Chinese CSP JD found it could achieve 3.83x increased performance using its existing servers based on the Intel® Xeon® processor E5 family. Using BigDL to deploy its existing Caffe* model on servers where the data is stored, and optimizations using the Intel® Math Kernel Library, the company established an agile platform that can now be used to create new DL and AI applications, and reduced its TCO by reusing existing hardware estate for image analysis.

Rendering interactive, photo-realistic, “wow factor” visuals is no longer solely the domain of the GPU. Software-Defined Visualization (SDVis) solutions—all targeted for the CPU—are redefining performance, from cinematic endeavors to scientific analysis. Find out how the open-source, high-performance rendering libraries in Intel® Rendering Framework are leveling the playing field on Intel® Xeon® processors … and how you can get your hands on the framework's libraries.

Explore the HiFUN Solver* real fluid dynamics application as it takes advantage of massively parallel supercomputing platform architecture … and how you can, too.

Learn how to generate a concise summary of application performance using Intel® VTune™ Amplifier's Application Performance Snapshot, and see an interesting new preview feature in Intel® MPI Library.

For over a decade, Python* has topped the short list of high-productivity languages and is one of the most popular abstractions to scientific computing and machine learning … even though the base Python language remains single-threaded. In the age of parallelism, how does this language remain performant? Find out.