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5 Tips for Speeding Up Your Code Using TBB

Intel Threading Building Blocks is a library to help your application use parallelization without you having learn all the subtleties of threading and to avoid the pitfalls. If you use TBB to speed up your software, below are a few tips to help you on your way. You don’t always …

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OpenMP vs. MPI: Which Is Better?

Unlike SIMD, where vectorization has multiple data items affected by a single instruction, in SPMD (Single Program Multiple Data) multiple processors run the same program but operate on different subsets of the data. There are two different ways that you can implement SPMD which depend upon the underlying architecture. If …

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Intel HPC Code Modernization Road Show Teaches Tuning for Xeon Phi

As multicore processors such as Xeon and Xeon Phi expand their presence in both on-premises and Cloud infrastructure, the need to tune your code to get the most bang for your processor buck becomes increasingly important. To that end, Intel is hosting a developer conference series that offers insights into …

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Parallel Computing in the Age of Big Data

The age of Big Data is here. With zettabytes of daily Internet traffic, analysis of even a small subset involves Big Data. The age old paradigm of sequential programming no longer suffices, it is essential to move to a parallel programming mode to service the needs of Big Data applications. …

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Unintended Consequences of Parallel Computing: How to Avoid Pitfalls

In this blog, we’ll take an in-depth look at parallel computing, and examine some of the pitfalls that can thwart efforts for great code to create applications that sing. Parallel Is Not Always Faster Than Sequential There are times when changing code from sequential to parallel will slow it down. …

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Intel’s James Reinders on Multicore, How to Future-Proof Your Code

Is your business getting the most out of all the cores in your processors? When you consider the rapid advances in today’s Xeon Phi coprocessors, the chances are much of your code would benefit from modernization. In this video, Intel Chief Evangelist James Reinders speaks to Colfax HPC research engineer …

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Data Normalization with SIMD Vectorization

Data sets often represent collected values that reflect real-world situations. For instance, census data might contain the ages of all residents within a certain township. Another example is when schools aggregate grade averages among classrooms. Many times, though, data collections have strong components with large magnitudes that tend to overwhelm …

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Bring New Life to Legacy C++ Code with Parallel Studio

In the software engineering class I teach, we discuss the need and importance of refactoring code. Over time, it is inevitable that code must be reworked to meet new needs. Sometimes customers complain about performance, while other times many software engineers want cleaner and more maintainable code. Using Intel Parallel …

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Parallel Power to the Programmer: Coding Course Leads the Way

As systems with multiple CPUS, each carrying multiple cores become increasingly popular for a large variety of workloads, organizations unsurprisingly want to take full advantage of each CPU and coprocessor that are part of the execution environment. Those who include Xeon Phi coprocessors as part of their infrastructure will be …

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Go Lock-Free to Keep Your Code Up to Speed

To get the best performance in parallel programming, you want to try to avoid locks and critical sections, which can slow down your code. In this blog, Jeff Cogswell investigates lock-free programming and explains briefly how it works. Then he’ll show you where to learn more from a master with …

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