
function. The boxes themselves are lazy and won't be evaluated immediately, but we still must create n new thunks. That's why it takes linear time. The functorlazy vector doesn't visit any of the elements. Instead, it just adds (*2)
to funcList. Internally, the library uses unsafeCoerce
to allow us to append functions of any type to the list.
Now, let's actually visit the nodes to force evaluation of the values:
print (vector ! 1)
print (vector ! 4)
Our diagrams become:
The boxed vector accesses the elements, sees that there is a computation waiting to be performed, permorms it, then stores the result. The functorlazy vector does something quite different. First, it checks to see how many functions have been applied to the element (by looking up the appropriate index in vecInt). In this case, the elements are not uptodate because there is one function in the list, but vecInt says that no functions have been applied to the box. Therefore, we apply all the functions in the list and update the box and vecInt. Since we are actually modifying the values of these vectors, this requires a call to unsafePerformIO
. Assuming there are no bugs, this should actually be safe :)
Now, let's fmap
once more:
fmap (+7) vector
And evaluate some more elements:
print (vector ! 0)
print (vector ! 2)
Cache misses galore
In the functorlazy vector, we must access four completely different regions of memory every time we do a read. In particular, there are two different vectors that we must access. There is no way for both of these vectors to fit in the cache at the same time, so we are basically guaranteed to get a cache miss everytime we access a vector element. I'm sure there must be a way to avoid this...
Final notes
The examples directory contains all the code I used for run time and correctness testing.
If you have any questions/comments/bug reports, please let me know!