Stanford Infolab


Fast parallel code generation for data analytics frameworks. Developed at Stanford University.


WeldNumpy is a Weld-enabled library that provides a subclass of NumPy’s ndarray module, called weldarray, which supports automatic parallelization, lazy evaluation, and various other optimizations for data science workloads. This is achieved by implementing various NumPy operators in Weld’s IR. Thus, as you operate on a weldarray, it will internally build a graph of the operations, and pass them to weld’s runtime system to optimize and execute in parallel whenever required.

In examples, you can see improvements of upto 5x on a single thread on some NumPy workloads, essentially without changing any code from the original NumPy implementations. Naturally, much bigger performance gains can be got by using the parallelism provided by Weld. In general, Weld works well with programs that operate on large NumPy arrays with compute operations that are supported by Weld.

NumPy features that WeldNumpy currently supports are:

One of the goals of this library is to require minimal changes to your original NumPy source code in order to harness the optimizations provided by Weld. Thus, you can write NumPy programs in the same style, and WeldNumpy will only evaluate those when neccessary.

In general, it may be more flexible to not always utilize weldarray - for instance with relatively small sized NumPy arrays, the overhead of compiling the Weld programs will be more expensive than the total NumPy execution times.

The biggest speed benefit from using WeldNumPy v/s NumPy is just that the Weld code can be automatically parallelized. But even with single threaded code, there are various tricks that Weld uses in order to get the most out of the performance. Let us now look at a few basic usage examples that also highlight some of these benefits provided by WeldNumpy.


Because python is eagerly evaluated, it has to materialize every object in memory even if it is only going to live for a short duration as part of other calculations. By using lazy evaluation, WeldNumpy can avoid such costs. For instance:

import weldarray from weldnumpy
import numpy as np

a = weldarray(np.random.rand(1000000))
b = weldarray(np.random.rand(1000000))
c = weldarray(np.random.rand(1000000))

d = a*b + c
d += 100.00
# All the above operations on 'd' can again be fused together into a single
# loop which would avoid wasting memory on intermediate results like a*b etc.

# To cause the evaluation, one can do:
d = d.evaluate()

Loop Fusion

Again, because of lazy evaluation, WeldNumpy can look across the program, and find multiple loops that go over the same ndarray - which can then be converted into a single loop. Here is another contrived example:

# Another way to use weldnumpy is to change the import statement at the top of
# the NumPy file. In general, the WeldNumpy class just serves a wrapper around
# NumPy functions - with the array creation routines modified to return 
# weldarray's instead. 

import weldnumpy as np

# These statements would return weldarray's, because the weldnumpy class
# provides wrapper functions for all array creation routines
a = np.random.rand(100000000)
b = np.random.rand(100000000)

for i in range(10):
    a += b

# Because weld is lazily computed, until this point, no computation has actually occurred. Now, if you choose to
# access the array, 'a' in any way, or call, a = a.evaluate(), then the stored
# operations on 'a' will be evaluated in weld. Looking at the complete program
# will let Weld apply optimizations like loop fusion, which would essentially
# convert the above program to:
#   a = ((((a+b)+b)+b)+ ... b)
# which clearly saves A LOT of loops compared to a traditional NumPy program.
# This will also be executed in parallel if specified.

print(a)    # since print accesses elements of a, internally it will call a.evalaute()

Changing the import statement as in the previous example, may serve as a quick method to use weldnumpy, but in general for a large program, it makes sense to only import weldarray and convert only the large arrays whose operations you want to optimize, as in the first example above. More detailed examples with comments are provided in examples.

Redundant Computations

a = np.random.rand(10000)
# Here, NumPy would evaluate a**2 twice. But WeldNumpy will only need to
# evaluate it once - and then for the second a**2, the value would have been
# stored. 
b = a**2 + a**2 

# forces evaluation
b = b.evaluate()

Notice that in the example above, it seems like a very easy error to spot. But in general, such redundant computations can get arbitrarily complicated, and it is nice to be able to automatically eliminate these.


In general, the semantics for the operations on weldarray’s are exactly the same as the equivalent operations on NumPy arrays. The only extra operation on a weldarray is: weldarray.evaluate() which forces the evaluation of all the operations registered on the weldarray.

Differences with NumPy

Compilation Costs

In general, there is a slight overhead for compiling the Weld IR to LLVM before it can be executed. If the array sizes are large enough, then these compilation costs add little overhead as compared to computations. But this also means that if you are using NumPy with small arrays, then there would be little to no use of WeldNumpy.

Lazy Evaluation

Weld is a lazily evaluated IR - i.e., when a program line is encountered, it is not neccessarily executed. Instead, these operations are just stored as metadata in the weldarray, so that they could be executed at a later time with the intention that certain optimizations (like the ones described above) will suddenly become possible. NumPy doesn’t do this, so the challenge is to present the same interface as NumPy without explicitly using lazy evaluation.

Implicit Evaluation

In general, if you print an array / or access it in some other way without explicitly evaluating it, you will still see the correct results because weldnumpy will implicitly evaluate it. For example:

from weldnumpy import weldarray
import numpy as np
w = weldarray(np.random.rand(100000))
w2 = weldarray(np.random.rand(100000))
w += w2
# this will print latest value of w[0], and also cause evaluation of the whole
# w array.

Another use case for implicit evaluation is when we offload operations to NumPy. Since NumPy expects to operate on the memory of the ndarray like object


This is one of the two public functions on weld arrays that is different from the NumPy operations. It will just evaluate all the operations registered with the weldarray, and return an updated weldarray. In general, calling evaluate multiple times on the same weldarray does not affect it.

w = w + n
# assuming 'w' is a weldarray with stored operations, like "+ n", this will
# update the # weldarray to it's latest values.
w = w.evaluate()

As mentioned before, WeldNumPy also has a concept of implicit evaluation. But these implicit evaluations are triggered based on heuristics - e.g., when we are able to intercept a call to print. Thus, there can be many cases where the user might want to force evaluation of all stored operations on a weldarray. One crucial case that can lead to a subtle bug is when the weldarray is passed to other unknown functions, in particular, some of NumPy’s functions like np.array_equal:

from weldnumpy import weldarray
import numpy as np
w = weldarray(np.random.rand(100))
n = np.random.rand(100)
w2 = w + 100.00
n2 = n + 100.00

# Now, clearly the two arrays n2, and w2, should be equal. And this is
# confirmed by:
np.allclose(n2, w2) # returns True

# But array_equal returns False, because array_equal, internally casts any array like
# object it gets into an array with its base memory. Since the w2 = w + 100.00
# operation above did not create any new memory for w2, the base array for w2
# is also w -- thus in array_equal NumPy actually downcasts w2 to this base
# array before doing the further calculations. 
np.array_equal(n2, w2)  # returns False!

# Instead if we do:
np.array_equal(n2, w2.evaluate())   # returns True!
# Or:
w2 = w2.evaluate()
np.array_equal(w2, n2)      # returns True

# Then in both these cases, the operations stored on w2 do get evaluated which
# makes Weld give it its own memory - thus casting it to its base array in
# array_equal only casts it to the 'correct' memory for this case.

We can also see similar things happening with other NumPy functions like np.random_choice etc. Note: Downcasting to the base array is not the behaviour for most NumPy functions, but there are a few that just do this. In general, the simplest thing might be to remember to evaluate the arrays before sending it into an unknown NumPy function.

Things that don’t quite work

In general, most common functions on a ndarray are routed by NumPy through the weldarray subclass - thus things like universal functions, np.reshape, np.T etc. just work as expected. And if there are some unsupported functions (like reductions over multi-dimensional arrays), then these are just offloaded to NumPy, and the result is converted to a weldarray before returning - thus everything works as expected.

But we have not exhaustively looked through all the functions that NumPy provides, so potentially, there could be some other issues. One example scenario is is when you use import weldnumpy as np: This requires us to add wrapper functions for various NumPy functions in WeldNumPy. We do this by using a blunt import * - but there are functions that can be missed, for instance:

Making the most of the Weld model

We have designed this such that you can use it without really understanding what is going on under the hood - but If you understand how a few things work, you may be able to get the most out of your programs.

Fewer Evaluations = Better Performance

There are two main reasons why calling evaluate unneccessarily will cause performance slowdowns:

Thus, besides avoiding explicit evaluate calls when you don’t need them, you should also avoid things that cause implicit evaluations: printing the weldarray before the end of computations, using unsupported operations if you can avoid them, and so on.