CUDA Core Compute Libraries

Finite Field Operations on GPGPU

FastRerandomize: Rerandomization Using Accelerated Computing

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

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

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

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

AMD GPU Profiler for ROCm compatible GPU.

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.

Paperspace CORE API Documentation

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

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.

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.

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.

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

Advance Statistical Computing, 2019, Seoul National University

Repository of the lab7 assignment for the Parallel Programming course.