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Avoiding Data Races with Reducers

Fixing what happens when simultaneous threads modify the SAME memory.

Parallelizing code drastically speeds execution, but if you’re not careful data race conditions can produce very unwanted results. See how Slashdot Media Contributing Editor Rick Leinecker gets at the problem and fixes it automatically with a reduction clause.

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Using Cilk Plus to Speed Parallel Code

Move Over, OpenMP: Cilk Plus struts it stuff. What’s the best way to parallelize for loops? In this blog, Slashdot Media Contributing Editor Rick Leinecker looks at Cilk Plus as an alternative to OpenMP Using Cilk Plus Rick Leinecker, August 2016 Many of my blogs that talk about parallelization use …

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Timing C++ Execution: Do Compilers Matter?

Execution speed can be impacted by choice of language AND compiler In this instructional video, Slashdot Media Contributing Editor David Bolton show us how to get the most performance out of C++ code by paying attention to compiler features that are in tune with hardware features.

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Intel C++ and Latency: How Fast is Fast?

Have modern processor caches and new compilers solved latency in multicore/multiprocessor environments? As the limits of Moore’s law are put to the test, processors strive to drive latency down and performance up. Here, Slashdot Media Contributing Editor David Bolton takes a hard look at the numbers and shares his results. …

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Basics of Parallelization – Part 2

Adding vectorization to multithreading delivers more performance in multicore environments Is your code optimized to take advantage of the many cores available in today’s systems? Parallel processing expert and Slashdot Media Contributing Editor Rick Leinecker addresses vectorization in part two of his series on parallel processing basics. I recently wrote …

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Hands-On Workshop Shows How To Modernize Code for Todays Systems

Webinar series focuses on adapting code to take advantage of latest Intel architectures Is your code taking advantage of all the feature of Intel Xeon and Knights Landing (Xeon Phi) processors? Ten 2-hour sessions presented by Colfax Research give you a hands-on experience, in this webinar series led by Colfax …

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Intel IT Shows How To Get A Lot More For A Lot Less

Annual report yields high-efficiency best-practices recommendations to increase flexibility, compute while saving costs How is Intel transforming its own infrastructure? In this blog, Slashdot Media Contributing Editor John O’Donnell takes a look at the annual Intel report card and shares best practices advice In addition to touting the benefits of …

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NumPy Arrays: Taking A Close Look

Accelerating Python array processing with NumPy If you use arrays in Python, chances are you should be using NumPy to improve performance. In this article, Slashdot Media Contributing Editor David Bolton takes a deep dive into NumPy, with comparisons to array processing in other languages. A Closer Look at NumPy …

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Free Online Course offers Deep Dive into Optimizing for Intel Architecture

Looking to get the most from Xeon or Xeon Phi? Colfax Research offers tutorials, self-study course to help. Optimizing code for parallel processing and vectorization can be a challenge, but help is here in the form of free web-based workshops offered by Colfax Research. Slashdot Media Senior Editor Michael Krieger …

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Threaded Building Blocks (TBB) at Ten Years

A decade of simplifying Parallel Programming In this video, Slashdot Media Contributing Editor Rick Leinecker talks about the Threaded Building Blocks (TBB) technology, and summarizes the major features of TBB. Rick also shows some code snippets to illustrate their use. This technology should be considered any time you are considering …

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