{"id":80940,"url":"https://github.com/rahulunair/awesome-oneapi","name":"awesome-oneapi","description":"An Awesome list of oneAPI projects","projects_count":171,"last_synced_at":"2026-10-18T19:00:25.019Z","repository":{"id":87094736,"uuid":"600122560","full_name":"rahulunair/awesome-oneapi","owner":"rahulunair","description":"An Awesome list of oneAPI projects","archived":false,"fork":false,"pushed_at":"2023-02-10T16:29:17.000Z","size":50,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-18T18:02:48.688Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"oneapi-community/awesome-oneapi","license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rahulunair.png","metadata":{},"created_at":"2023-02-10T16:26:54.000Z","updated_at":"2023-02-01T06:00:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"76ba452f-b6e7-4d16-a25d-44713dfcea88","html_url":"https://github.com/rahulunair/awesome-oneapi","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rahulunair%2Fawesome-oneapi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rahulunair%2Fawesome-oneapi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rahulunair%2Fawesome-oneapi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rahulunair%2Fawesome-oneapi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rahulunair","download_url":"https://codeload.github.com/rahulunair/awesome-oneapi/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":236460153,"owners_count":19152250,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-12-06T19:26:52.534Z","updated_at":"2026-10-18T19:00:25.026Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Projects","Visual","Libraries","Machine Learning","Tutorials","Papers","Blogs","Videos","Events","Community","Books"],"sub_categories":["HPC","Others","AI"],"readme":"# awesome-oneapi\nAn Awesome list of oneAPI projects\n\nA curated list of awesome oneAPI and SYCL projects for AI ahd HPC. Inspired by awesome-machine-learning.\n\n## What is oneAPI?\n\noneAPI is a industry standard spec that enables hetrogenuous computing\n- letting you write once and support many accelerators. For more\ninformation, you can read up at https://oneapi.io/\n\n## Table of Contents\n1. [HPC](#HPC)\n2. [AI](#AI)\n3. [Others](#Others)\n4. [Visual](#Visual)\n5. [Machine Learning](#Machine-Learning)\n6. [Tutorials](#Tutorials)\n7. [Libraries](#Libraries)\n8. [Papers](#Papers)\n9. [Blogs](#Blogs)\n10. [Videos](#Videos)\n11. [Announcements](#Announcements)\n12. [Events](#Events)\n13. [Communities](#Communities)\n14. [Books](#Books)\n15. [Contributions](#Contributions)\n\n## Projects\n\n### HPC\n* [GinkgoOneAPI](https://github.com/ginkgo-project/ginkgo) - In this project we want to explore the potential of having an Intel OneAPI backend for the Gingko software package: https://ginkgo-project.github.io/\n* [Open-source Scientific Applications and Benchmarks](https://github.com/zjin-lcf/oneAPI-DirectProgramming) - This repository contains a collection of data-parallel programs for evaluating oneAPI direct programming. Each program is written with CUDA\u001f SYCL\u001f and OpenMP target offloading. Intel® DPC++ Compatibility Tool (DPCT) can convert a CUDA program to a SYCL program.\n* [Acceleration of oil \u0026amp](https://github.com/menotti/oil_gas_fpga) -  gas applications on GPUs and FPGAs. The objective of this project is to use GPUs and FPGAs to accelerate applications in the oil and gas area.\n* [HPCToolKit](http://hpctoolkit.org/) - HPCToolkit is an open-source performance tool that is in some respects similar to VTune\u001f though it also works on Power and ARM architectures. It also works on NVIDIA and AMD GPUs. Our aim is to also use it for performance analysis of Intel GPUs with Intel’s OpenCL to our targets as a prelude to A0\n* [A Parallel Max-Miner Algorithm For An Efficient Association Rules Learning (ARL) And Knowledge Mining Using The Intel® oneAPI Toolkit](https://github.com/arthurratz/intel_max_miner_oneapi) - This project demonstrates the using of the Intel® oneAPI library to deliver a modern code in Data Parallel C++\u001f implementing a Parallel Max-Miner algorithm to optimize the performance of the association rules learning (ARL) process\n* [Boosting epistasis detection on Intel CPU+GPU systems](https://github.com/hiperbio/cross-dpc-episdet) - This work focuses on exploring the architecture of Intel CPUs and Integrated Graphics and their heterogeneous computing potential to boost performance and energy-efficiency of epistasis detection. This will be achieved making use of OpenCL\u001f Data Parallel C++ and OpenMP programming models.\n* [Comparitive performance of OpenCl Kernels](https://github.com/teptwomey/Sobel_Filter_rtl) - Working as part of a research effort to quantify performance differences between OpenCl kernels and RTL kernels.\n* [Grid](www.github.com/paboyle/Grid) - Lattice QCD particle physics code. \n* [TAU Performance System](https://github.com/UO-OACISS/tau2) - The TAU Performance System® supports profiling and tracing of programs written using the Intel OneAPI. Intel OneAPI provides two interfaces for programming - OpenCL and DPC++/SYCL for CPUs and GPUs. TAU supports both - the OpenCL profiling interface and Intel Level Zero API to observe performance. \n* [Fan Efficiency Particle Simulation](https://github.com/GuMiner/DPC-experiments) - This project simulates the efficiency of various fans to determine the best candidates for rapid prototyping (3D printing). \n* [gene sequence de-redundancy](https://gitee.com/ju_zhen/nGIA) - A novel Greedy Incremental Alignment-based algorithm called nGIA was proposed for sequence clustering with high efficiency and precision.  The nGIA consists of a pre-filter\u001f a modified short word filter\u001f a new data packing strategy\u001f a modified greedy incremental method\u001f and is parallelized via GPU.\n* [PRT](https://github.com/SynthNetics/PRT) - PRT - Parallel Routing Table\u001e\u001ethe concept is to facilitate parallel search \u001ethrough a table of Routing keys \u001e\u001eperhaps Intel might consider a onePRT module\u001e\n* [oneMD](https://github.com/allisterb/oneMD) - Data-parallel molecular dynamics simulator for Intel oneAPI.\n* [SYCL support in the OP2 DSL](https://github.com/OP-DSL/OP2-Common/tree/feature/sycl) - The introduction of SYCL/OneAPI in the OP2 DSL for unstructured mesh computations. The project evaluates different execution strategies for different target hardware and compilers.\n* [Cross-architecture high-order exhaustive epistasis detection on CPU and GPU devices](https://github.com/rjfnobre/crossarch-episdet) - DPC++ application for performing high-order epistasis detection searches targeting Intel DevCloud CPU and GPU nodes.\n* [N Body 4th Dimension Simulation](https://github.com/debashree19d/nbody) - An N-body simulation is a simulation of a dynamical system of particles\u001f usually under the influence of physical forces\u001f such as gravity\n* [Rocket projection using Quadratics](https://github.com/prilcool/Intel-devmesh-codeproject-four) - Use Quadratics to simulate a  rocket launch with a known equation from previous test launch \u001f and determine  maximum rocket altitude\u001f the time it will take to reach max height \u001fThe time  it will take  the rocket splash down into the ocean . so  you can take the perfect pictures  timed to perfection.\n* [Traffic Congestion Control Using oneApi](https://github.com/JohhnyKinuthia/oneapi-challenge) -  This project aims to solve the traffic congestion problem in cities using DPC++\u001eCongestion is a common phenomenon in most cities around the world. By taking a more proactive approach\u001f we can try and solve the congestion problem.\n* [Bioinformatic-Algorithms](https://github.com/artecs-group/nmf-dpcpp) - The computing time required to process large data matrices may become impractical\u001f even for a parallel application running on a multiprocessors cluster. NMF-DPC++ is an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance through SYCL.\n* [Monte Carlo Pi approximation 3 dimension](https://github.com/subarna20/pisimulation) - Monte Carlo Simulation is a broad category of computation that utilizes statistical analysis to reach a result. This sample uses the Monte Carlo Procedure to estimate the value of pi\n* [oneOLIGO](https://github.com/Eug9/oneoligo.git) - In project OneOligo\u001f we are using oneAPI for implementing scalable\u001f heterogeneous-parallel-processing algorithms that can be used quickly and accurate decode digital data stored in synthetic DNA generated by project OligoArchive. \n* [RSCode](https://github.com/yszheda/GPU-RSCode) - Accelerate Reed-Solomon coding for Fault-Tolerance in RAID-like system\u001ehttps://github.com/yszheda/GPU-RSCode\n* [XJoin](https://github.com/Eug9/XJoin) - Portable\u001f parallel hash join implementation across diverse XPU architectures with oneAPI\n* [ATLAS Charged Particle Seed Finding with DPC++](https://github.com/acts-project/acts) - The ATLAS Experiment is one of the general-purpose particle physics experiments built at the Large Hadron Collider (LHC) at CERN in Geneva. Its goal is to study the behavior of elementary particles at the highest energies ever produced in a laboratory\u001f help us better understand universe.\n* [MCsquare](https://gitlab.com/openmcsquare/MCsquare) - Fast Monte Carlo dose calculation algorithm for the simulation of PBS proton therapy.\n* [ArrayFire - oneAPI Backend](https://github.com/arrayfire/arrayfire) - ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs\u001f GPUs\u001f and other hardware acceleration devices. This project is to develop a oneAPI backend to the library\u001f which currently supports CUDA\u001f OpenCL\u001f and x86.\n* [A DPC++ Backend for the OCCA Portability Framework](https://github.com/libocca/occa) - OCCA—an open source\u001f portable\u001f and vendor neutral framework for parallel programming on heterogeneous platforms—is used by mission critical computational science and engineering applications of public and private sector organizations\u001f including the U.S. Department of Energy and Shell.\n* [Performance Evaluation of FPGA-based Hardware Accelerator for HPC applications](https://github.com/adarshmishra-ti/Performance-Evaluation-of-FPGA-based-Hardware-Accelerator-for-HPC-applications) - In this project\u001f we aim to find if FPGAs can be considered as a viable option as a hardware accelerator\u001f and if so\u001f how is their performance compared to existing processors like GPGPUs in various types of HPC workloads. We have an opportunity to take benefit of the recent developments in High-Level \n* [EEsim](https://github.com/danchitnis/EEsim) - EEsim is a circuit simulator based on ngspice and it runs inside the browser using WebAssembly technology. \n* [Accelerated Circuit Simulation using SYCL](https://github.com/FMarno/SYCL-LU-Decomposition) - Simulation of integrated circuits consists of solving matrix-based equations.  In this project we demonstrate the acceleration of LU decomposition as the core algorithm in solving circuits using SYCL and oneAPI on CPU and GPU. \n* [Simple Molecular Dynamics](https://github.com/VCCA2021HPC/simple-md) - Demonstrate use of SYCL for a basic molecular dynamics implementation\u001e\n* [DayDream](https://github.com/BLILLP/IntelDemo.git) - Memory management in humans\u001f algorithms and poc using parallel processing\n* [Matrix Multiplication on Intel DevCloud using DPC++](https://github.com/KastnerRG/Read_the_docs/blob/master/docs/project6.rst) - This project is about hardware accelaration on Intel DevCloud for one API for using DPC++ on different platforms using multicore CPU\u001fGPU\u001fFPGA which uses a single programming methodology for different hardware accelarator components defined in the upgrading and for a better programmity of processor.\n* [oneAPI HPC-AVX](https://xingyuanjie.top/2021/10/23/AVX%E5%90%91%E9%87%8F%E5%8C%96%E5%AD%A6%E4%B9%A0(%E4%B8%80)/) - Summary of oneAPI-HPC-AVX\u001eAdd two double arrays using the AVX instruction\n* [Homogeneous and Heterogeneous Implementations of a tridiagonal solver on Intel® Xeon® E-2176G with oneMKL getrs](https://github.com/olutosinbanjo/oneMKL_getrs.git) - Homogeneous and Heterogeneous implementations of a tridiagonal solver with oneMKL getrs \n* [ROS-oneAPI](https://github.com/ftyghome/ROS-oneAPI) - A ROS package that brings intel's oneAPI to the ROS framework.\u001eThis repository provides an example of vector summation on ROS using the Intel oneAPI framework. With oneAPI\u001f the summation operation can be run parallelly on CPUs GPUs and even Intel FPGA devices.\n* [eXTreme fine-grAined concurrent taSKing framework](https://gitlab.com/pnookala/llvm-openmp.git) - XTASK enables extreme fine-grained parallelism across modern many-core architectures with hundreds of cores by implementing a novel lock-less multiple producer multiple consumer\u001f out-of-order queuing mechanism for managing parallel tasks.\n* [Observing OpenMP GPU runtime events with the OMPT interface using oneAPI](https://github.com/adamtuft/ompt-target-events) - Using the OpenMP tool interface in the oneAPI OpenMP implementation to observe GPU runtime events on Intel GPUs.\n* [Phase field solvers using SYCL for microstructure evolution](https://github.com/ICME-India/MicroSim) - Phase field technique is used to simulate microstructure evolution during materials processing such as 3D printing and additive manufacturing apart from traditional manufacturing techniques like welding\u001f casting etc. These non-linear PDE solvers are compute intensive and also memory intensive.\n* [Direction Field Visualization with Python](https://github.com/olutosinbanjo/direction_field) - This project demonstrates the visualization of a direction field with Python using the differential equation of a falling object as a case study.  The effectiveness of Heterogeneous Computing is also shown by exploring optimized libraries added functionalities in Intel® Distribution for Python.\n* [ACTS GPU R\u0026amp](https://github.com/acts-project/traccc) - D Projects\n* [Parallel Realization of DCT Algorithm](https://github.com/derolol/oneapi_dct) - This project uses oneAPI to realize the parallelization of discrete cosine transform\u001f and completes the performance test of the algorithm on the DevCloud platform\n* [Parallel-Computing——GaussianElimination](https://gitee.com/hulakk/parallel-computing-public) - 高斯消元法是一种将普通矩阵化为行阶梯矩阵的方法，该方法可用于求解线性方程组，本实验的内容为高斯消元法的并行化实现。\n* [Logistic Regression](https://github.com/Coolog/Logistic-Regression.git) - 高性能报告\n* [Intel DevMesh/CRC64_OpenMP](https://gitee.com/Robin_Chan/hpc-crc64-openmp) - Through Intel oneAPI software model\u001f the CRC64 parallel computing based on C/C++ OpenMP Offload is implemented in Jupyter hub.\n* [Parallel Algorithm of Sparse Matrix Multiplying](https://github.com/G0atKing/Parallel-Algorithm-of-Sparse-Matrix-Multiplying) - This project is based on Intel oneAPI to realize the parallelization of sparse matrix multiplication in COO format. Through SIMD vectorization\u001f pThread multithreading\u001f and openMP multithreading.\n* [MPIwithACOonTSP](https://github.com/lijinyong0618/MPIwithACOonTSP) - Assignment Project for High Performance Computing at South China University of Technology. Solving TSP problems by parallelized ant colony algorithms. Created by Li Jinyong(202220143152) and Liu Yitong(201930330186).\n* [Accelerating Irregular Codes on Elastic Dataflow Architectures](https://github.com/robertszafa/llvm-sycl-passes) - Current HLS tools fail to synthesise efficient architectures for irregular codes because they rely on\u001estatic scheduling. Elastic dataflow techniques enable circuits to be scheduled dynamically and achieve\u001ea higher performance in accelerating irregular codes.\n\n### AI\n* [Predicting Corn\u001f Wheat and Soybean Yield](https://github.com/mmtokay/CropYield) - Precipitation and temperature are the two main characteristics of weather and climate\u001f they also play an important role on the outcome of crop yield.  In this study the goal is try to predict crop yield for corn\u001f wheat and soybeans using meteorological data using machine learning.\n* [Efficiency and productivity for decision-making on mobile SoCs](https://bitbucket.org/corbera/vi-mdp/src/oneAPI/) - Markov decision processes provide a formal framework for a computer to make decisions autonomously and intelligently when the effects of its actions are not deterministic. To solve this computationally complex problem\u001f we experiment with scheduling on a low-power heterogeneous CPU+GPU SoC platform.\n* [MixPose Live Yoga Stream](https://github.com/mixpose/) - MixPose is personalized AI fitness with real-time live stream yoga\u001f yoga for beginners friendly and weekly free yoga classes available.\n* [NASA Frontier Development Lab:  Space Resources - Localization](http://moonbench.space/) - This project developed: (1) a virtual dataset designed to test automated localization algorithms on planetary surfaces\u001f such as the Moon or Mars\u001f where GPS is unavailable; (2) a neural network approach for absolute localization by matching orbital maps with surface perspective imagery.\n* [Kick starting TensorFlow With Intel® AI Analytics Toolkit(Beta) Powered by oneAPI](https://github.com/ialimustufa/oneAPI) - Try your hands on Some amazing SOTA Models with Intel® one API in your browser without any installation;\u001eIn this project\u001f I am covering 3 Models from Intel Model Zoo and evaluating them powered by Intel® oneAPI platform!\n* [Enhancing old photos with Deep Learning](https://github.com/tinuxnet/PhotoEnhancer) - Combining in the right way some Deep Neural Network we can enhance old photos non only restoring them but also improving the resolution and recreating more details.\n* [Detecting Acute Lymphoblastic Leukemia Lymphoblasts with Tensorflow/oneAPI/OpenVINO \u0026amp](https://github.com/aiial/hias-all-oneapi-classifier) -  Neural Compute Stick An open-source classifier programmed using the Intel® Distribution for Python* and trained using Intel® Optimization for TensorFlow*. The model is deployed on a Raspberry 4 using Intel® Distribution of OpenVINO™ Toolkit and inference is carried out using Neural Compute Stick 2.\n* [Robust Transformer](https://github.com/abhijitmjj/Robust-transformer) - A tool for designing reproducible Machine learning experiments. It’s an end to end functional style data transformation distributed pipeline built on top of Intel OneAPI and Google JAX. \n* [Using Intel Technologies](https://github.com/aiial/all-detection-system-for-magic-leap-1) -  Magic Leap 1 to detect Acute Lymphoblastic Leukemia (ALL) Lymphoblasts Combines Magic Leap's Spacial Computing technologies with Intel's oneAPI\u001f OpenVINO  Neural Compute Stick to provide real-time classification of Acute Lymphoblastic Leukemia Lymphoblasts in peripheral blood samples within a Mixed Reality environment.\n* [Using oneAPI\u001f Intel NUC  NVIDIA Jetson Nano to detect Acute Lymphoblastic Leukemia](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier) - The Acute Lymphoblastic Leukemia Jetson Nano Classifier is a Convolutional Neural Network developed using Intel® oneAPI AI Analytics Toolkit Intel® Optimization for Tensorflow on an Intel® NUC NUC7i7BNH  to accelerate training and TensorRT for high performance inference on NVIDIA® Jetson Nano.\n* [NMS by DPC++](https://github.com/intel-sandbox/ICE_dpcpp_sample/tree/master) - Non-maximum supression is a algorithm for many detection/tracking use case. But for large data NMS is time-consuming. So making this algorthm paralleled is necessary.\n* [Deep Reinforcement Learning in STIOT](https://github.com/yhaowang666/MEC-DQL.git) - A policy based deep reinforcement learning algorithm for task offloading and resource allocation.\n* [Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL](https://github.com/SC-SGS/Distributed_GPU_LSH_using_SYCL) - In the age of AI\u001f algorithms must efficiently cope with vast data sets. We propose a performance-portable implementation of Locality-Sensitive Hashing (LSH)\u001f an approximate k-nearest neighbors algorithm\u001f using different SYCL implementations—ComputeCpp\u001f hipSYCL\u001f DPC++—supporting multiple GPUs.\n* [English2words](https://jupyter.oneapi.devcloud.intel.com/user/u110299/doc/tree/English2words) - A project to identify the vocabulary frequency of the \"English 2\" test paper.\n* [Skin Cancer Detection](https://github.com/srianumakonda/Skin-Cancer-Detection) - Used Convolutional Neural Networks + Deep Learning algorithms to create a learned agent that can perform binary classification of malignant vs benign skin cancer.\n* [DataGAN: Leveraging Synthetic Data for Self-Driving Vehicles](https://github.com/srianumakonda/DataGAN) - Leveraging Generative Adversarial Networks to create self-driving data at scale is crucial. By emphasizing a focus on DCGANs\u001f I’m focusing on creating high-quality self-driving images that can be used to train and improve the performance of computer vision models.\n* [SiteMana](https://github.com/SiteManaApp) - Using oneAPI powered personalization engine to converts a website visitors into a buyers\n* [Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages](https://github.com/Debapriya-Tula/EACL2021-DravidianTask-Bitions) - we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance\u001f Tamil and Latin).\n* [Gavin AI](https://github.com/Gavin-Development) - Gavin AI is a project\u001f created by Scot_Survivor (Joshua Shiells) \u0026amp; ShmarvDogg\u001f which aims to have Englsih human like conversations through the use of AI and ML. Gavin works on the Transformer architecture however\u001f Performer \u0026amp; FNet architectures are being investigated for better scaling.\n* [LQ Visual Inspector](https://github.com/TebogoNakampe/leather-inspector) - Leather Quality Visual Inspection: a purpose-built solution for faster more accurate leather quality control\n* [AccelerateDataMining](https://github.com/lkk688/DeepDataMiningLearning) - Accelerate data mining and deep learning applications via Intel onapi. Provide sample code and examples in SJSU CMPE255 Data Mining class (https://catalog.sjsu.edu/preview_course_nopop.php?catoid=12\u0026ampcoid=58423) to demonstrate the importance of acceleration and use Intel oneAPI as one technological e\n* [DREAMPlaceFPGA](https://github.com/rachelselinar/DREAMPlaceFPGA) - This work aims to accelerate the different stages involved in FPGA placement - global placement\u001f legalization and detailed placement\u001f using the Pytorch deep-learning toolkit. Placement in the FPGA design flow determines the physical locations of all the heterogeneous instances in the design.\n* [Virtual  Construction Inspector](https://github.com/cabelo/VirConI) - Personal Protective Equipment (PPE) Detector\n* [Accelerating SeqAn](https://github.com/seqan/seqan3) - This project targets specifically the acceleration of the pairwise alignment algorithms and pattern matching algorithms contained in the SeqAn library. \n* [Drift Detection for Edge IoT Applications](https://github.com/blackout-ai/Face_Aging_Concept_Drift) - This concept drift project is run on video and image datasets such that we can calculate an overall precision and standard error. \u001e\u001eThe concept drift detection technique finds True positives and False negatives using real and virtual drift detection. \n* [Alice](https://github.com/intel/dffml/tree/alice/entities/alice/) - We are writing a tutorial for an open source project on how we build an AI to work on the open source project as if she were a remote developer. Bit of a self fulfilling prophecy\u001f but who doesn't love an infinite loop now and again.\n* [Performance and Portability Evaluation of the K-Means Algorithm on SYCL with CPU-GPU architectures](https://github.com/artecs-group/k-means) - This work uses the k-means algorithm to asses the performance portability of one of the most advanced implementations of the literature\u001f He-Vialle\u001f over different programming models (DPC++\u001f CUDA\u001f OpenMP) and multi-vendor CPU-GPU architectures.\n* [Exhibition Art - Shipping Cost Predictor](https://github.com/deepakjoshi2k/Machine-Learning-Exhibit-Art-Shipping-) - My project is the complete analysis of the data of a Shipping Company. The main aim is to predict the cost of shipping provided the required entries were given but along with the EDA\u001f it becomes much more than just the Cost Predictor.\n* [Simple Neural Network Benchmark using oneAPI and CUDA](https://github.com/Goleys/oneAPI_NeuralNetwork_Research.git) - We will create a simple Neural Network using CUDA and DPC++. Then\u001f we will use oneAPI to test our code using an Intel base workstation and compare it with the performance of an NVIDIA GPU.\n* [Electric Vehiles' Charging Patterns](https://github.com/Prajwal111299/Electric-Vehicle-Charging-Patterns) - Using EVs' charging data\u001f I explored when drivers are likely to plug in their cars\u001f and how much additional electricity demand will be created when the number of EVs increases.\n* [Image Registration](https://github.com/KID-1412-git/Image-Registration.git) - 采用并行算法，加速模板图片在原图上的区域匹配，得到匹配的区域。\n* [OneAPI Machine Learning Library](https://example.com/this-is-to-come) - This project will be a Machine Learning Library written by myself. To help me learn Machine Learning in depth. Including but not limited to\u001f NLP\u001f Image Recognition\u001f Image Classification\u001f and Audio Recognition. \u001eIt will have Bindings to Python \u0026amp; C++. \u001eLogos \u0026amp; Official Images to come.\n* [Modin gives Pandas wings](https://www.kaggle.com/code/rishirajacharya/oneapi-modin-gives-pandas-wings) - An element of the oneAPI AI Analytics toolbox is Intel® Distribution of Modin. With little any work\u001f you can scale data analytics and speed up your current Pandas code. Learn how to run the same Pandas code 10\u001f 20\u001f or 30 times quicker.\n* [Answer sheet Assessment and Checking: oneAPI Deep Neural Network Project](https://github.com/AftabAhmedAbro/Answer-sheet-Assessment-and-Checking-oneAPI-Deep-Neural-Network-Project) - Project on making a system using oneAPI Deep Neural Network Library to check and assess the answer sheets of thousands of candidates for various tests for organizations using ML/DL techniques\u001eDataset: with the help of the MNIST dataset\n* [Novel Applications of Transformer Models in Data Interpretation and Visualization](https://github.com/andreicozma1/oneAPI-viz) - This project explores the possibilities of using attention-based transformer models to aid in tasks related to data visualization and interpretation\u001f through the use of various Intel oneAPI toolkits such as the DNN library\u001f oneAPI AI Kit\u001f as well as oneAPI Render Kit.\n* [RISK IDENTIFICATION IN PREDICTIVE ESTIMATION](https://github.com/QuanticTechnovations/OneAPI_RiskAnalyzer) - Significant business decisions is taken based on the outcome predicted by the ML model. Users get benefit from an unbiased\u001f statistically sound and rigorous re-validation of the prediction’s accuracy from an independent source. That is what the Predictive Risk Analyzer tool from Quantic aims to go.\u001e\n* [Neural style transfer optimization technique using Generative adversarial neural networks(GANs) to create an NFT.](https://github.com/willywairagu/AICE/tree/master/ASTIS_Week5_GANs-NFTs-main) - Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image\u001f but “painted” in the style of the style reference image.\n\n\n### Others\n* [Hospital-Management-System](https://github.com/ParthSabhadiya/Hospital-Management-System) - We Design one website it contains patient all medical history with health card unique number\n* [Applications of Naive Bayes Classifiers and Algorithms and Detailed Use-Cases](https://github.com/vishruthb/naivebayes) - Strategical analysis and use cases of the Naive Bayes classifiers including Iris datasets\u001f Gaussian number sets\u001f and more that will soon be documented in the project repository.\n* [Student Management System](https://github.com/Sukanya41455/Student_management_system) - This project was developed to keep records of the students and their academic details.\n* [Spatter](https://github.com/hpcgarage/spatter) - Spatter is a new benchmark tool for assessing memory system architectures in the context of a specific category of indexed accesses known as gather and scatter. oneAPI and DevCloud are used to develop support for a oneAPI backend for Spatter that can be targeted to Intel FPGAs.\n* [EdgeWare](https://github.com/GbllYao/EdgeWare) - When multiple services running on the resource-constrained edge devices\u001f how limited resources can dynamically support the desired services is of the utmost importance for both researchers and developers.  Efficiently and dynamically managing edge services becomes critical for edge platforms. \n* [Data security : Use oneAPI to crack insecure passwords](https://github.com/prilcool/Intel-devmesh-codeproject-three) - This article shows you how to use Intel's  DPC++ to crack insecure  user passwords based on MD5 hashes.\n* [GWSynth](https://github.com/AndrewPastrello/GWSynth) - Synthesizes audio from gravitational waveforms produced by binary black hole inspiral-merger-ringdown simulations. The ODE solver stage of the simulation has been modified to use the Parareal parallel-in-time integration method implemented in DPC++ with Intel oneAPI.\u001e\n* [Rubik's Cube Solver Command Line (CLI) Tool ](https://github.com/kazi92/rubikSolver) - Rubik's cube solver using parallel processing through CLI tool\n* [Blockchain with DPC++](https://github.com/ani4aniket/one-api-dpcpp-blockchain) - In this project\u001f I am using OneAPI to run a scalable blockchain module completely from scratch written in C++.\u001e\n* [Hash Viewer](https://github.com/mwt/hash-viewer) - You can use this to store and share entire documents in URLs. It is similar to Hashify but has a simpler presentation\u001f uses LZ-based compression via lz-string\u001f and supports LaTeX math input via MathJaX. It is hosted on GitHub Pages and uses marked for markdown processing.\n* [Acceleration of Kalman Filter applications on GPUs and FPGAs.](https://github.com/menotti/Kalman-Filter-for-Beginners/) - The objective of this project is to accelerate implementations of the Kalman filter for various purposes\u001f including the calculation of drone attitude.\n* [ oneAPI Video Processing Library Package for openSUSE Linux ](https://build.opensuse.org/package/show/home:cabelo:intel/intel-oneapi-onevpl) - This project is an initiative to provide the   oneAPI Video Processing Library packages for openSUSE Linux distributions.\n* [openSUSE for Intel ARC](https://build.opensuse.org/project/show/home:cabelo:intel) - Individual initiative in Brazil of the world's first Linux image with native kernel 6.2-rc5 or higher with Intel ARC stable open source driver. All GPU detection procedure takes place automatically during installation.\n* [面向TSP问题的改良遗传算法(GA)](https://github.com/QuietHuihui/oneAPI_TSP) - 高性能计算与云计算实验4以及大作业\u001e使用oneAPI将遗传算法进行部分并行以及加速改良，提高其解决旅行商问题的效率。\u001e\n* [OneAPI_homework](https://github.com/Xiaozaichen/OneAPI_homework) - 使用intel oneAPI AI Analytics Toolkit对connect-4数据集加速使用支持向量机分类，connect-4数据集是记录了棋盘各种分布，并且带有输赢和平局的标签，使用该数据集训练SVM然后进行预测。\u001e使用intel oneAPI HPC Toolkit对高斯消去法的矩阵计算加速\u001e\n* [mat_mul_test](https://github.com/CrazyNicolas/oneAPI-samples/tree/master/DirectProgramming/DPC%2B%2B/DenseLinearAlgebra/matrix_mul) - a short matrix multiplication\n* [Gaussian Elimination](https://github.com/shizia/Gaussian_Elimination) - Gaussian Elimination based on Intel oneAPI\n* [GaussianElimination_oneAPI](https://gitcode.net/qq_43786455/ge) - a parallel algorithm of gaussian elinimation\n* [Parallel Pattern-Defeating Quicksort with Intel® oneAPI Thread Building Blocks](https://github.com/SneezeFor16Min/oneapi-parallel-pdqsort) - Parallelized Pattern-Defeating Quicksort (2021) using Intel® oneAPI TBB and C++ 20 features.\n* [Crypto Info API](https://github.com/rahulkarda/crypto-info-api) - The API serves informational content for a wide variety of decentralized blockchains/cryptocurrencies and smart contract platforms.\n* [Rasty](https://github.com/seelabutk/rasty) - Rasty is a wrapper around Ospray that offers a way to render geo-spatial data with information about the location mapped onto the meshes via a colormap. The expected input data is a geotiff file and a netcdf file.\n* [Substrate](https://github.com/seelabutk/substrate) - Substrate is a tool that allows you to automate the process of deploying visualization tools to different computing environments.\n* [Fractal Marcher](https://github.com/branson2015/FractalMarcher) - Fractal rendering utilizing Ray Marching\n* [toyBrot](https://gitlab.com/VileLasagna/toyBrot) - toyBrot is a raymarching fractal generator that is used both as a  simple benchmarking tool and a study tool for parallelisation. The code is is implemented with over 10 different technologies\u001f including Intel TBB\u001f ISPC and SYCL (with support for oneAPI)\n* [TornadoVM](https://github.com/beehive-lab/TornadoVM) - TornadoVM is an open-source software technology that automatically accelerates Java programs on multi-core CPUs\u001f GPUs\u001f and FPGAs.\n* [vivid-oneAPI](https://github.com/oneTBB-UMA/vivid-oneAPI) - Heterogeneous image processing using some of the most popular features of oneAPI.\n* [Mandelbrot set plotter](https://github.com/Shmarvadon/Mandlebrot_Plot) - This is a program i designed to plot the Mandelbrot set\u001f it can do this either with 1 thread\u001f all threads or utilising SYCL via intels DPC++ compiler included in oneAPI toolkit to use GPU acceleration.\n* [Intel Embree Package for openSUSE Linux](https://build.opensuse.org/package/show/home:cabelo:intel/embree) - This project is an initiative to provide the Intel Embree packages  for openSUSE Linux distributions.\n* [CapFrameX](https://github.com/CXWorld/CapFrameX) - Frametimes capture and analysis tool based on Intel's PresentMon. Overlay provided by Rivatuner Statistics Server.\n* [SYCL Container](https://github.com/danchitnis/sycl-container) - A container for development SYCL code. Currently work with Intel CPU, CUDA GPU. (Intel GPU work in progress). Can be used independently of with VScode remote extension\n* [oneAPI_project_SpMV](https://gitee.com/baibai1/one-api_project_-sp-mv/tree/master/) - 此项目是由cuda源代码实现的稀疏矩阵向量乘利用dpct工具自动转为dpc++，然后手动修改少部分代码后迁移而来的项目，主要实现了SpMV计算接口。矩阵和向量都是随机生成，矩阵采用CSR压缩格式存储计算，还未来得及利用local memory进行加速和CSRv算法优化，待有时间时再做优化。本项目的大量注释代码亦有可取之处，device_spmv函数中kernel函数大量注释为CSRv的算法实现，更符合gpu的访存模式和local memory能带来较大的加速效果。另main中的注释代码可进行设备预热，性能计时。verify函数是gpu计算结果与cpu计算结果对比，用以判断gpu部分代码正确性。\n\n## Visual\n* [Certiface Anti-Spoofing](https://github.com/cabelo/oneapi-antispoofing) - Certiface AntiSpoofing use oneAPI for fast decode video for perform liveness detection with inference. The system is capable of spotting fake faces and performing anti-face spoofing in face recognition systems.\n* [Intel® oneAPI Rendering Toolkit and its Application to Games](https://software.intel.com/oneapi/render-kit) - Learn how to use the Intel® oneAPI Rendering Toolkit for high fidelity visualization and photo-realistic rendering and how it gets used in games today.\n* [Intel® Open Image Denoise in Blender - GDC 2020](https://openimagedenoise.github.io) - Overview and demonstration of Intel® Open Image Denoise in Blender 2.82.  This demo explains denoising for ray tracing and provides step by step example using the Intel® Open Image Denoise in Blender 2.82.  The Intel® Open Image Denoise library\u001f part of the Intel® oneAPI rendering toolkit\n* [Using Intel Technologies](https://github.com/aiial/hias-all-oneapi-classifier) -  Oculus Rift to detect Acute Lymphoblastic Leukemia (ALL) Lymphoblasts Combines Oculus Rift's Virtual Reality technologies with Intel's oneAPI\u001f OpenVINO \u0026amp; Neural Compute Stick to provide real-time classification of Acute Lymphoblastic Leukemia Lymphoblasts in peripheral blood samples within a Virtual Reality environment.\n* [Dust3D](https://github.com/huxingyi/dust3d) - Dust3D is a free and open source 3D modeling application that accelerates pipelines and allows for the creation of assets with texture and rigging automatically generated. It’s very intuitive and features auto UV unwrapping\u001f auto-rigging\u001f PBR Material support\u001f as well as pose and motion authoring.\n* [Intel® Open Image Denoise in Blender Preview](https://openimagedenoise.github.io) - Overview and demonstration of Intel® Open Image Denoise in Blender 2.9. This demo explains denoising for ray tracing and provides step by step example using the Intel® Open Image Denoise in Blender 2.9 render preview. The Intel® Open Image Denoise library\u001f part of the Intel® oneAPI rendering toolkit\n* [Blur and Anonymize Faces using OpenCV Deep Learning Model ](https://github.com/DEBANJANAB/Face-Blur-and-Anonymization-using-OpenCV) - Face blurring is a computer vision method used to anonymize faces in images and video. In this project\u001f I have used Deep Learning based Face Detector to detect the face we need to blur. OneAPI will be incorporated to make using the code easier in every machine. \n* [Medical Imaging 2D Convolution Demo](https://gitlab.devtools.intel.com/agolden/hls-medical-imaging-convolution-demo) - During my time as an intern at Intel (Summer 2020)\u001f I worked with Health and Life Sciences in the Medical Imaging segment.  My project for the summer was to create a demo that runs convolution on medical images on the oneAPI Development Cloud.\n* [HIAS TassAI Facial Recognition Agent](https://github.com/AIIAL/HIAS-TassAI-Facial-Recognition-Agent) - Security is an important issue for hospitals and medical centers to consider. Today's Facial Recognition can provide ways of automating security in the medical industry\u001f reducing staffing costs and making medical facilities safer for both patients and staff.\n* [N.I.A](https://github.com/srikarsamudrala/N.A.I-gaze-estimation) - I have teamed up with my friends Sharan Babu and Matev to create a Gaze detection system that lets you control your desktop mouse with your eyes. With the help of Openvino we have completed out project.\n* [Migrating and Tuning a CUDA-based stencil computation to DPC++ using oneAPI](https://github.com/cs2isenaicimatec/oneAPI-solving-stencil-migration) - We propose the tuning and migration of a CUDA-based RTM to a DPC++ application by applying DPC++ Compatibility Tool. We aim to demonstrate the versatility of oneAPI to build unified code capable of being executed in different processing units such as CPUs and GPUs with low implementation cost.\n* [RealsenseID Pam Face Authentication](https://github.com/cabelo/pam-realsenseid) - The su/sudo command in the operating system has the function of scaling administration privileges to the user. In this project\u001f we will see how to increase security by including Realsense ID Facial Biometrics technology together with the password\u001f thus creating an additional layer of authentication\n* [Raytracing From CUDA to DPC++](https://github.com/shaovoon/simple_raytracing_sycl2020) - A walkthrough of converting a code from parallel C++ ray-tracing code to CUDA\u001f and the work needed to make that CUDA code run on CPU using parallel for_each() and then converted the code to SYCL 2020 via Intel® DPC++. This is an entry competing in The Great Cross-Architecture Challenge.\n* [stereo matching](https://github.com/silverfly1992/stereo-matching-dpc) - Stereo vision methods are widely used in photogrammetry\u001f automatic driving\u001f industrial inspection\u001f virtual reality and other fields. Stereo matching\u001f as a key step in stereo vision\u001f has a great influence on the accuracy and speed of measurement results. The heterogeneous acceleration technology base\n* [Fractal Tricorn Mandel Brot](https://github.com/ranjan1977i/Fractal.git) - Fractal implementation of Conjugate of MandelBrot\n* [rscreatelg](https://github.com/cabelo/rscreatelg) - This software is designed to generate holographic video for Looking Glass holographic screens. I'm not associated with the Looking Glass Factory and I developed this project in my spare time to use Looking Glass on GNU / LINUX. I use the opencv library and admire the camera's Intel Realsense camera\u001f\n* [Dehazing Images using Feature Attention and Knowledge Distillation](https://github.com/manncodes/dehazing-openvino) - This project presents an end-to-end feature fusion attention network (FFA-Net) to directly restore the haze-free image. To further accelerate\u001f we distill knowledge from a Teacher model( generally a model achieving SOTA) to the student model(with significantly less parameters than Teacher).\n* [MediaMapper](https://github.com/nikkhiel/mediamapper) - Mediamapper project plans to produce map of topics on social media based on keywords\u001f hashtags\u001f titles\u001f labels\u001f captions\u001f search-words as well as images and videos. The name MediaMapper signifies the mapping of topics and trends on the overall social media all over the Internet. \n* [Computer Pointer Controller OpenVINO](https://github.com/Nitin-Mane/Computer-Pointer-Controller) - the task is based on a gaze detection model to control the mouse pointer of the computer. The module state on the Intel model usage and will be applying the Gaze Estimation model to estimate the gaze of the user's eyes and change the mouse pointer position accordingly.\n* [iDVR Intelligent Digital Video Recording ](https://github.com/cabelo/idvr) - The objective of the project is to obtain a stream of h265 videos encoded by external devices or by the server itself. But with a big difference: the stream or video will be indexed by models of convolutional neural networks. The system will find specific scenes without evaluating all media content.\n* [OpenCV Image Stitching oneAPI](https://github.com/forkbomb-666/opencv-image-stitching-oneapi) - A demo of running OpenCV Image Stitching API which can be used to fuse satellite/space images or create a panorama.\n* [The Magic of Fractals](https://github.com/AbhiLegend/one-api-fractal-dpcpp) - In colloquial usage\u001f a fractal is \"a rough or fragmented geometric shape that can be subdivided in parts\u001f each of which is (at least approximately) a reduced/size copy of the whole\". The term was coined by Benoît Mandelbrot in 1975 and was derived from the Latin fractus meaning broken or fractured.\u001e\n* [Intel® oneAPI Rendering Toolkit introduction: GDC 2021 showcase](https://software.intel.com/oneapi/render-kit) - Develop with the Intel® Rendering Toolkit for high fidelity and high performance ray tracing applications. Improve your games with ray tracing experiences.\n\n## Machine Learning\n* [isolalert](https://github.com/cabelo/isolalert) - Project created to analyze the behavior of the population\u001f calculate the necessary distance between people to avoid contagion (COVID-19) and statistical processing of the number of people and vehicles in a given region.\u001e\n* [Fighting Novel Coronavirus COVID-19 with Data Science \u0026amp](https://github.com/azeemx/fighting_covid19_with_ds_ml.git) -  Machine Learning. In December 2019\u001f A novel Coronavirus was found in a seafood wholesale market located in Wuhan\u001f China.\u001f On 11 February 2020\u001f WHO announced a name for the new coronavirus disease: COVID-19.\u001f And was recognised as a pandemic on 11 March 2020.\n* [ORM One Remote Monitor](https://github.com/cabelo/orm) - COVID-19 will change people's habits. We will use teaching and remote work (home office) more frequently\u001f schools and companies will need to measure student performance and employee commitment.\n\n## Tutorials\n* [Pro TBB Book code samples ported to oneAPI](https://github.com/Apress/pro-TBB) - The latest book on Threading Building Blocks (TBB) was recently published by Apress. The book comes with code samples available on GitHub. This project intends to port some of the examples to oneAPI to take advantage of the new features of this promising heterogeneous programming model.\n* [oneAPI example](https://github.com/fgq1994/NLP-Tutorials/blob/master/transformer.py) - transformer example\n* [Computer Graphics Introduction](https://gitlab.com/bkmgit/cgintro) - Ray tracing tutorial example\n* [Integral_DPCPP](https://github.com/norhidayahm/integral_dpcpp) - This project is modified from the integral project from the course \"Fundamentals of Parallelism on Intel Architecture\" by Dr. Andrey Vladimirov in Coursera. The codes are converted to C++ and DPC++. \n* [dpcpp-tutorial](https://github.com/acanets/dpcpp-tutorial) - This is a collection of DPC++ sample programs that demonstrate the design\u001f analysis and optimization of DPC++ programs\n* [oneAPI_samples](https://github.com/Mrhuajie-wu/gw3_oneAPI_samples) - sample1:计算随机变量的数字特征，如单个变量的期望、方差，两个变量之间的协方差。同时实现两个变量之间的线性回归。\u001esample2:卷积算法有很强的并行性，通过将每次核的矩阵乘法运算并行实现算法的并行化。\u001esample3:展示数据在传输中以及计算中的具体情况。\n* [Parallel assignment](https://github.com/ouc16020021031/Parallel-assignment) - The program can assign and initialize values in parallel\n* [Tweet_SentimentAnalysis_ML](https://jupyter.oneapi.devcloud.intel.com/user/u78961/lab) - Build a binary classifier to categorize the tweets in the training data as a\u001e1. Disaster/Attack/Calamity with potential loss of Life and property\u001e2. Tweets unrelated to loss of life and property\u001e3. Create a function which can take a tweet as input and classify it \n* [SolvingLinearEquations](https://github.com/LazyTigerLi/SolvingLinearEquations) - This project gives an example of solving linear equations by iterative method\u001f which is implemented using DPC++ language for Intel CPU and accelerators. However\u001f the calculation may not converge currently.\n* [Loop Unroll](https://github.com/shumona8/loopunrolldifferently) - Doing loop unroll using three variables\n* [Banking](https://github.com/prilcool/Intel-devmesh-codeproject-one) -  Financial Audits through parallel computing DPC++ ( Process Millions of records in seconds) Design scalable and flexible  parallel data processing and audit systems with Intel DPC++.   We show you how you can utilize DPC++  to  efficiently process millions of records in parallel in under 60 seconds . For Auditing interest  paid to each user based on the users dynamic account  balances . \n* [Pearson’s Correlation Coefficient](https://github.com/prilcool/Intel-devmesh-codeproject-two) -  Linear Regression  with DPC++ We  Implement  two  Statistical Mathematical Algorithms such as Pearson’s Correlation Coefficient \u0026amp Linear Regression  with DPC++  and  show you how to implement this algorithms in real life in sales and marketing  to  forecast Future sales based on advertising expenditure.\n* [Intel ONE API DPC++ Vector Multiplication](https://github.com/AbhiLegend/DPC-) - The vector-multiplication is a simple program that multiplies Three large vectors of integers and verifies the results. This program is implemented using C++ and Data Parallel C++ (DPC++) languages for Intel(R) CPU and accelerators.\u001e\u001eIn this example\u001f you can learn how to use the most basic code in C\n\n\n## Libraries\n* [oneDNN](https://github.com/oneapi-src/oneDNN) - oneAPI Deep Neural Network Library\n* [oneDAL](https://github.com/oneapi-src/oneDAL) - oneAPI Data Analytics Library\n* [level zero](https://github.com/oneapi-src/level-zero) - oneAPI Level Zero Specification Headers and Loader\n* [oneDAL](https://github.com/oneapi-src/oneTBB) - oneAPI Threading Building Blocks\n* [oneCCL](https://github.com/oneapi-src/oneCCL) - oneAPI Collective Communications Library\n* [oneMKL](https://github.com/oneapi-src/oneMKL) - oneAPI Math Kernel Library\n\n## Papers \n* [SUPRA-on-oneAPI](https://github.com/intel/supra-on-oneapi) - This paper shows the general process of migrating CUDA-based ultrasound imaging project to Intel oneAPI. we  migrated and optimized the generated DPC++ code\u001f  it can now run on Intel CPU\u001f integrated GPU(Gen9)\u001f Xe MAX GPU and Arria 10 FPGA. the B-mode imaging performance meet real-time requirement.\n* [Parallel Three-Way Quicksort Using Data Parallel C++ and Intel® oneAPI Toolkits](https://github.com/arthurratz/intel_parallel_stable_sort_oneapi) - A case-study of the classical three-way quicksort performance optimization using the revolutionary Intel® oneAPI HPC Toolkit\n\n## Blogs\n[oneAPI @ Devto](https://dev.to/oneapi) - oneAPI blogs\n\n## Videos\n[GPU Performance Portability @ CppCon 2022](https://www.youtube.com/watch?v=8Cs_uI-O51s) - GPU Performance Portability Using Standard C++ with SYCL - Hugh Delaney \u0026 Rod Burns - CppCon 2022\n[IWOCL 2021 - Intro to SYCL](https://www.youtube.com/watch?v=VByHaRjEQpg) - Introduction to SYCL from a session at IWOCL 2021.\n\n## Official Announcements\n\n## Events\n* [oneAPI Events](https://oneapi.io/events) - oneAPI events both past and future.\n\n## Community\n* [/r/sycl](https://www.reddit.com/r/sycl/) - SYCL subreddit\n* [/r/oneapi](https://www.reddit.com/r/oneapi/) - oneAPI subreddit\n* [Stackoverflow](https://stackoverflow.com/questions/tagged/intel-oneapi) - the latest questions on oneAPI on Stackoverflow\n\n## Books\n* [Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL](https://www.amazon.com/Data-Parallel-Mastering-Programming-Heterogeneous-ebook/dp/B08MKYHJ6R?ref_=ast_author_dp) - Learn how to accelerate C++ programs using data parallelism.\n* [Pro TBB: C++ Parallel Programming with Threading Building Blocks](https://www.amazon.com/Pro-TBB-Parallel-Programming-Threading-ebook/dp/B07V5YFCMV?ref_=ast_author_dp) - This open access book is a modern guide for all C++ programmers to learn Threading Building Blocks (TBB). Written by TBB and parallel programming experts.\n\n## Contributions\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/rahulunair%2Fawesome-oneapi/projects"}