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Go with Intel® Data Analytics Acceleration Library and Go*

Use of the Go* programming language and it’s developer community has grown significantly since it’s official launch by Google in 2009. Like many popular programming languages (C and Java come to mind), Go started as an experiment to design a new programming language that would fix some of the common problems of other languages and yet stay true to the basic tenets of modern programming: be scalable, productive, readable, enable robust development environments, and support networking and multiprocessing.

Petabyte-Scale Active Archive in Private Object Storage

In big data science, storage archives protect massive volumes of research-critical content. Scientists at the University of Warsaw (UW) Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) rely on a petabyte-scale active archive built on modern storage technology.

Cycles Per Instruction – Why it matters

To compare how one version of a part of the code is running to another version, since this is a ratio, it is important to keep one of the values constant in order to understand if the optimization is working. If more cpu cycles are being used, but more instructions are being executed, then the ratio could be the same, but this measure will not show any improvement. The goal is to lower the CPI in certain parts of the code as well as the overall application.

New Intel Xeon Scalable Processors Accelerate HPC Systems

Intel outlines the highlights and features of the company’s new Intel Xeon Scalable processors designed to accelerate HPC systems. The Intel Xeon processor Scalable Family, the newest Intel Xeon processors, are optimized to address today’s most demanding high-performance computing challenges.

Performance Gains Using Libraries

In many cases, applications that perform various simulations use some of the same math functions that many other applications use. Rather than each developer recoding the same math functions over and over, libraries, developed by experts can significantly speed up execution of the overall application. Since there can be many optimizations that experts who understand many of the nuances of the hardware would understand, it is important that developers be familiar with various libraries that are made available for HPC types of applications.

Intel Omni-Path Architecture: Incredible Momentum for a New Technology

This is the last of four articles on Intel Omni-Path Architecture (Intel OPA) next-generation fabric for high-performance computing (HPC) solutions. Intel OPA is in clusters used for traditional supercomputing, artificial intelligence (AI), HPC cloud, and in the enterprise.

Deep Learning Frameworks Get a Performance Benefit from Intel MKL Matrix-Matrix Multiplication

Intel® Math Kernel Library 2017 (Intel® MKL 2017) includes new GEMM kernels that are optimized for various skewed matrix sizes. The new kernels take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and achieves high GEMM performance on multicore and many-core Intel® architectures, particularly for situations arising from deep neural networks..

Intel® HPC Orchestrator and OpenHPC: Trends and Directions

Sharing a common architecture, Intel® HPC Orchestrator and OpenHPC are changing the face of HPC by providing a cohesive and comprehensive system software stack. Dr. Robert Wisniewski, Chief Software Architect Extreme Scale Computing at Intel Corporation, discusses the advantages of this approach and how to leverage it to bring together HPC and the cloud.

Vectorization with AVX-512 Intrinsics

“With the Intel compilers, intrinsics are recognized and the instructions are generated in-line which is a tremendous advantage. Since the Intel Xeon Phi processor when using the AVX-512 intrinsics can perform a tremendous number of floating point operations per second, it is beneficial to use intrinsics for certain math computations. To use intrinsics, all that is needed is the proper header file and then to call the desired intrinsic function.”

Analytics for Massive Datasets Using High Density GPU Accelerators

Cloud computing providers are now allowing users to rent GPU power in the cloud for big data analytics instead of building or buying their own hardware. In this week’s Sponsored Post, Katie Rivera, of One Stop Systems, explains the technology behind analytics for massive datasets using high density GPU accelerators.