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Putting Computer Vision to Work with OpenVINO

OpenVINO is a single toolkit, optimized for Intel hardware, that the data scientist and AI software developer can use for quickly developing high-performance applications that employ neural network inference and deep learning to emulate human vision over various platforms. “This toolkit supports heterogeneous execution across CPUs and computer vision accelerators including GPUs, Intel® Movidius™ hardware, and FPGAs.”

Are Platform Configuration Problems Degrading Your Application’s Performance?

The Intel VTune™ Amplifier Platform Profiler on Windows* and Linux* systems shows you critical data about the running platform that help identify common system configuration errors that may be causing performance issues and bottlenecks. Fixing these issues, or modifying the application to work around them, can greatly improve overall performance.

Accelerated Python for Data Science

The Intel Distribution for Python takes advantage of the Intel® Advanced Vector Extensions (Intel® AVX) and multiple cores in the latest Intel architectures. By utilizing the highly optimized Intel MKL BLAS and LAPACK routines, key functions run up to 200 times faster on servers and 10 times faster on desktop systems. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.

Latest Intel Tools Make Code Modernization Possible

Code modernization means ensuring that an application makes full use of the performance potential of the underlying processors. And that means implementing vectorization, threading, memory caching, and fast algorithms wherever possible. But where do you begin? How do you take your complex, industrial-strength application code to the next performance level?

Learn What to Do Next with Intel VTune Amplifier Application Performance Snapshot

Tuning code has, for a long time, been an art. Knowing what to look for and how to correct inefficiencies in serious numerical computations has not been easy for most programmers. It’s often hard to even know which tool to start with. Which is why the Intel® VTune™ Amplifier Application Performance Snapshot could prove to be a great way to get an instant summary of an application’s performance characteristics and issues.

Intel Performance Libraries Accelerate Python Performance for HPC and Data Science

Python is now the most popular programming language, according to IEEE Spectrum’s fifth annual interactive ranking of programming languages, ahead of C++ and C. Recent Intel Distributions for Python show that real HPC performance can be achieved with compilers and library packages optimized for the latest Intel architectures. Moreover, the library packages targeted for big data analysis and numerical computation included in this distribution now support scaling for multi-core and many-core processors as well as distributed cluster and cloud infrastructures.

Use Intel Media SDK to Build Cross-Platform High-Quality Video Workflows

The latest release of Intel® Media SDK offers a single, cross-platform, GPU-enabled API for building optimized media and video applications from PC’s to workstations and into the cloud.

Deep Learning Open Source Framework Optimized on Apache Spark*

Intel recently released BigDL. It’s an open source, highly optimized, distributed, deep learning framework for Apache Spark*. It makes Hadoop/Spark into a unified platform for data storage, data processing and mining, feature engineering, traditional machine learning, and deep learning workloads, resulting in better economy of scale, higher resource utilization, ease of use/development, and better TCO.

Maximizing Performance of HiFUN* CFD Solver on Intel® Xeon® Scalable Processor With Intel MPI Library

The HiFUN CFD solver shows that the latest-generation Intel Xeon Scalable processor enhances single-node performance due to the availability of large cache, higher core density per CPU, higher memory speed, and larger memory bandwidth. The higher core density improves intra-node parallel performance that permits users to build more compact clusters for a given number of processor cores. This permits the HiFUN solver to exploit better cache utilization that contributes to super-linear performance gained through the combination of a high-performance interconnect between nodes and the highly-optimized Intel® MPI Library.

Data Compression Optimized with Intel® Integrated Performance Primitives

Intel® Integrated Performance Primitives (Intel IPP) offers the developer a highly optimized, production-ready, library for lossless data compression/decompression that targets image, signal, and data processing, and cryptography applications. The Intel IPP optimized implementations of the common data compression algorithms are “drop-in” replacements for the original compression code.