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

Porting CUDA Applications to Run on AMD GPUs

Giving developers the ability to write code once and use it on different platforms is important. Organizations are increasingly moving to open source and open standard solutions which can aid in code portability. AMD developed a porting solution that allows developers to port proprietary NVIDIA® CUDA® code to run on AMD graphic processing units (GPUs).

This paper describes the AMD ROCm™ open software platform which provides porting tools to convert NVIDIA CUDA code to AMD native open-source Heterogeneous Computing Interface for Portability (HIP) that can run on AMD Instinct™ accelerator hardware. The AMD solution addresses performance and portability needs of artificial intelligence (AI), machine learning (ML) and high performance computing (HPC) for application developers. Using the AMD ROCm platform, developers can port their GPU applications to run on AMD Instinct accelerators with very minimal changes to be able to run their code in both NVIDIA and AMD environments.

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Sponsored by AMD

Whitepaper

QCT HPC BeeGFS Storage: A Performance Environment for I/O Intensive Workloads

A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.

This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.

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Sponsored by QCT

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