CEO Jack Hidary on SandboxAQ’s Ambitions and Near-term Milestones

October 18, 2022

Spun out from Google last March, SandboxAQ is a fascinating, well-funded start-up targeting the intersection of AI and quantum technology. “As the world enter Read more…

Google’s DeepMind Has a Long-term Goal of Artificial General Intelligence

September 14, 2022

When DeepMind, an Alphabet subsidiary, started off more than a decade ago, solving some most pressing research questions and problems with AI wasn’t at the top of the company’s mind. Instead, the company started off AI research with computer games. Every score and win was a measuring stick of success... Read more…

Google Program to Free Chips Boosts University Semiconductor Design

August 11, 2022

A Google-led program to design and manufacture chips for free is becoming popular among researchers and computer enthusiasts. The search giant's open silicon program is providing the tools for anyone to design chips, which then get manufactured. Google foots the entire bill, from a chip's conception to delivery of the final product in a user's hand. Google's... Read more…

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The Mainstreaming of MLPerf? Nvidia Dominates Training v2.0 but Challengers Are Rising

June 29, 2022

MLCommons’ latest MLPerf Training results (v2.0) issued today are broadly similar to v1.1 released last December. Nvidia still dominates, but less so (no gran Read more…

Google Cloud’s New TPU v4 ML Hub Packs 9 Exaflops of AI

May 16, 2022

Almost exactly a year ago, Google launched its Tensor Processing Unit (TPU) v4 chips at Google I/O 2021, promising twice the performance compared to the TPU v3. At the time, Google CEO Sundar Pichai said that Google’s datacenters would “soon have dozens of TPU v4 Pods, many of which will be... Read more…

APS March Meeting: Google, Intel and Others Highlight Quantum Progress Points

March 17, 2022

At yesterday’s Quantum in Industry session at the APS March Meeting 2022, Google, IBM, Intel, Quantinuum, and Silicon Quantum Computing/University of South Wales (USW) presented progress points and ongoing challenges in the race to achieve practical quantum computing. While IBM has proclaimed 2023 to be the year it achieves quantum advantage, the other... Read more…

Tiny Chips Cause Giant Error Correction Challenges

February 9, 2022

It’s no secret that finding and correcting errors in modern computer chips is an ever-growing problem. An article published this week in the New York Times ( Read more…

With a Carbon Footprint Like HPC’s, It Matters When and Where You Step

December 9, 2021

From European HPC experts pondering “can fast be green?” to new milestones on the Green500 list, sustainability certainly had a moment at the hybrid SC21 conference. And it’s no wonder: the exascale era is here, and power consumption for HPC is skyrocketing even as efficiency is driven to its extremes. At SC21, another session – “HPC’s Growing Sustainability Challenges and Emerging Approaches” – tackled the topic... 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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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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