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.

Field programmable gate arrays (FPGAs) not only are capable of high performance, they’re wonderfully malleable—configurable to match the application rather than the other way around. Because of this freedom, FPGA app developers have come to rely on tools and languages (specifically HDLs) used by hardware designers, rather than software programmers. But that’s changing. Find out the Intel® FPGA SDK for OpenCL™ technology provides an alternative to HDL programming.

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.

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.

For brick-and-mortar retailers, the new paradigm is “digitize”—i.e., use new IoT technologies to understand and respond to real-world customer behavior. Find out how Advantech UShop* and Intel® Vision Products are using AI to keep foot traffic—and commerce—alive and kicking.

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.