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Scale Performance from Multicore to Many-Core

Implementing a parallel algorithm can get frustrating at times. Developers can thread an application, but see little performance gain. Or they can hit a scalability ceiling where performance gains level off as more cores are added. A newly updated tool, the Advisor XE 2015, can help solve those issues by …

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Army of Parallel Programming Experts Shares Tricks of the Trade

In this video, author and Intel parallel computing evangelist James Reinders discusses his latest book, “High Performance Parallelism Pearls: Multicore and Many-core Programming Approaches,” an instructional volume packed with learnings from 69 contributing authors in 28 chapters that are structured to show you how to get the most performance from …

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Putting Every Core to Work: Using Graphics Power for General Purpose Computing

Graphics engines aren’t just for graphics anymore. In this video, Intel Technical Consulting Engineer Anoop Madhusoodhanan Prabha discusses a new use for that graphics power, namely boosting your general purpose computing horsepower. Watch this video to see how you can use the newest compiler features to channel the capabilities of …

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Allocate Memory Effectively in TBB

Threading Building Blocks (TBB) includes drop-in replacement functions for the C-type memory allocation functions, as well as replacements for the C++-type allocators. Jeff Cogswell introduces them. When you’re using Threading Building Blocks and writing an app that makes use of the basic memory allocation functions, there’s a feature whereby you …

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Cray Enters New Territory with Chapel Parallel Language

Supercomputer manufacturer Cray is developing a general-purpose, parallel programming language called Chapel that supports Intel architecture. Jeff Cogswell gives you a quick tour. When people think of Cray, they usually think of supercomputers. But something else is coming out of the company: A language for parallel programming that is portable …

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Apache Spark Lets You Take Parallel Programming Across a Cluster

Parallel programming in a cluster of nodes shares many of the same concepts as parallel programming across cores and vectors. Apache Spark is a clustered, in-memory processing engine that lets you scale your software across thousands of nodes. Jeff Cogswell introduces it. When working in parallel programming, we often live …

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Multithreaded Code Made Easy with Worksharing Constructs

OpenMP includes four constructs that together make up what are called worksharing constructs. Three of these are available in C++ and all four are available in Fortran. Jeff Cogswell shows you how you can use them to easily write multithreaded code. OpenMP includes many different constructs for dividing up your …

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Cancel TBB Threads Using Exceptions

Threading Building Blocks has a built-in system that allows for canceling groups of threads. The system is implemented using C++’s own exception handling system. Jeff Cogswell shows you how to use it. In any type of parallel coding system, there may be situations where you need to cancel a set …

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How to Effectively Use OpenMP’s Execution Model

To get the most out of OpenMP, you need to have a clear understanding of its execution model, including how and when threads are created and destroyed. Jeff Cogswell explains. When working with OpenMP, it’s important to understand how the threads are created and destroyed, as part of the overall …

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Manage Memory Better with OpenMP

OpenMP uses a memory model that allows threads to use shared and private variables. The model works together with the execution model. In this blog, Jeff Cogswell shows you how to manage your OpenMP memory effectively. OpenMP uses a shared memory approach whereby separate threads share the same memory. But …

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