The UC Berkeley crew has now shown the value of AI-based optimization work by having OpenEvolve work out a more efficient approach to load balancing across GPUs handling LLM inference.
Abstract: Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse ...
The neutral atom array architecture for quantum computing has been rapidly advancing over the last several years, and a recent study published in Nature has just revealed another step forward for this ...
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Abstract: Distributed computations, such as distributed matrix multiplication, can be vulnerable to significant security issues, notably Byzantine attacks. These attacks may target either worker nodes ...
When you watch “The Matrix” at Cosm, you’re essentially seeing a film within a film. A shot inside an apartment becomes a glimpse into an entire complex. A fight scene on a rooftop is now one small ...
Google DeepMind’s AI systems have taken big scientific strides in recent years — from predicting the 3D structures of almost every known protein in the universe to forecasting weather more accurately ...
Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback