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algorithms_and_complexity_sorting

This Repository contains the Algorithm homeworks for ANNU

aliabusaleh

androidtutorialforbeginners

Step by step to build Android apps using Android Studio

awesome-profile-readme-templates

A collection of awesome readme templates to display on your profile

base36

PostgreSQL base36 data type input/output support, internally a bigint.

bdma-advanced-db

bdma-data-warehouse-tpcdi

bdma-dbsa-url-postgres

bdma-upc-sdm-lab

bdma-upc-sdm-lab2

blockchain-demo

A web-based demonstration of blockchain concepts.

business-assignment-4

cashe_optimization

this is the third Homework for Architecture 2 Course

cs-mgma-map-reduce

cs-va-london-boroughs

CI Milestone 2 Project (D3.js, Dc.js, crossfilter.js, scss)

csharpdemo

dataset

dbma-data-warehouse-tpc-ds

dll-producer-consumer-demo

ethereumcasts

Companion repo to an Ethereum/Solidity course on Udemy

etl-pipeline

Building an ETL pipeline with dbt, Snowflake, Airflow

final-android-resizer

A simple yet powerful resizer for Android Image resources

git-localconfig-microservice-demo-repo

hashlips_art_engine

HashLips Art Engine is a tool used to create multiple different instances of artworks based on provided layers.

hashlips_nft_contract

A simple NFT smart contract that works with the rest of the HashLips ecosystem.

hashlips_nft_minting_dapp

HashLips minting dapp is a quick and easy way to connect your smart contract and start minting NFTs.

inventoryproject

kubernetes_bynana

leetcodesolutions

In this Repository, I'll be adding the solutions for the problems I'm solving on LeetCode

matrix_maltiplication_using_vectorization

1 Introduction The purpose of this assignment is to give some experience in using SIMD instructions on x86 and getting compiler auto-vectorization to work. We will use matrix- vector and matrix-matrix multiplication to illustrate how SIMD can be used for numerical algorithms. You will be using GCC in this experiment. GCC supports two sets of intrinsics, or built- ins, for SIMD. One is native to GCC and the other one is defined by Intel for their C++ compiler. We will use the intrinsics defined by Intel since these are much better documented. Both Intel1 and AMD2 provide excellent optimization manuals that discuss the use of SIMD instructions and software optimizations. These are good sources for information if you are serious about optimizing your software, but they are not mandatory reading for this assignment. You will, however, find them, and the instruction set references, useful as reference literature when using SSE. Another useful reference is the Intel C++ compiler manual3, which documents the SSE intrinsics supported by ICC and GCC. We will, for various practical reasons, use the Linux lab machines for this lab assignment. Section 3 introduces the tools and commands you need to know to get started. 2 Introduction to SSE The SSE extension to the x86 consists of a set of 128-bit vector registers and a large number of instructions to operate on them. The number of available registers depends on the mode of the processor, only 8 registers are available in 32-bit mode, while 16 registers are available in 64-bit mode. The lab systems you’ll be using are 64-bit machines. The data type of the packed elements in the 128-bit vector is decided by the specific instruction. For example, there are separate addition instructions for adding vectors of single and double precision floating point numbers. Some operations that are normally independent of the operand types (integer or floating point), e.g. bit-wise operations, have separate instructions for different types for performance reasons.