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Performance in the Datacenter

Many modern applications are being developed with so called run-time languages, which are compiled at execution time. The performance of these applications in cloud data centers is important for anyone considering moving their applications and workloads to the cloud. Download Intel Distribution for Python for free today to supercharge your applications.

Streamline Your HPC Setup with Intel Cluster Checker

“Understanding a cluster can be complex if tools are not available such as Intel Cluster Checker. Think of how many times users complain that their applications are not runing with the expected performance and how long it takes system administrators to diagnose the issue. With Intel Cluster Checker, diagnosing and debugging of these issues is easier and less complex. By usingthis tool, customers will be more statisfied and a higher return on the investment will be realized.”

Materials Science Modeling with VASP

In today’s world where science and engineering depend on the simulation of new materials and their behavior is of critical importance. New materials are constantly being designed and brought into product design in order to create products that can withstand many environmental conditions and still perform for their intended use. HPC is critical for the simulation of these materials and applications which perform at the fastest speed available on a given hardware platform can lead to earlier introduction of products that contain these materials.

High Performance Big Data Computing Using Harp-DAAL

Harp-DAAL is a framework developed at Indiana University that brings together the capabilities of big data (Hadoop) and techniques that have previously been adopted for high performance computing.  Together, employees can become more productive and gain deeper insights to massive amounts of data.

Python Can Do It

“Python remains a single threaded environment with the global interpreter lock as the main bottleneck. Threads must wait for other threads to complete before starting to do their assigned work. The result of this model is that production code is produced that is too slow to be useful for large simulations.”

NVIDIA Makes Visualization Easier in the Cloud

Visualizing the results of a simulation can give new insight into complex scientific problems. Interactive viewing of entire datasets can lead to earlier understanding of the challenge at hand and can enhance the understanding of complex phenomena. With the release of the of HPC Visualization Containers with the NVIDIA CPU Cloud, it has become much easier to get a visualization system up and production ready much quicker than ever before.

FPGA Programming Made Easy

In the past, it was necessary to understand a complex programming language such as Verilog or VHDL, that was designed for a specific FPGA. “Using a familiar language such as OpenCL, developers can become more productive, sooner when deciding to use an FPGA for a specific purpose. OpenCL is portable and is designed to be used with almost any type of accelerator.”

Performance Insights Using the Intel Advisor Python API

Tuning a complex application for today’s heterogeneous platforms requires an understanding of the application itself as well as familiarity with tools that are available for assisting with analyzing where in the code itself to look for bottlenecks.  The process for optimizing the performance of an application, in general, requires the following steps that are most likely applicable for a wide range of applications.

Flow Graph Analyzer – Speed Up Your Applications

Using the Intel® Advisor Flow Graph Analyzer (FGA), an application such as those that are needed for autonomous driving can be developed and implemented using very high performing underlying software and hardware. Under the Intel FGA, are the Intel Threaded Building Blocks which take advantage of the multiple cores that are available on all types of systems today.

Outliers – Why So Important for Data Analytics?

Data analytics deals with making observations with various data sets, and trying to make sense of the data. When dealing with very large data sets, automated tools must be used to find patterns and relationships. One of the most important tasks from large data sets is to find an outlier, which is defined as a sample or event that is very inconsistent with the rest of the data set.