CUDA Core Compute Libraries

Finite Field Operations on GPGPU

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

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

Based on Baidu's Edge Board, Accelerate HRNet model inference using NPU.

Paperspace CORE API Documentation

Based on Baidu's Edge Board, Accelerate Yolov3 model inference using NPU.

Based on Baidu's Edge Board, Accelerate Resnet model inference using NPU.

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.

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.

Advance Statistical Computing, 2019, Seoul National University

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.