Hacker News with Generative AI: Scientific Computing

Scientific computing with confidence using typed dimensions (laurentrdc.xyz)
I have performed non-trivial scientific calculations, in university and beyond, for almost 15 years.
Khronos SYCL Being Updated to Increase Appeal for HPC and Scientific Computing (phoronix.com)
In addition to the release today of OpenMP 6.0 ahead of the SC24 supercomputing conference in Atlanta, over at The Khronos Group they have provided an update on upcoming SYCL improvements to benefit high performance computing (HPC) and scientific computing applications.
Ask HN: Are my HPC professors right? Is Python worthless compared to C? (ycombinator.com)
I'm a PhD student implementing a finite element code. It simulates electromagnet waves passing through heterogeneous material. This code has to run in parallel, and run fast. I've been using old C libraries like PETSc to do this, and honestly, I do not enjoy working with C at all. Its esoteric and difficult to understand, and just overall feels like I'm using a tool from the 70s.
My NumPy year: Creating a DType for the next generation of scientific computing (quansight.com)
From no CPython C API experience to shipping a new DType in NumPy 2.0.
My NumPy year: Creating a DType for the next generation of scientific computing (quansight.com)
From no CPython C API experience to shipping a new DType in NumPy 2.0.
Machine Learning to Computational Plasma Physics Reduced-Order Plasma Modeling (arxiv.org)
Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge.
Building a compile-time SIMD optimized smoothing filter (scientificcomputing.rs)
I built a Savitzky-Golay filter (fancy name for a dot product with some known constants on a rolling window) and tried to optimize the crap out of it.
CuPy: NumPy and SciPy for GPU (github.com/cupy)
CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.
Performance of Eigen vs. Blaze vs. Fastor vs. Armadillo vs. XTensor (2020) (medium.com)
It is March 2020. C++20 is almost around the corner and as a scientific C++ programmer I am quite thrilled with the new features that the language is getting.
Ngscopeclient: Advanced T&M remote control and analysis suite (ngscopeclient.org)
Drag and drop to create complex, GPU-accelerated analysis pipelines in the filter graph editor
Slime mold simulation in Rust using WASM and WebGPU (github.com/plul)
Show HN: Datoviz – Vulkan-based GPU scientific visualization (C/C++/Python) (github.com/datoviz)
Pixi – rust-based package manager for reproducible scientific workflows (prefix.dev)
Neko: Portable framework for high-order spectral element flow simulations (github.com/ExtremeFLOW)
NumPy 2.0 Is Released (numpy.org)
Ndindex: A Python library for manipulating indices of ndarrays (quansight-labs.github.io)
ROOT: analyzing petabytes of data scientifically (root.cern)
LANL Achieves Yottabyte-Scale Data Compression in Neutron Transport Equations (hpcwire.com)
Fortran popularity rises with numerical and scientific computing (infoworld.com)
High performance array programming in Petalisp (zenodo.org)
Warp Factory: numerical toolkit for analyzing warp drive spacetimes (github.com/NerdsWithAttitudes)