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Simplifying HPC Software Stack Management

While most of the fundamental HPC system software building blocks are now open source, dealing with the sheer number of components and their inherently complex interdependencies has created a barrier to adoption of HPC for many organizations. This is the first article in a four-part series that explores using Intel HPC Orchestrator to solve HPC software stack management challenges.

Diagnose Cluster Health with Intel® Cluster Checker

Intel® Cluster Checker, distributed as part of Intel® Parallel Studio XE 2018 Cluster Edition, provides a set of system diagnostics and analysis methods in a single tool to assist managing clusters of any size. “Think of Intel Cluster Checker as a clinical system that detects signs that issues affecting the health of the cluster exist, diagnoses those issues, and suggests remedies. Using common diagnostic tools signs that may indicate symptoms leading to a diagnosis and a possible solution.”

OpenMP at 20 Moving Forward to 5.0

This year, OpenMP*, the widely used API for shared memory parallelism supported in many C/C++ and Fortran compilers, turns 20. OpenMP is a great example of how hardware and software vendors, researchers, and academia, volunteering to work together, can successfully design a specification that benefits the entire developer community.

Intel Parallel Studio XE 2018 Released

Intel has announced the release of Intel® Parallel Studio XE 2018, with updated compilers and developer tools. It is now available for downloading on a 30-day trial basis. ” This week’s formal release of the fully supported product is notable with new features that further enhance the toolset for accelerating HPC applications.”

TensorFlow Deep Learning Optimized for Modern Intel Architectures

Researchers at Google and Intel recently collaborated to extract the maximum performance from Intel® Xeon and Intel® Xeon Phi processors running TensorFlow*, a leading deep learning and machine learning framework. This effort resulted in significant performance gains and leads the way for ensuring similar gains from the next generation of products from Intel. Optimizing Deep Neural Network (DNN) models such as TensorFlow presents challenges not unlike those encountered with more traditional High Performance Computing applications for science and industry.

More Than Ever, Vectorization and Multithreading are Essential for Performance

Employing a hybrid of MPI across nodes in a cluster, multithreading with OpenMP* on each node, and vectorization of loops within each thread results in multiple performance gains. In fact, most application codes will run slower on the latest supercomputers if they run purely sequentially. This means that adding multithreading and vectorization to applications is now essential for running efficiently on the latest architectures.

3X Performance Boost Using Intel Advisor and Intel Trace Analyzer in Astrophysics Simulations

On today’s processors, it is crucial to both vectorize (using AVX* or SIMD* instructions) and parallelize software to realize the full performance potential of the processor. By optimizing their MHD astrophysics applications with tools from Intel Parallel Studio XE, and running on the latest Intel hardware, the NSU team achieved a performance speed-up of 3X, cutting the standard time for calculating one problem from one week to just two days.

Go with Intel® Data Analytics Acceleration Library and Go*

Use of the Go* programming language and it’s developer community has grown significantly since it’s official launch by Google in 2009. Like many popular programming languages (C and Java come to mind), Go started as an experiment to design a new programming language that would fix some of the common problems of other languages and yet stay true to the basic tenets of modern programming: be scalable, productive, readable, enable robust development environments, and support networking and multiprocessing.

Deep Learning Frameworks Get a Performance Benefit from Intel MKL Matrix-Matrix Multiplication

Intel® Math Kernel Library 2017 (Intel® MKL 2017) includes new GEMM kernels that are optimized for various skewed matrix sizes. The new kernels take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and achieves high GEMM performance on multicore and many-core Intel® architectures, particularly for situations arising from deep neural networks..

Multicore Performance Challenges for Game Developers

Game developers face a unique challenge – how to make their graphics-heavy applications perform well across a very wide spectrum of hardware devices, not just high-end systems. So while an early version of a game might have been developed on some high-end system with 10 teraflops of CPU potential in a discrete graphics card, how do you scale it down to smaller consumer devices where optimization options are more limited?