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DKRZ German Weather Forecasting Service Visualization Demo

Last updated: October 17, 2017Video length: 2 min

Software on Intel® Xeon® processors doing high quality rendering for visual data representation.

Intel® Xeon Phi™ Processor Applications

This presentation is an expanding collection of Intel® Xeon Phi™ processor application showcase and proof points that demonstrate improved software performance for key applications and benchmarks in key business segments, such as Manufacturing, Life Sciences, Finance, Energy and more.

Best of Modern Code October

The Best of Modern Code | October

Don't miss your chance--Register now for the Intel® HPC Developer Conference happening next month. This month you can read about cells in the cloud, future-proofing your code and mode collapse in GANS.

October Top Ten

Top Ten Intel Software Developer Stories | October

Learn to build a face access control solution, get horrified in a haunted high school, and be sure to register for the Intel® HPC Developer Conference this month.

Improve Performance Using Vectorization and Intel® Xeon® Scalable Processors

Introduction Modern CPUs include different levels of parallelism. High-performance software needs to take advantage of all opportunities for parallelism in order to fully benefit from modern hardware. These opportunities include vectorization, multithreading, memory optimization, and more. The...

Writing own-vector algorithms in OpenJDK* for faster performance

In this paper, we discuss insights into Vector API, which is being developed as part of OpenJDK* under Project Panama. First, we’ll go over some Vector API fundamentals, basic functionalities, and tips. We’ll then show you some code samples of vector algorithms for standard Machine Learning...

Achieving High-Performance Computing with The Intel® Distribution for Python*

Last updated: September 29, 2017Video length: 30 min

Learn how to accelerate Python* for advanced numerical, scientific, and machine learning workloads utilizing the Intel® Distribution for Python*.

Intel® Accelerates Hardware and Software Performance for Server-Side Java* Applications

Intel® contributes significantly to both software and hardware optimizations for Java*. These optimizations can deliver performance advantages for Java applications that run using the optimized Java Virtual Machine (JVM), and which are powered by Intel® Xeon® processors and Intel® Xeon Phi™...

Lab7 Systems Helps Manage an Ocean of Information

Finding efficient ways to manage the massive amounts of data generated by new technologies is a key concern for many industries. It’s especially challenging in the world of life sciences, where research breakthroughs are based on an ever-expanding ocean of information. With help from Intel and...

Intel® Xeon Phi™ Processor Software

Download the software and utilities that enable functionalities of the Intel Xeon Phi Processor. Supported Operating Systems are: CentOS 7.2, SuSE Linux Enterprise Server (SLES) 12 and 12 SP1 and Red Hat Enterprise Linux 7.2.

Quick Start Guide for the Intel® Xeon Phi™ Processor x200 Product Family

Introduction This document describes the process for taking the Intel® Xeon Phi™ processor from the point where the hardware has been received up to the point where the processor is ready to be used by the programmer. This document does: Provide a high level overview of the architecture of the...

Vector API Developer Program for Java* Software

This article introduces Vector API to Java developers, it shows how to start using the API in Java programs, and provides examples of vector algorithms. It provides step-by-step details on how to build the vector API and build java applications using it. It provides the location for downloadable...

Using Intel® Math Kernel Library Compiler Assisted Offload in Intel® Xeon Phi™ Processor

Introduction Beside native execution, another usage model of using the Intel® Math Kernel Library (Intel® MKL) on an Intel® Xeon Phi™ processor is the compiler assisted offload (CAO). The CAO usage model allows users to offload Intel MKL functions and data to an Intel Xeon Phi processor by using...

Improving Performance of Math Functions with Intel® Math Kernel Library

Introduction Intel® Math Kernel Library1 (Intel® MKL) is a product that accelerates math processing routines to increase the performance of an application when running on systems equipped with Intel® processors. Intel MKL includes linear algebra, fast Fourier transforms (FFT), vector math, and...

Intel® HPC Developer Conference: For the HPC Practitioner

Last updated: September 19, 2017

Time to sign up for the Intel HPC Developer Conference Nov 11-12 in Denver, Colorado

Applications for Latency

The Best of Modern Code | September

Find out who is speaking at the HPC Developer Conference, do some fast computations, and a little deep learning this month.

Intel® Xeon® Processor Scalable Family Technical Overview

The new generation, the Intel® Xeon® processor Scalable family (formerly code-named Skylake-SP), is based on 14nm process technology, with many new and enhanced architecture changes including, Skylake Mesh Architecture and Intel® Advanced Vector Extensions 512 (Intel® AVX-512).

Modernizing Software with Future-Proof Code Optimizations

by Henry A. Gabb, Sr. Principal Engineer, Intel Software and Services Group Create High Performance, Scalable and Portable Parallel Code with New Intel® Parallel Studio XE 2018 Intel® Parallel Studio XE is our flagship product for software development, debugging, and tuning on Intel processor...

Intel® Xeon® Processor Scalable Family

Last updated: September 13, 2017Video length: 4 min

New micro architecture and technical features of the Intel® Xeon® Scalable Processor Family.

September Top Ten

Top Ten Intel Software Developer Stories | September

Find out who won the 2017 Intel® Level Up Game Developer Contest. Plus we discover vectorization in games as well as profiling TensorFlow* workflows.

Gentle Introduction to PyDAAL: Vol 1 of 3 Data Structures

The Intel® Data Analytics Acceleration Library (Intel® DAAL) is written on Intel® architecture optimized building blocks and includes support for all data analytics stages. Data-driven decision making is empowered by Intel® DAAL with foundations for data acquisition, preprocessing, transformation,...

Pre-Processing GeoTIFF files and training DeepMask/SharpMask model

Last updated: September 8, 2017By Abu Bakr

For this project, UNOSAT is responsible for providing us with satellite images. At first, we will be using GeoTIFF files of Muna refugee camps, Nigeria. You can find the map analyzed by the UN on this link. Other than these images we were also provided with shapefile and geodatabase files. We can...

Cells in the Cloud: Distributed Runtime Prototype Implementation

Hello, everyone! In the previous part of this blog post series, we presented the nature of the simulations performed by the BioDynaMo project. Moreover, we observed how our desired requirements and constraints for the distributed runtime affected the design of the architecture and defined the...

Develop and Test SAP HANA-based Applications on an Intel NUC Mini-PC

SAP is well-known as the originator of “enterprise resource planning” (ERP), the business process automation software that manages back office functions through a system of integrated applications. Another game-changing innovation from the enterprise software leader is the SAP* HANA* in-memory...

Intel® Xeon® Scalable Processor Cryptographic Performance

Executive Summary The new Intel® Xeon® Scalable processor family provides dramatically improved cryptographic performance for data at rest and in transit. Many Advanced Encryption Standard (AES)1 based encryption schemes will immediately benefit from the 75 percent improvement in Intel® Advanced...

Intel® HPC Developer Conference: Get Enabled

It's soon, everyone. The conference you want to go to. It's the Intel® HPC Developer Conference 2017. This isn't for those who are performance-curious; it's for everyone who wants to maximize their hardware's potential. You'll get the latest in technical knowledge plus the hands-on experience you...

Cells in the Cloud: Thoughts on the Distributed Architecture

Hello, everyone! In the first part of this blog post series, we introduced the BioDynaMo project, which is part of CERN openlab. Specifically, we gave an overview of the project's history, current status and the goal for this summer. The latter being implementing a distributed runtime prototype...

DeepMask Installation Problems and Solutions (3)

Last updated: August 28, 2017By Abu Bakr

These are some of the problems that I encountered while installing and training a DeepMask model. 1. module coco not found Install the lua coco package by following the steps mentioned below: Clone coco repository: git clone https://github.com/pdollar/coco Under coco/ run the following...

Identify Performance Hotspots Using Intel® VTune™ Amplifier for Windows*

Last updated: August 25, 2017Video length: 5 min

As a first step, use the Intel® VTune™ Amplifier to identify the functions, loops, and files that have the biggest impact on your application’s performance.

Deep learning for fast simulation: Introduction

Last updated: August 25, 2017By Elena O.

Hello everyone! Let me tell you more about my project. I started working on my project in the beginning of July. My main task was to create a Generative Adversarial Networks (GANs) model to simulate the passage of a particle through matter. Why GANs? I’ll explain it later. Since I've never heard...

DeepMask Installation and Annotation Format for Satellite Imagery Project (2)

Last updated: August 25, 2017By Abu Bakr

When we look at satellite images of refugee camps, we can easily distinguish between shelters and other different objects. If we want to do analysis on the basis of this information, it becomes a really laborious task due to the presence of hundreds of different objects and thus results in a...

Track Reconstruction with Deep Learning at the CERN CMS Experiment

This blog post is part of a series that describes my summer school project at CERN openlab. In the first post we introduced the problem of track reconstruction and the track seeds filtering. Today we are going to discuss the model architecture and the results. Understanding the data Our dataset...

Getting to Small Batches in Hardware Design using Simulation

In the previous part of this two-part blog, I discussed the general principle of doing work in small batches, the great benefits that it brings, and how the principle can be applied outside the traditional software development domain. In this part, I will discuss some more concrete examples about...

Cells in the Cloud: Scaling a Biological Simulator to the Cloud

Hello, fellow developers! I am Konstantinos, and I currently work at CERN as a research intern for this summer. I was accepted by the BioDynaMo (a.k.a Cells in the cloud) project, which is one of the projects that are part of the CERN openlab program. For those who are not familiar with the CERN...

IoT in LHC: A Deeper Look into the Frameworks

I’m Lamija Tupo, a student from Sarajevo, and I’m a summer student in CERN as part of CERN Openlab. I’m working on ‘Edge Computing: Integrating IoT Devices into the LHC Control Systems’ project using the Intel® Joule™ development board. The final decision on which framework to use should be based...

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