Topic: GPU computing

CERN is recruiting a detector-simulation physicist to develop GPU-accelerated Monte Carlo software in the Geant4 team, with a focus on optical-photon transport. The work includes GPU algorithm design, validation against established CPU simulations, performance profiling and optimisation, and integration with experimental software frameworks. The 24-month hybrid appointment is based in Geneva with an ideal start date of 1 January 2027. Applications close on 11 October 2026 at 23:59 Geneva time.

Fireworks AI co-founder Benny Chen explains the infrastructure required to serve machine-learning models at scale, from GPU procurement and kernels to runtimes and request routing. He discusses power constraints, hardware utilization, mixture-of-experts models, quantization, reinforcement-learning efficiency and speculative decoding. The conversation also covers distributed training across data centres, sparse autoencoders, the constraints on open-source interpretability research, and the relationship between open models and compute availability. Hosted by Ravid Shwartz Ziv and Allen Roush; th

Katarzyna Swirydowicz uses an asynchronous solver on an AMD system to examine the practical implementation of computational linear algebra across CPUs and GPUs. The talk introduces the relevant computational ideas, programming models, and software tools, then considers how algorithmic structure interacts with hardware capabilities. This case study illustrates both the opportunities and implementation challenges of asynchronous methods for large-scale scientific computing on GPU-accelerated systems.