Tag Archives: Intel

Review of Intel Parallel Studio 2017: Advisor

Recently, I got access to the latest release of Parallel Studio with an update version of Advisor. 6 years after my last review, let’s dive into it again!
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Review of Intel Parallel Advisor (part of Parallel Studio 2011)

After my review of Intel Parallel Studio and then my post of Advisor Lite, I had the opportunity of doing the beta of Intel Advisor and then the final version of Parallel Studio.

The review will not be as thorough as the one on Advisor Lite, because Advisor is an update of Advisor Lite. It has some additional features, and that’s what I’d like to focus on.

I won’t dwell into the details of Intel’s new offer, suffice to say that Intel took the opportunity of changing some offers name and of incorporating some parts of Parallel Studio in its other products, and of course on Linux (which was left alone until then).

Continue reading Review of Intel Parallel Advisor (part of Parallel Studio 2011)

Parallel Studio: Using Advisor Lite

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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Review of Intel Parallel Studio

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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A quick hack to use the MKL with numpy/scipy on Linux

I’ve promised to make an update whenever I would find a solution to the problem I had some months ago when I tried to use the latest MKL with numpy. Well, there was a simple hack that did the trick. It is far from being perfect, but at least, the tests pass now.
So the only thing you have to do is to export the LD_PRELOAD variable:

export LD_PRELOAD=/path/to/the/MKL/lib/libmkl_core.so