Dekker’s Algorithm Does not Work, as Expected

Sometimes it is very reassuring that certain things do not work when tested in practice, especially when you have been telling people that for a long time. In my talks about Debugging Multicore Systems at the Embedded Systems Conference Silicon Valley in 2006 and 2007, I had a fairly long discussion about relaxed or weak memory consistency models and their effect on parallel software when run on a truly concurrent machine. I used Dekker’s Algorithm as an example of code that works just fine on a single-processor machine with a multitasking operating system, but that fails to work on a dual-processor machine. Over Christmas, I finally did a practical test of just how easy it was to make it fail in reality. Which turned out to showcase some interesting properties of various types and brands of hardware and software.

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Book Review: Intel’s Multicore Programming Book

Multicore programming book coverThe book “Multicore Programming – Increasing Performance through Software Multithreading” by Shameem Akhter and Jason Roberts is part of a series of books put out by Intel in their multicore software push. In case you have not noticed, Intel has a huge market push currently where they give seminars, publish articles and books, and give curricula to universities in order to get more parallel software in place. I read this book recently, and here is a short review.
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When Multicore makes Things Simpler, like IMA

Most of the time when talking about the impact of multicore processing on software, we complain that it makes the software more complicated because it has to cope with the additional complexities of parallelism. There are some cases, however, when moving to multicore hardware allows a software structure to be simplified. The case of Integrated Modular Avionics (IMA) and the honestly idiotic design of the ARINC 653 standard is one such case.
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Øredev 2007

Öredev logo

Just like in 2006, I went to the Øredev conference in Malmö and presented a workshop using Virtutech Simics. This year, I worked with Jonas Svennebring from Freescale and we created a workshop around parallelizing network processing software for running on a multicore Freescale processor. The workshop went reasonably well, and the participants definitely learned something about what we trying to get across, even though we did not have much time to actualy complete the programming assignments.

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Homogeneous and Heterogeneous Multicore vs Programmers

An old colleague just sent me an email bringing up a discussion we had last year, where he was a strong proponent for the homogeneous model of a multiprocessor. The root of that discussion was the difference between the Xbox 360 and Playstation 3 processors. The Xbox 360 has a three-core, two-threads-per-core homogeneous PowerPC main processor called the Xenon (plus a graphics processor, obviously), while the PS3 has a Cell processor with a single two-threaded PowerPC core and seven SPEs, Synergistic Processing Elements (basically DSP-like SIMD machines).

In the game business, it is clear that the Xenon CPU is considered easier to code for. This means that even though the Cell processor clearly has higher theoretical raw performance, in practical the two machines are about equal in power since it is harder to make use of the Cell. Which seems to be a fact.

So here, homogeneous systems do appear to have it easier among programmers. However, I do not believe that that extends to all systems, all the time, everywhere.

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SICS Multicore Day 2007 – More on Programming

Some more thoughts on how to program multicore machines that did not make it into my original posting from last week. Some of this was discussed at the multicore day, and others I have been thinking about for some time now.

One of the best ways to handle any hard problem is to make it “somebody else’s problem“. In computer science this is also known as abstraction, and it is a very useful principle for designing more productive programming languages and environments. Basically, the idea I am after is to let a programmer focus on the problem at hand, leaving somebody else to fill in the details and map the problem solution onto the execution substrate.

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