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Using Tasks Instead of Threads

Tasks are a lightweight alternative to threads that provide faster startup and shutdown times, better load balancing, an efficient use of available resources, and a higher level of abstraction.
Authored by admin Last updated on 07/05/2019 - 09:41
Article

Utilizando tarefas ao invés de threads

Tasks are a lightweight alternative to threads that provide faster startup and shutdown times, better load balancing, an efficient use of available resources, and a higher level of abstraction.
Authored by admin Last updated on 07/05/2019 - 09:53
Article

Code Sample: Optimizing Binarized Neural Networks on Intel® Xeon® Scalable Processors

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
Authored by Yash Akhauri Last updated on 03/21/2019 - 12:40
Article

Explicit Vector Programming in Fortran

No longer does Moore’s Law result in higher frequencies and improved scalar application performance; instead, higher transistor counts lead to increased parallelism, both through more cores and thr

Authored by Martyn Corden (Intel) Last updated on 03/27/2019 - 15:50
Article

Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

Overview
Authored by JON J K. (Intel) Last updated on 05/30/2018 - 07:00
Article

Large Matrix Operations with SciPy* and NumPy*: Tips and Best Practices

Introduction
Authored by Last updated on 07/06/2019 - 22:04
Article

Measuring performance in HPC

This is the first article in a series of articles about High Performance Computing with the Intel® Xeon Phi™ coprocessor.

Authored by Last updated on 07/06/2019 - 16:10
Article

Putting Your Data and Code in Order: Data and layout - Part 2

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Authored by David M. Last updated on 07/06/2019 - 16:40
Article

Exploiting Data Parallelism in Ordered Data Streams

This article identifies some of these challenges and illustrates strategies for addressing them while maintaining parallel performance.
Authored by admin Last updated on 07/05/2019 - 14:50