“Just as developers need tools to understand the performance of a CPU intensive application in order to modify the code for higher performance, so do those that develop interactive 3D computer graphics applications. An excellent tool for t this purpose is the Intel Graphics Performance Analyzer set. This tool, which is free to download, can help the developer understand at a very low level how the application is performing, from a number of aspects.”
Articles and news on parallel programming and code modernization
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.
In previous articles (1 and 2) here on insideHPC, James Reinders described “Intel Xeon Phi processor Programming in a Nutshell” for Intel’s 72-core processor. In this special guest feature, he discusses cluster modes and the interaction of the memory modes with these cluster modes.
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.
“In order for developers to be able to focus on their application, a Vision Algorithm Designer application is included in the Intel Computer Vision SDK. This gives users a drag and drop interface that allows them to create new applications on the fly. Large and complex workflows can be modelled visually which takes the guesswork out of bringing together many different functions. In addition, customized code can be added to the workflows.”
James Reinders discusses one of the “mode” options that Intel Xeon Phi processors have to offer: memory modes. “For programmers, this is the key option to really study because it may inspire programming changes.”
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.
This year, the OpenMP Architecture Review Board is celebrating the 20th anniversary of the first OpenMP API specification with a pair of events: OpenMPCon and the International Conference on OpenMP (IWOMP). Both events will take place the week of September 18 at Stony Brook University in New York. “Developers attending this year’s OpenMPCon and IWOMP conferences will have the added bonus of joining us to celebrate the vital contribution OpenMP has made by enabling high-performance computing over the past two decades and will also help us to shape OpenMP’s next twenty years.” said Michael Klemm, OpenMP CEO.”
“In the past, developers would get best results if a loop was unrolled, that is, duplicating the body as many times as needed to that the operations could be operated on using full vectors. The number of iterations would reflect the hardware that the code was targeted towards. Since the application may have to run on different hardware in the future, results for todays generation of hardware may be compromised in the future. In fact, it is better to let modern compilers to the unrolling.”
Intel DAAL is a high-performance library specifically optimized for big data analysis on the latest Intel platforms, including Intel Xeon®, and Intel Xeon Phi™. It provides the algorithmic building blocks for all stages in data analysis in offline, batch, streaming, and distributed processing environments. It was designed for efficient use over all the popular data platforms and APIs in use today, including MPI, Hadoop, Spark, R, MATLAB, Python, C++, and Java.