dohashi

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14 years, 348 days
I am a Senior Software Developer in the Kernel Group, working on the Maple language interpreter. I have been working at Maplesoft since 2001 on many aspects of the Kernel, however recently I have been focusing on enabling parallel programming in Maple. I have added various parallel programming tools to Maple, and have been trying to teaching parallel programming techniques to Maple programmers. I have a Master's degree in Mathematics (although really Computer Science) from the University of Waterloo. My research focused on Algorithm and Data Structure, Design and Analysis.

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These are Posts that have been published by dohashi

I've had a few request to provide some more information on External Calling, so I thought I would make a few posts about it. This first post will be a high level description of External Calling and how it works, with examples coming later. As External Calling is an advanced topic, I am going to assume you know how to compile a shared library and are generally familiar with the C language. Although this first post won't require any real programming knowledge.

What is External Calling?

External Calling is the name for Maple's ability to connect to and call functions from other programming languages. Maple uses this for various reasons. We have written our own libraries in C, C++ and Java to solve particular problems. We partner with various labs around the world who have developed code, often in languages like C or C++, so external calling is used to interface with their code. We also connect to high performance libraries like NAG and BLAS to provide those high performance routines in Maple. Of course, you can use External Calling to connect Maple to your code as well.

Although Maple can call various programming languages, the most common languages we connect to are C and C++, and those are the languages I am going to focus on.

How does it work?

In Maple, you call ?define_external or use the ?ExternalCalling package. Both these methods take a description of the function that you want to call and returns a Maple procedure. Normally you would assign the procedure to a name and then call the externally defined function just like any other Maple procedure.

There are a couple different ways to use define_external to connect to a shared library, the differences are mostly concerned with how the parameters given in Maple are converted to parameters used in the external function.

  • Wrapperless external calling. With wrapperless external calling, Maple calls a function implemented in the shared library by automatically converting the values given in Maple into valid types for the external function.
  • Generated wrappers: With generated wrappers, Maple automatically generates a small C library that handles conversions from Maple values to the values used in the external function. Using generated wrappers allows Maple to handle more data types, like call back procedures.
  • Custom wrappers: A custom wrapper is a C function that you write yourself. This function accepts arguments as Maple data structures and returns a Maple data structure. You are responsible for converting the Maple data structures into whatever forms you need and converting your computed value back into a Maple data structure. Maple provides the External Calling API to assist in working with Maple from the externally defined function.

The first two forms of external calling are the easiest to do, however they are also the most limited. Internally we exclusively (I think) use the third, custom wrapper, form of external calling. That is the form I am going to talk about.

Custom Wrapper

The name "custom wrapper" is a bit of a misnomer. The function that you write does not need to "wrap" anything, it can implement anything you want. As long as you can convert the result into a Maple data structure, you can pass it back into Maple. In fact Maple also supports returning generic data, via the ?MaplePointer routines, but that is a more complex topic for a later blog post.

Your external function is simply a C function with the following calling convention:

ALGEB CustomWrapper( MKernelVector kv, ALGEB args )

ALGEB is the C data type that represents a Maple data structure. The MKernelVector is a data structure that acts as an intermediary between your external calling routines and the Maple engine. You will need to pass this structure back into the External Calling API functions. Both of these types, plus the External Calling API functions are defined in a header, maplec.h, that needs to be included in your code. I will provide more details when I provide examples.

The External Calling API

The External Calling API is a set of functions that we make available for working with the Maple Engine from external code. Maple also allows third party applications to load the Maple engine as a shared library, we call this ?OpenMaple. The External Calling functions are also available in OpenMaple, so you will often see OpenMaple used in the Maple help pages. Most functions can be used in both OpenMaple and External Calling, except for a few that are OpenMaple specific and involve starting and stopping the Maple Engine.

Maple's help system documents all the External Calling functions so you can see what is available. There is an overview of the external calling functions on this page, ?ExternalCalling,C,API. Briefly, however there are functions for converting Maple types to C and back, creating and interacting with Maple data structures (list, set, rtable, table, string, etc), creating and interacting with Maple language elements (names, procedures, etc), printing to the Maple interface, memory allocation, evaluating Maple statements and raising exceptions. There is even a C interface to the Task Programming Model.

Next Time...

In my next post I will provide some examples of using the External Calling API to actually do stuff in an externally defined procedure.  However, I am going to spend some time trying to figure out the easiest way for you to get the tools you'll need to be able to develop externally defined functions yourself, so my next post might take a bit of time.

Darin

Although I mostly post about parallel programming, my background is in algorithms and data structures.  I have a soft spot for sorting algorithms.  It probably started when I did some research into adaptive sorting as part of my coursework.  Anyway, someone added sound to visualizations of different sorting algorithms.  I'm not sure if it really helps explain the algorithms any better, but it does make them more interesting to watch.

http://www.geek.com/articles/geek-cetera/sorting-algorithms-quite-boring-until-you-add-sound-effects-20100819/

Darin

Consider the following C code:

It has been a while since my last post, mostly because of a combination of getting Maple 14 ready to ship and a lack of meaty topics to write about. I am trying to get back into the habit of posting more regularly. You can help me achieve my goal by posting questions about parallel programming. I'll do my best to answer. However for now, I'll give a brief overview of the new parallel programming features in Maple 14.

A new function has been added to the Task Programming Model. The Threads:-Task:-Return function allows a parallel algorithm implemented on top of the Task Programming Model to perform an early bail out. Lets imagine that you have implemented a parallel search. You are looking for a particular element in a large set of data. Using the Task Programming Model, you've created a bunch of tasks, each searching a particular subset of the data. However one of the first tasks to execute finds the element you are looking for. In Maple 13, there was no built in way of telling the other tasks that the result have been found and they they should not execute. In Maple 14, the Return function allows one task to specify a return value (which will be returned from Threads:-Task:-Start) and signal the other tasks that the algorithm is complete and that additional tasks should not be executed. Tasks that are already running will still run to completion, but tasks that have not started executing will not be started.

You may have noticed that there is a race condition with Return. What happens if two tasks both call Return in parallel? Only one of the values will become the value that is passed to Threads:-Task:-Start. I suppose I could say the "first" value is the one that is used, but really, what does that mean? If you call Return, then the value passed to Return should be an acceptable result for the algorithm.  If you call Return more than once, any of those values should be valid, thus it shouldn't matter which one becomes the return value.  That said, the Return function does give some feedback. In the task that succeeds in having its value accepted, Return will return true. In all other tasks that call Return, it will return false. This allows the code to know if a particular result was or was not accepted.

Maple 14 also adds the Task Programming Model to the C External Calling API. This means that you can write your algorithms in C and make use of the Task Programming Model. The C API is similar to the Maple API, with a few differences. In particular, you need to create each child task individually, instead of as a single call to Continue, as you would in Maple. As well, because it is C code, you need to worry about a few details like memory management that are handled automatically in Maple.  Using External Call is fairly advanced, so I won't go into too much detail here.  If you'd like to see more details of using the Task Programming Model in External Calling, I can write a seperated post dedicated to that.

As with every release of Maple, we spent some time trying to make our existing functionality faster and more stable. For parallel programming, we reduced the overhead of using the Task Programming Model, as well as reducing the locking in the kernel (which should help improve parallelism). Of course many bugs have been fixed, which should make parallel programming more reliable in Maple 14.

Amdahl's Law is a formula for determining the theoretical speed up when parallelizing a function. For example, imagine we wanted to parallelize a function that spends 90% of its time in one algorithm. If there is a parallel version of that algorithm, how much faster would the entire function run with 2, 4 or more cores?

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