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

Atomic operations are CPU instructions that are guaranteed to execute in a single CPU cycle. This means that the operation is guaranteed to complete without being interrupted by the actions of another thread. Although this may not sound too exciting, careful programming using these instructions can lead to algorithms and data structures that can be used in parallel without needing locks. Maple currently does not support atomic operations, however they are an interesting tool and are used in the kernel to help improve Maple's parallelism in general.

I recieved this comment on an older post of mine, and I was going to post a reply, but I noticed my reply was pretty long, and I was also missing a post for last friday, so I figured I'd cheat and make the reply a blog post of its own.

In this post I'll take a closer look at the ways in which Maple code can be thread unsafe. If you have not already seen my post on Thread Safety, consider reading that post first. As a brief review, a procedure is thread safe if it works correctly when run in parallel.

The most obvious way in which procedures can be thread unsafe is if they share data without synchronizing access (using a Mutex, for example). So how can two threads share data?

I am going to wander away from parallel programming in Maple, to talk about GPU programming. However I will show an example of connecting Maple to a CUDA accelerated external library, so that's close enough.  This post is not intended to be a tutorial on CUDA or OpenCL programming, but an introduction to how the technology works.

I'd like to start by thanking all those readers who left feedback on my last post. It was good hear that most of you enjoy reading my posts and that they are generally helpful. I would like to encourage you to continue posting feedback, especially questions or comments about anything that I fail to explain sufficiently.

The following is a discussion of the limitations of parallel programming in Maple. These are the issues that we are aware of and are hoping to fix in future releases.

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