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Making Python Fly: Accelerate Performance Without Recoding

Developers are increasingly besieged by the big data deluge. Intel Distribution for Python uses tried-and-true libraries like the Intel Math Kernel Library (Intel MKL)and the Intel Data Analytics Acceleration Library to make Python code scream right out of the box – no recoding required. Intel highlights some of the benefits dev teams can expect in this sponsored post.

CPU, GPU, FPGA, or DSP: Heterogeneous Computing Multiplies the Processing Power

Whether your code will run on industry-standard PCs or is embedded in devices for specific uses, chances are there’s more than one processor that you can utilize. Graphics processors, DSPs and other hardware accelerators often sit idle while CPUs crank away at code better served elsewhere. This sponsored post from Intel highlights the potential of Intel SDK for OpenCL Applications, which can ramp up processing power.

Achieving the Best QoE: Performance Libraries Accelerate Code Execution

The increasing consumerization of IT means that even staid business applications like accounting need to have the performance and ease of use of popular consumer apps. Fortunately, developers now have access to a powerful group of libraries that can instantly increase application performance – with little or no rewriting of older code. Here’s a quick rundown of Intel-provided libraries and how to get them.

Intel High-Performance Python Extends to Machine Learning and Data Analytics

One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel® Distribution for Python, included in Intel® Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics.

Making Computer Vision Real Today – For Any Application

With the demand for intelligent vision solutions increasing everywhere from edge to cloud, enterprises of every type are demanding visually-enabled – and intelligent – applications. Up till now, most intelligent computer vision applications have required a wealth of machine learning, deep learning, and data science knowledge to enable simple object recognition, much less facial recognition or collision avoidance. That’s changed with the introduction of Intel’s Distribution of OpenVINO toolkit.

Python Power: Intel SDK Accelerates Python Development and Execution

It was with one goal – accelerating Python execution performance – that lead to the creation of Intel Distribution for Python, a set of tools designed to provide Python application performance right out of the box, usually with no code changes required. This sponsored post from Intel highlights how Intel SDK can enhance Python development and execution, as Python continues to grow in popularity.

Putting Computer Vision to Work with OpenVINO

OpenVINO is a single toolkit, optimized for Intel hardware, that the data scientist and AI software developer can use for quickly developing high-performance applications that employ neural network inference and deep learning to emulate human vision over various platforms. “This toolkit supports heterogeneous execution across CPUs and computer vision accelerators including GPUs, Intel® Movidius™ hardware, and FPGAs.”

Are Platform Configuration Problems Degrading Your Application’s Performance?

The Intel VTune™ Amplifier Platform Profiler on Windows* and Linux* systems shows you critical data about the running platform that help identify common system configuration errors that may be causing performance issues and bottlenecks. Fixing these issues, or modifying the application to work around them, can greatly improve overall performance.

Accelerated Python for Data Science

The Intel Distribution for Python takes advantage of the Intel® Advanced Vector Extensions (Intel® AVX) and multiple cores in the latest Intel architectures. By utilizing the highly optimized Intel MKL BLAS and LAPACK routines, key functions run up to 200 times faster on servers and 10 times faster on desktop systems. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.

Latest Intel Tools Make Code Modernization Possible

Code modernization means ensuring that an application makes full use of the performance potential of the underlying processors. And that means implementing vectorization, threading, memory caching, and fast algorithms wherever possible. But where do you begin? How do you take your complex, industrial-strength application code to the next performance level?