Cython Programming Tutorials & Engineering Articles

Slack's build pipeline team reduced build times for Quip and Slack Canvas from 60 minutes to as little as 10 minutes by applying classic software engineering principles—separation of concerns, cach...

The article discusses the integration of machine learning interatomic potentials (MLIPs) into molecular dynamics (MD) simulations using the ML-IAP-Kokkos interface within the LAMMPS MD package.

Meta and Quansight have made significant improvements to the Python ecosystem by enhancing type checking and introducing free-threaded Python, which allows for better performance and developer prod...

The article discusses the release of the NVIDIA NeMo Canary model, a state-of-the-art multilingual model for speech recognition and translation.

The article discusses NVIDIA NeMo's latest addition, Parakeet-TDT, a model designed to enhance automatic speech recognition (ASR) accuracy and speed.

The article discusses the NVIDIA NeMo Parakeet family of automatic speech recognition (ASR) models, highlighting their state-of-the-art accuracy and versatility in transcribing spoken English.

This article discusses the implementation of real-time multi-camera pipelines using the NVIDIA Jetson platform, specifically leveraging DeepStream 5. 1.

NVIDIA has released the CUDA 11.

The article discusses how NVIDIA NeMo and DefinedCrowd collaborate to enhance the development of conversational AI models.

CUDA 11. 3 introduces significant enhancements to the CUDA programming model, including improvements to CUDA Graphs, stream-ordered memory allocator, and language support for C++ and Python.

NVIDIA has announced the release of the HPC SDK version 21. 3, which is now available for free download.

The article discusses NVIDIA's efforts to unify the CUDA Python ecosystem, enhancing the developer experience by providing standardized low-level interfaces for accessing CUDA APIs from Python.

NVIDIA has announced the release of CUDA 11. 3, which includes enhancements to the programming model and performance for GPU-accelerated applications.

The article discusses cuSignal, a library designed to accelerate signal processing using GPU technology.

The article discusses how to accelerate Python code on GPUs using the nvc++ compiler and Cython.

The article discusses the release of a high-performance Python library for robotic simulation using the MuJoCo engine, highlighting its capabilities and performance improvements.

The article introduces Numba, a Python compiler that enables high-performance computing by compiling Python code for execution on CUDA-capable GPUs and multicore CPUs.