ECP Update: Satisfying DOE and Building SDKs for Heterogeneous Exascale Architecture

Delivering an exascale-ready software infrastructure is the Exascale Computing Project’s mission, a goal towards which it has steadily marched although ECP doesn’t grab headlines the way pre-

By John Russell

Codeplay Open Sources a Version of DPC++ for Nvidia GPUs

Writing software to run efficiently on today’s heterogeneous compute architectures is an ongoing challenge made increasingly difficult by the growing number of processor and accelerator choices

By John Russell

Intel Stopping Nervana Development to Focus on Habana AI Chips

Just two months after acquiring Israeli AI chip start-up Habana Labs for $2 billion, Intel is stopping development of its existing Nervana neural network processor line. Instead, Intel will focus

By John Russell

MLPerf Ascends as DAWNBench is Phased Out

As the MLPerf benchmark emerges as an industry standard for measuring the performance of machine learning models, its creators said they will phase out the foundational DAWNBench metric. Stanford

By George Leopold

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Julia Programming’s Dramatic Rise in HPC and Elsewhere

January 14, 2020

Back in 2012 a paper by four computer scientists including Alan Edelman of MIT introduced Julia, A Fast Dynamic Language for Technical Computing. At the time, t Read more…

By John Russell

New Tool from MIT Uses Machine Learning to Predict Code Performance on a Chip

January 13, 2020

Typically, code’s performance on a given computer chip is estimated using performance models that test the code on a variety of architectures, after which com Read more…

By Oliver Peckham

Study Examines Efforts (and Prospects) for ML Use in Computer Architecture Design

January 9, 2020

The rush to adopt machine learning (ML) in broad applications hasn’t yet been matched by efforts to use ML to inform computer architecture design. That’s no Read more…

By John Russell

Blasts from the (Recent) Past and Hopes for the Future

December 23, 2019

What does 2020 look like to you? What did 2019 look like? Lots happened but the main trends were carryovers from 2018 – AI messaging again blanketed everything; the roll-out of new big machines and exascale announcements continued; processor diversity and system disaggregation kicked up a notch; hyperscalers continued flexing their muscles (think AWS and its Graviton2 processor); and the U.S. and China continued their awkward trade war. Read more…

By John Russell

CloudyCluster Helps Students Build Bright Futures in HPC

December 20, 2019

Looking for a future in HPC, some students literally taught themselves Python in a day while competing in the Cloud HPC Hackathon (also known as the Omnibond C Read more…

By Rob Farber

ARPA-E Applies ML to Power Generation Designs

December 19, 2019

The U.S. Energy Department’s research arm is leveraging machine learning technologies to simplify the design process for energy systems ranging from photovolt Read more…

By George Leopold

Jensen Huang’s SC19 – Fast Cars, a Strong Arm, and Aiming for the Cloud(s)

November 20, 2019

We’ve come to expect Nvidia CEO Jensen Huang’s annual SC keynote to contain stunning graphics and lively bravado (with plenty of examples) in support of GPU Read more…

By John Russell

IBM Adds Support for Ion Trap Quantum Technology to Qiskit

November 11, 2019

After years of percolating in the shadow of quantum computing research based on superconducting semiconductors – think IBM, Rigetti, Google, and D-Wave (quant Read more…

By John Russell

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