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.”
Next-generation sequencing (NGS) tools produce vast quantities of genetic data which poses a growing number of challenges to life sciences organizations. Accelerating analytics, providing adequate storage and memory capacity, speeding time-to-solution, and reducing costs are major concerns for IT department operating on traditional computing systems. In this week’s Sponsored Post, Bill Mannel, Vice President & General Manager of HPC Segment Solutions and Apollo Servers, Data Center Infrastructure Group at Hewlett Packard Enterprise, explains how next-generation sequencing is altering the patient care landscape.
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.
“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.
In this week’s Sponsored Post, Katie Garrison, of One Stop Systems explains how GPUs and Flash solutions are used in radar simulation and anti-submarine warfare applications. “High-performance compute and flash solutions are not just used in the lab anymore. Government agencies, particularly the military, are using GPUs and flash for complex applications such as radar simulation, anti-submarine warfare and other areas of defense that require intensive parallel processing and large amounts of data recording.”
“As with all new technology, developers will have to create processes in order to modernize applications to take advantage of any new feature. Rather than randomly trying to improve the performance of an application, it is wise to be very familiar with the application and use available tools to understand bottlenecks and look for areas of improvement.”
High-performance computing (HPC) tools are helping financial firms survive and thrive in this highly demanding and data-intensive industry. As financial models grow in complexity and greater amounts of data must be processed and analyzed on a daily basis, firms are increasingly turning to HPC solutions to exploit the latest technology performance improvements. Suresh Aswani, Senior Manager, Solutions Marketing, at Hewlett Packard Enterprise, shares how to overcome the learning curve of new processor architectures.
“By implementing popular Python packages such as NumPy, SciPy, scikit-learn, to call the Intel Math Kernel Library (Intel MKL) and the Intel Data Analytics Acceleration Library (Intel DAAL), Python applications are automatically optimized to take advantage of the latest architectures. These libraries have also been optimized for multithreading through calls to the Intel Threading Building Blocks (Intel TBB) library. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.”