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Solving Heterogeneous Programming Challenges with Python, Today
June 30, 2022
You may be surprised how ready Python is for heterogeneous programming, and how easy it is to use today. Our first three articles about heterogeneous programming focused primarily on C++ as we ponder “how to enable programming in the face of an explosion of hardware diversity that is coming?” For a refresher on what motivates this question... Read more…
Why SYCL: Elephants in the SYCL Room
February 3, 2022
Commentary -- In the second of a series of guest posts on heterogeneous computing, James Reinders, who returned to Intel last year after a short “retirement, Read more…
Solving Heterogeneous Programming Challenges with SYCL
December 8, 2021
In the first of a series of guest posts on heterogenous computing, James Reinders, who returned to Intel last year after a short “retirement,” considers how SYCL will contribute to a heterogeneous future for C++. Reinders digs into SYCL from multiple angles... Read more…
15 Slides on Programming Aurora and Exascale Systems
May 7, 2020
Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…
European LEGaTO Project Seeks to Develop Energy Efficient Stack
March 14, 2018
A new European project – Low Energy Toolset for Heterogeneous Computing (LEGaTO) – seeks to develop a software stack that improves energy management in supp Read more…
Dealing with HPC Correctness: Challenges and Opportunities
January 25, 2018
Developing correct and reliable HPC software is notoriously difficult. While effective correctness techniques for serial codes (e.g., verification, debugging an Read more…
KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017
December 15, 2016
Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a deca Read more…
AMD Reveals ‘Instinct’ for Machine Intelligence
December 13, 2016
At the AMD Tech Summit in Sonoma, Calif., last week (Dec. 7-9), CEO Lisa Su unveiled the company's vision to accelerate machine intelligence over the next fiv Read more…
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Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
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Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
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