StarPU is a open-source software framework that enables the optimization of scientific computing applications for heterogeneous architectures. It provides a simple and efficient way to parallelize compute-intensive tasks and manage resources, resulting in improved performance and reduced execution time.

The `ask` project is an open-source tool that provides support for scientific workflows, with a focus on simulation and optimization. It is built using OpenMOLE, a scientific workflow platform that enables the execution of complex simulations and optimization algorithms on various computing resources.

The oneAPI-intel project is a unified programming model that allows developers to build high-performance applications for various architectures, including CPUs, GPUs, and FPGAs. It provides a set of libraries, tools, and extensions for optimizing computing-intensive workloads, such as AI, data analytics, and scientific simulations.

The ust4hpc-2023 project is a tool for scientific research that optimizes high performance computing resources, allowing for efficient simulation and modeling of complex scientific calculations. It also provides robust management of large data sets, enabling researchers to easily access and analyze their data.

This project focuses on optimizing matrix multiplication using static algorithms on multicores and GPUs, aiming to improve performance and efficiency in scientific computing and large-scale matrix computations.

The cmake_external_blas project provides a way to externalize BLAS (Basic Linear Algebra Subprograms) libraries during the cmake build process, allowing for better customization and optimization of numerical computations in scientific computing applications.

The starpu_example_stencil project is a computational framework designed to solve partial differential equations and perform numerical simulations. It leverages the StarPU scheduling and task management library to optimize scientific computations, enabling faster and more efficient simulations.

This project provides an example program that demonstrates the integration of OpenMP with the GNU Scientific Library (GSL) for parallelizing numerical computations. It is intended to serve as a reference for developers seeking to optimize their scientific codes using OpenMP and GSL.

The oneapi-nvidia project focuses on optimizing deep learning models for NVIDIA GPUs, developing high-performance computing applications, and accelerating scientific simulations using NVIDIA hardware. It provides tools and libraries for developers to leverage the power of NVIDIA GPUs and improve the performance of their data-intensive workloads.

This project is a benchmark plugin for the ICY application, designed to optimize scientific workflows, evaluate the performance of algorithms, and analyze data processing pipelines. It provides a set of methods to measure and compare the performance of different computational tasks.