CUDA Core Compute Libraries

Pythonic binding to the Apple Neural Engine

Finite Field Operations on GPGPU

My solutions for NVIDIA course Fundamentals of Accelerated Computing with CUDA C/C++

[SoftwareX 2026] FastRerandomize: Rerandomization Using Accelerated Computing

Accelerated and Distributed Monte Carlo Tally on Unstructured Meshes

Fundamentals of Accelerated Computing C/C++ is a course provided by NVIDIA.

Generate Cloud Optimized GeoTIFFs accelerated on Apple Silicon (MLX)

AMD GPU Profiler for ROCm compatible GPU.

Written by Sem Kirkels, Nathan Bruggeman and Axel Vanherle. Grayscales an image, applies convolution, maximum pooling and minimum pooling.

Integrate Hermes Agent skills directly into Claude Code for native agentic operations without external processes or locking.

this is the results of 2 years of development of the first logic simulator that is based on HW instead of SW. Project by Max Nigri

Paperspace CORE API Documentation

The project aims to optimize the Dynamic Time Warping (DTW) algorithm and accelerate it using Graphics Processing Units (GPUs), So that algorithm can be executed in a GPU-equipped laptop or a GPU-equipped embedded device like NVIDIA Jetson, rather than connecting to a massive server.

Parallelism standards for accelerating performance on calculations for detection of positive DNA selection

This repository documents realities that surface only after scale,authority,and commitment eliminate reversibility—where decisions ossify into structure, costs compound beyond instrumentation, failures persist without incident,accountability diffuses,and systems continue operating long after meaningful correction has become structurally impossible.

This repository contains an advanced tutorial on optimizing Python code for machine learning applications, focusing on processing large amounts of data efficiently. It covers three powerful libraries: Numba, NumPy, and Polars.

How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.

Talks and Presentations on Deep Learning principles,models and architectures

Fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler.