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Hands On with Intel DAAL Part 2: Can Uber Learn from London Taxi Data?

Data Science, Big Data and Machine Learning applied to London’s famous Taxi fleet In the first part of this series, Slashdot Media Contributing Editor David Bolton discussed collection of data from London Taxis to determine if scheduling can be improved. In Part two, he shows us how to use the …

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Basics of Parallelization – Part One

Basic tricks for parallelization code to take advantage of multiple cores and processors In today’s multicore systems, many programmers are leaving performance on the table. This multipart series shows how to get the most out of your hardware with multithreading and vectorization. Read on to see how Slashdot Media Contributing …

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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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Using Data Analysis Acceleration Library (DAAL) to Speed Taxi Scheduling

Part 1 of 2 – Collecting data from London’s famed Taxis to optimize routes Is “the Knowledge” enough to keep London taxis on track? In the first part of this two part series, Slashdot Media Contributing Editor David Bolton looks at London’s famous taxis, and collects data from the taxi …

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Parallel Python – Making Code Run 2000x Faster

“Embarrassingly Parallel” code acceleration with Intel Python, OpenMP and Cython In this video, Slashdot Media Contributing Editor David Bolton shows how ‘embarrassingly parallel’ code such as calculating by can be sped up over 2000x (not percent) by utilizing Intel tools including the Intel Python compiler and OpenMP    

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Using Dask Library in Intel Python

Working with a parallel library for analytics and integrating with TBB There is no shortage of analytics tools, especially for Python. In this hands-on blog, Slashdot Media Contributing Editor David Bolton looks at Dask, which provides a rich set of parallelization tools that work well with Intel’s Threaded Building Block …

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Using Pipeline in Intel Threaded Building Blocks (TBB) – Part 2

Utilizing threading tools to enhance application performance  In part two of this series, Slashdot Media Contributing Editor David Bolton provides real-world examples of how Intel Threaded Building Blocks (TBB) can accelerate both the time to develop and execute threaded code on today’s multicore and multiprocessor systems. In part one, I …

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Multithreaded Python with OpenMP

Unleashing multiple threads with Python, Cython and OpenMP As Python continues to percolate through the world of multiprocessing and parallel computing, the need to integrate with tools like OpenMP becomes increasingly important. Watch Slashdot Media Contributing Editor David Bolton demonstrate how to do just that.

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Intel Python for Linux: A Quick Look at 2017 Beta

Latest Intel Python for Linux Beta optimized for performance, supports RHEL and Ubuntu In this blog, Slashdot Media Contributing Editor David Bolton takes a look at the Intel Python 2017 Beta release, with support for a broad range of packages including NumPy, SciPy, Numba, and Intel’s Data Analytics Acceleration Library …

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Faster Software through Vectorization and Parallelization

How can developers continue to achieve performance gains? Here’s a look at how to take advantage of the cores at your disposal Everyone agrees there is a need for speed, and pressure is on developers and engineers to deliver better performance from every new piece of hardware. In this post, …

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