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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…
Python Again Tops IEEE Spectrum’s Programming Language List
July 22, 2020
What’s your go-to programming language? As judged by IEEE Spectrum, it is (again) Python and comfortably so according to an article posted today. C++ finished Read more…
Google’s ML Compiler Initiative Advances
September 12, 2019
Machine learning models running on everything from cloud platforms to mobile phones are posing new challenges for developers faced with growing tool complexity. Read more…
When Dense Matrix Representations Beat Sparse
September 9, 2019
In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance pen Read more…
MIT Aims to Ease AI Programming
July 2, 2019
A new AI programming language seeks to ease the process of writing inference algorithms and other predictive models without the hassle of grinding out complicated equations and code. Among the goals of the probabilistic programming system dubbed “Gen” is making it easier for coding novices to write models and algorithms for broader AI applications such as computer vision and robotics. Read more…
TIOBE Index: Python Reaches Another All-Time High
June 17, 2019
TIOBE has released its June 2019 Index, and Python has reached another all-time high. TIOBE, which stands for “the importance of being earnest,” was founded in 2000. Its Programming Community Index – which is updated on a monthly basis... Read more…
ISC19 Panel: When’s enough, enough? With so many parallel programming technologies, is it time to focus on consolidating them?
June 10, 2019
ISC is looming fast and on the Wednesday we will be holding a panel asking the question whether it is time to focus more on the consolidation and interoperabili Read more…
Top Ten Ways AI Affects HPC in 2019
March 26, 2019
AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization Read more…
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Whitepaper
Streamlining AI Data Management
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.
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Taking research further with extraordinary compute power and efficiency
Karlsruhe Institute of Technology (KIT) is an elite public research university located in Karlsruhe, Germany and is engaged in a broad range of disciplines in natural sciences, engineering, economics, humanities, and social sciences. For institutions like KIT, HPC has become indispensable to cutting-edge research in these areas.
KIT’s HoreKa supercomputer supports hundreds of research initiatives including a project aimed at predicting when the Earth’s ozone layer will be fully healed. With HoreKa, projects like these can process larger amounts of data enabling researchers to deepen their understanding of highly complex natural processes.
Read this case study to learn how KIT implemented their supercomputer powered by Lenovo ThinkSystem servers, featuring Lenovo Neptune™ liquid cooling technology, to attain higher performance while reducing power consumption.
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