Messaggi di Rogue Scholar

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Pubblicato in JSC Accelerating Devices Lab

In November of 2022, I created a table comparing GPU programming models and their support on GPUs of the three vendors (AMD, Intel, NVIDIA) for a talk. The audience liked it, so I beefed it up a little and posted it in this very blog.

Pubblicato in JSC Accelerating Devices Lab

The Supercomputing Conference 2023 took place in Denver, Colorado, from November 12th to 17th. For the Women in HPC workshop, we submitted a paper, which focused on benchmarking different accelerators for AI. The paper was accepted and I was invited to hold a lightning talk to show the work, spun off our OpenGPT-X project.

Pubblicato in JSC Accelerating Devices Lab

Environment Setup Enabling UCC in OpenMPI Enabling NCCL in UCC (Team Layer Selection) All The Variables Results 1. Plain OpenMPI 2. OpenMPI with UCC 3. OpenMPI with UCC+NCCL Scaling Plots Average Latency Bus Bandwidth Comparing MPI, UCC, UCC+NCCL Comparing UCC+NCCL, NCCL Summary Technical Details This post

Pubblicato in JSC Accelerating Devices Lab

** Poster publication:** http://hdl.handle.net/2128/34532 The ISC High Performance Conference 2023 was held at Hamburg, Germany from 21st May to 25th May. At the conference, we presented a project poster on the OpenGPT-X project, outlining the progress and initial exploration results. The poster was even featured in HPCWire’s May 24 recap of ISC within the AI segment!

Pubblicato in JSC Accelerating Devices Lab

For a recent talk at DKRZ in the scope of the natESM project, I created a table summarizing the current state of using a certain programming model on a GPU of a certain vendor, for C++ and Fortran. Since it lead to quite a discussion in the session, I made a standalone version of it with some updates and elaborations here and there.

Pubblicato in Henry Rzepa's Blog

If you get a small rotatable molecule below, then ChemDoodle/HTML5/WebGL is working. Why might this be important? Well, the future is mobile, in other words, devices that rely on batteries or other sources of built-in power. This means the power guzzling GPU cards of the past (some reach ~400 Watts!) cannot be used.