Across industries, companies are beginning to watch the convergence of High-performance Computing (HPC) and Big Data. Many organizations in the Financial Services Industry (FSI) are running their financial simulations on business analytics systems, some on HPC clusters. But they have a growing problem: integrating analytics of non-structured data from sources like social media with their internal data. Learn how Lustre can help solves these challenges.
“Modern Numerical Weather prediction (NWP) can now use many thousands of cores in a single run of the application. By using modern CPUs such as the Intel Xeon processors and the Intel Xeon Phi coprocessors, tremendous performance and efficiency can be obtained. It is important to remember that many of the applications are written in Fortran and many of the contributors are domain experts, not parallel programming gurus.”
Training the neural networks used in deep learning is an ideal task for GPUs because GPUs can perform many calculations at once (parallel calculations), meaning the training will take much less time than it used to take. More GPUs means more computational power so if a system has multiple GPUs, it can compute data much faster than a system with CPUs only, or a system with a CPU and a single GPU. One Stop System’s High Density Compute Accelerator is the densest GPU expansion system to date.
The Morton order is a mapping of multidimensional data to one dimension that preserves locality of the data. This is also known as Z-order. “By using Morton ordering as an alternative to row-major or column-major data storage, significant speedups can be achieved on the Intel Xeon Phi coprocessor or Intel Xeon CPU when performing matrix multiplies or matrix transposes.”
With the explosion of data over the past few years, data storage has become a hot topic among corporate decision makers. It is no longer sufficient to have adequate space for the massive quantities of data that must be stored; it is just as critical that stored data be accessible without any bottlenecks that impede the ability to process and analyze data in real time.
“A parallel implementation of SpMV can be implemented, using OpenMP directives. However, by allocating memory for each core, data races can be eliminated and data locality can be exploited, leading to higher performance. Besides running on the main CPU, vectorization can be implemented on the Intel Xeon Phi coprocessor. By blocking the data in various chunks, various implementations on the Intel Xeon Phi coprocessor can be run and evaluated.”
One of the most used algorithms in numerical simulation is the solving of large, dense matrices. Thermal analysis, boundary element methods and electromagnetic wave calculations all depend on the ability to solve these large matrices as fast as possible. The ability to use a coprocessor such as the Intel Xeon Phi coprocessor will greatly speed up these calculations.
For companies looking to test the viability of engineering in the cloud, Altair has teamed with Intel and Amazon Web Services (AWS) to offer an “HPC Challenge” for product design. In a nutshell, the program provides free cycles on AWS for up to 60 days, where users can run compute-intensive jobs for computer-aided engineering (CAE).