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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.

Enabling FPGAs

Field Programmable Gate Arrays (FPGAs) are an exciting technology that allows hardware designers to create new digital circuits through a programming environment. Compared to hardware that is designed once or software which must adhere to the hardware architecture, an FPGA allows developers to draw a circuit to solve a specific problem.

Intel Parallel Studio 2018: Modernize Your Code

“Intel Parallel Studio 2018 has been designed to recognize the latest CPU architectures including the Intel Xeon Scalable processor family and the Intel Xeon Phi processors in order to get maximum performance from their differing architectures, yet remain binary compatible. With the recent introduction of the Intel  AVX-512 vectorization instructions, application developers can more easily take advantage of these new instructions when developing and compiling with the Intel Parallel Studio 2018.”

Intel Parallel Studio XE AVX-512: Tuning for Success with the Latest SIMD Extensions and Intel® Advanced Vector Extensions 512

With the introduction of Intel Parallel Studio XE, instructions for utilizing the vector extensions have been enhanced and new instructions have been added. Applications in diverse domains such as data compression and decompression, scientific simulations and cryptography can take advantage of these new and enhanced instructions. “Although microkernels can demonstrate the effectiveness of the new SIMD instructions, understanding why the new instructions benefit the code can then lead to even greater performance.”