ayazhassan Goto Github PK

bonusproject

code-samples

Source code examples from the Parallel Forall Blog

cppe-dataset

Code for our paper CPPE - 5 (Medical Personal Protective Equipment), a new challenging object detection dataset

cpu-free-model

https://dl.acm.org/doi/10.1145/3577193.3593713

cs344

Introduction to Parallel Programming class code

databaseproject

datasciencecoursera

datasharing

The Leek group guide to data sharing

gpuocelot

Automatically exported from code.google.com/p/gpuocelot

helloworld-rpc

A VERY simple rpc example to start with.

helloworld_rpc

human-eval

Code for the paper "Evaluating Large Language Models Trained on Code"

hydrazine

Automatically exported from code.google.com/p/hydrazine

image-recognition-tutorial-using-mxnet-with-docker

This is an extension of the tutorial available at https://www.r-bloggers.com/image-recognition-tutorial-in-r-using-deep-convolutional-neural-networks-mxnet-package/ for image recognition example using MXNet. The users can easily build a docker image for the required environment and directly start running the example. There is no need to do installation of linux and other required packages.

individual-work

integrate_python_code_with_simulink

jetbot

An educational AI robot based on NVIDIA Jetson Nano.

jetbot_ros

ROS nodes and Gazebo model for NVIDIA JetBot with Jetson Nano

kdd-2018

module3

GitHub Campus Advisor Module 3

morphologica

A library of supporting code for numerical modelling (JSON config, HDF5 data, Modern OpenGL visualization)

multi-gpu-programming-models

Examples demonstrating available options to program multiple GPUs in a single node or a cluster

nemo

NeMo: a toolkit for conversational AI

padding_free_matrix_transpose_gpu

The advances of Graphic Processing Units (GPU) technology and the introduction of CUDA programming model facilitates developing new solutions for sparse and dense linear algebra solvers. Matrix Transpose is an important linear algebra procedure that has deep impact in various computational science and engineering applications. Several factors hinder the expected performance of large matrix transpose on GPU devices. The degradation in performance involves the memory access pattern such as coalesced access in the global memory and bank conflict in the shared memory of streaming multiprocessors within the GPU. In this paper, two matrix transpose algorithms are proposed to alleviate the aforementioned issues of ensuring coalesced access and conflict free bank access. The proposed algorithms have comparable execution times with the NVIDIA SDK bank conflict - free matrix transpose implementation. The main advantage of proposed algorithms is that they eliminate bank conflicts while allocating shared memory exactly equal to the tile size (T x T) of the problem space. However, to the best of our knowledge an extra space of Tx(T+1) needs to be allocated in the published research. We have also applied the proposed transpose algorithm to recursive gaussian implementation of NVIDIA SDK and achieved about 6% improvement in performance.

parallelprogrammingwithopenmp

Training Material

pareval

programming_examples

Programming Examples taught in class

programming_examples-helloworld-rpc

rna-prediction-using-parallel-lr-parsing-algorithm

rstudio

A repository that will be linked with RStudio