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Articles and news on parallel programming and code modernization

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.

Expressing Parallelism in C++ with Threading Building Blocks

Parallelism helps applications make the best use of processors on single or multiple devices. However, parallelism implementation itself can prove a challenging task. In this video, Mike Voss, principal engineer with the Core and Visual Computing Group at Intel discusses the benefits of Intel® Threading Building Blocks (Intel® TBB), a C++ library, and how it can simplify the work of adding parallelism without the need to probe into threading details.

Intel® Compilers Overview: Scalable Performance for Intel® Processors

Intel Compilers for C/C++ and Fortran empower developers to derive the greatest performance from applications and hardware. In this video, Igor Vorobtsov discusses nuances of Intel compiler features which enable high-level optimization, auto-parallelization, auto-vectorization, dynamic profile guided optimization, detailed optimization reports, inter-procedural optimization (IPO), and much more.

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.

Streamline Your HPC Setup with Intel Cluster Checker

“Understanding a cluster can be complex if tools are not available such as Intel Cluster Checker. Think of how many times users complain that their applications are not runing with the expected performance and how long it takes system administrators to diagnose the issue. With Intel Cluster Checker, diagnosing and debugging of these issues is easier and less complex. By usingthis tool, customers will be more statisfied and a higher return on the investment will be realized.”

Materials Science Modeling with VASP

In today’s world where science and engineering depend on the simulation of new materials and their behavior is of critical importance. New materials are constantly being designed and brought into product design in order to create products that can withstand many environmental conditions and still perform for their intended use. HPC is critical for the simulation of these materials and applications which perform at the fastest speed available on a given hardware platform can lead to earlier introduction of products that contain these materials.

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.

Video: Speed Your Code with Intel Parallel Studio XE

“Modern processors perform their best with parallel code that’s both vectorized and threaded, which can run more than 100 times faster more than serial code. So how can you accomplish this more easily through parallel programming? Enter Parallel Studio XE, a suite of tools that simplifies and speeds the design, building, tuning, and scaling of applications with the latest code modernization methods.”

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.