A convergence in the fields of High Performance Computing (HPC) and Big Data has led to new opportunities for software developers to create and deliver products that can help to analyze very large amounts of data. The HPC software ecosystem over the years have created and maintained sets of numerical libraries, communication API’s (MPI) and applications to make running HPC type applications faster and simpler to design. Low level libraries have been developed so that developers can concentrate on higher level algorithms. Products such as the Intel Math Kernel Library (Intel MKL) have been highly tuned to take advantage of multiple cores and newer instructions sets.
Today Intel released Intel Parallel Studio XE 2016, the next iteration of its developer toolkit for HPC and technical computing applications. This release introduces the Intel Data Analytics Acceleration Library, a library for big data developers that turns large data clusters into meaningful information with advanced analytics algorithms.
In this special guest feature, Robert Roe from Scientific Computing World explores the efforts made by top HPC centers to scale software codes to the extreme levels necessary for exascale computing. “The speed with which supercomputers process useful applications is more important than rankings on the TOP500, experts told the ISC High Performance Conference in Frankfurt last month.”
“Software and computers are everywhere, revolutionizing every field around us. But the majority of schools don’t teach computer science. Code.org believes every student should have the opportunity to shape the 21st-century and wants to turn this problem around. This is just the beginning of a bold vision to bring this foundational field to every K-12 public school by 2020.”
“The fast fourier transform (FFT) algorithm is a powerful tool for looking at time-based measurements in an interesting way, but do you understand what it does? This talk will start from basic geometry and explain what the fourier transform is, how to understand it, why it’s useful and show examples. If you’re collecting time-series data (e.g. heart rate, stock prices, server usage, temperature) the fourier transform can be a useful tool for analyzing the underlying periodic nature of the data.”