An example specializer built on ASPIRE's SEJITS framework

The purpose of this repository is to demonstrate the functionality of ASPIRE's SEJITS framework. This repository contains an example specializer that performs reductions (hence the name ReductionSpecializer), and has example code both for industry specialists who write their own specializers, as well as end-users who simply use the specializers. End-users write very simple Python code, gaining very efficient performance because of our framework and the implementation of specializer-writers.

ReductionSpecializer is based on ctree. Be sure to install ctree before attempting to run this framework You can find ctree on github, with instructions for installation.

Additionally, ReductionSpecializer assumes that your machine has Python (any version after 2.0, but before 3.0). This is also a prerequisite for ctree.

This example specializer demonstrates various features of ASPIRE's SEJITS framework. Below is a list of the features that are currently included in the master branch of this repository.

• Difference between Specializer-Writer code and End-User code
• Simplicity of Python Code for end user to write
• Dynamic class creation using LazySpecializedFunction.from_function()
• Ability to have multiple LazySpecializedFunction subclasses use the same ConcreteSpecializedFunction subclass
• Persistent Caching improvements (drastic speedup between the 1st run of a particular data set size and subsequent runs)
• ctree syntax and usage in LazySpecializedFunction.transform() (in a subclass of LazySpecializedFunction)
• Lambda expression syntax for concise kernel functions

While the code in this repository offers a sufficient demonstration of the ease of use for the end-user, in this section we walk a potential user through the procedure of using this specializer.

Let's say the end-user has a numpy array that we want to sum all the elements of.

dataset = numpy.array([6, 2, 66, 23, 74, 23, 52, 362, 23, 723, 1042, 44, 2, 56, 27, 43])

Again, we want to add all those elements up. Traditionally, an end-user would have a for loop to iterate through these elements and add them all up. However, when sizes of datasets begin to increase, this protocol becomes slow - this is where SEJITS specializers come in.

Now, a specializer-writer wrote a SEJITS specializer that helps facilitate an optimized reduction (we want to do a sum-reduction). So let's define the method that represents our intent.

def add(x, y):
    return x + y

Very simple; note that we're only defining a method that takes in two arguments, not the entire array. This is what is known as a kernel function that we will pass into the SEJITS framework so that it knows how to operate on our dataset. We could also do this using a lambda expression, to make our code more concise:

add = lambda x, y: x + y      # be sure that you assign your lambda expression to a variable, as we did here

Now, all we have to do is make a few calls into the SEJITS specializer that has been written for us. You'll notice that in main.py of this repository, LazyRolledReduction is one of the subclasses of LazySpecializedFunction (this is something that a specializer-writer would have written). We'll use this in the following code.

RolledClass = LazyRolledReduction.from_function(add, "RolledClass")         # generate subclass with the add() method
sum_reducer = RolledClass()                                                 # create your reduction function, sum_reducer       
sejits_result = sum_reducer(sample_data)

Here, we created a reducer function (which we called sum_reducer) by simply dynamically creating a class called RolledClass that has the add() method that we defined earlier in it. The way we did this is the same even if we wanted to use a different function (for example, if we had a multiply() function rather than an add() function, we could use multiply instead of add in the first line of the above code). Finally, we called the sum_reducer on our sample_data and got our output.

If we now print sejits_results, we get 2568, which is the correct result. One of the best parts is that if we ever run this code again with the same data size, our caching mechanism improves performance significantly.

There are two ways of running the sample code.

The first is to head into terminal, change directory into where you cloned this repository and run the following command:

>>> python ReductionSpecializer/main.py <x> <y> <z>

Here, we're using <x>, <y> and <z> as command line arguments. Here's what they represent:

1. <x> is an integer that represents the GPU number. If you don't care, choose 0.
2. <y> is an integer that represents the work group size you want.
3. <z> is an integer that represents the size of your dataset of ones.

An example of this would be:

>>> python ReductionSpecializer/main.py 0 32 2048

The second way to do this is to simply run the tests in the tests.py file. After changing into this repository's directory on your machine, simply run:

>>> python ReductionSpecializer/tests.py