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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.

The OpenMP API Celebrates 20 Years of Success

OpenMP is a good example of how hardware and software vendors, researchers, and academia, volunteering to work together, can successfully design a standard that benefits the entire developer community. Today, most software vendors track OpenMP advances closely and have implemented the latest API features in their compilers and tools. With OpenMP, application portability is assured across the latest multicore systems, including Intel Xeon Phi processors.

C++ Parallel STL Introduced in Intel Parallel Studio XE 2018 Beta

Parallel STL now makes it possible to transform existing sequential C++ code to take advantage of the threading and vectorization capabilities of modern hardware architectures. It does this by extending the C++ Standard Template Library with an execution policy argument that specifies the degree of threading and vectorization for each algorithm used.

Intel Advisor Roofline Analysis Finds New Opportunities for Optimizing Application Performance

Intel Advisor, an integral part of Intel Parallel Studio XE 2017, can help identify portions of code that could be good candidates for parallelization (both vectorization and threading). It can also help determine when it might not be appropriate to parallelize a section of code, depending on the platform, processor, and configuration it’s running on. Intel Advisor Roofline Analysis reveals the gap between an application’s performance and its expected performance.

Intel® VTune™ Amplifier Turns Raw Profiling Data Into Performance Insights

Discovering where the performance bottlenecks are and knowing what to do about it can be a mysterious and complex art, needing some very sophisticated performance analysis tools for success. That’s where Intel® VTune™ Amplifier XE 2017, part of Intel Parallel Studio XE, comes in.

Intel MKL and Intel TBB Working Together for Performance

When used in a TBB environment, Intel has demonstrated a many-fold performance improvement over the same parallelized code using Intel MKL in an OpenMP environment. Intel TBB-enabled Intel MKL is ideal when there is heavy threading in the Intel TBB application. Intel TBB-enabled Intel MKL shows solid performance improvements through better interoperability with other parts of the workload.

Intel MPI Library 2017 Focuses on Intel Multi-core/Many-Core Clusters

With the release of Intel Parallel Studio XE 2017, the focus is on making applications perform better on Intel architecture-based clusters. Intel MPI Library 2017, a fully integrated component of Intel Parallel Studio XE 2017, implements the high-performance MPI-3.1 specification on multiple fabrics. It enables programmers to quickly deliver the best parallel performance, even if you change or upgrade to new interconnects, without requiring changes to the software or operating environment.