Matthieu Brucher's blog

Due to the end of the free lunch, manufacturers started to provide differents processing units and developers started to go parallel. It’s kind of back to the future, as accelerators existed before today (the x87 FPU started as a coprocessor, for instance). If those accelerators were integrated into the CPU, their instruction set were also.

Today’s accelerators are not there yet. The tools are not ready yet (code translators) and usual programming practices may not be adequate. All the ecosystem will evolve, accelerators will change (GPUs are the main trend, but they will be different in a few years), so what you will do today needs to be shaped with these changes in mind. How is it possible to do so? Is it even possible?
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After reviewing Parallel Studio, I’ve decided to look after Advisor Lite. Intel offers it for free, before the actual Advisor is released with a future Parallel Studio version. It aims at steering multithreaded development with Parallel Studio.
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I’ve played a little bit with Intel Parallel Studio. Let’s say it has been a pleasant trip out in the wildness of multithreaded applications.

Intel Parallel Studio is a set of tools geared toward multithreaded applications. It consists of three Visual Studio plugins (so you need a fully-fledged Visual Studio, not an Express edition):

• Parallel Inspector for memory analysis
• Parallel Amplifier for thread behavior and concurrency
• Parallel Composer for parallel debugging

This is an update of the review I’ve done for the beta version. Since this first review, I’ve tried the official first version.

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Since this post, Intel has officially released Parallel Studio. This is why I’ve published a new, up-to-date review here.

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Some months ago, I had a TotalView tutorial, thanks to my job. Now, I’ve actually used it to debug one of my parallel applications and I would like to share my experience with fantastic tool.
First TotalView is not only a parallel debugger available on several Linux and Unix platforms. It also is a memory checker (MemoryScape and the TotalView plugin) as well as a reverse debugger, that is, you can roll back the execution of a program, even after it crashed (where it would be useless with a standard debugger like GDB).
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Like other programming models, there are some patterns in selecting the right parallel solution when it comes to designing a parallel application. This is what this book is about. The solutions may be obvious, but patterns aften are.
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I came across the issue of how to teach a trainee how to write a parallel finite-difference time-domain (FDTD) method. There are a lot of books on the FDTD, but only a few on parallel ones. So I’ve decided to go for this book, knowing that some chapters won’t apply to our job (wave equations). My goal was to seek a book that would explain the basics of my issues.
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For each algorithm and program, there are architectures that are better than others. Some computation may need a lot of FLOPS, but FLOPS are not the only thing to consider. Communication and memory bandwidth and latency are as important as computational power, specially since memory speed and CPU speed are decoupled.

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I had this discussion with one of my Ph.D. advisors some months ago when we talked about correctly using the computers we had then (dual cores), and I had almost the same one in my new job here: applied maths (finite differences, signal processing, …) graduate students are not taught how to use current computers, so how could they develop an HPC program correctly?

I think it goes even further than that, and it will be a part of this post. What I see is that trainees and newly-hired people (to some extent myself included) lack a lot of basic Computer Science knowledge, and even IT knowledge.
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I was looking for an introductory book on peer-to-peer (P2P) application and their application to grid computation. Web services was a bonus, as it is something I don’t usually play with.
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