Projects sharing features with Libcudacxx

Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio

Numba is a just-in-time compiler that translates high-level Python functions into optimized machine code at runtime. By leveraging the LLVM compiler infrastructure, it provides a framework for accelerating numerical data processing and mathematical computations, enabling performance levels comparable to statically compiled languages. The project distinguishes itself through its ability to perform type-inference-based specialization, which generates machine instructions tailored to the specific data types used during execution. It employs a lazy compilation pipeline that defers translation unt

Thrust is a heterogeneous computing library and C++ template library that provides a collection of high-level templates for executing data-parallel operations. It functions as a parallel algorithms library designed to work across different hardware backends, including multicore CPUs and NVIDIA GPU hardware. The framework utilizes a header-only implementation and a generic-programming policy interface to abstract the differences between CPU and GPU memory and execution models. It employs an iterator-based data abstraction to provide a uniform interface for accessing elements across host RAM an

This project is a structured Rust programming curriculum and systems programming course designed to take learners from beginner to expert levels. It provides a comprehensive set of training materials focused on mastering the core syntax, idioms, and technical foundations of the Rust language. The project features a specialized language transition framework that maps concepts from C++, managed languages, and dynamic typing to Rust idioms. This allows developers from different ecosystems to translate architectural patterns and memory models into idiomatic Rust. The training covers a broad rang

Cancellation propagation library for Common Lisp with deadlines and timeouts

A C++ GPU Computing Library for OpenCL

A header-only C++ library for task concurrency

Reactive computation tree library for non-blocking concurrent Common Lisp

Common Lisp distributed computation framework implemented using Swank Client

Async++ concurrency framework for C++11

Powerful multi-threaded coroutine dispatcher and parallel execution engine

The project alpaka has moved to https://github.com/alpaka-group/cupla

Event loop friendly C++ actor micro-framework, supervisable

The libdispatch Project, (a.k.a. Grand Central Dispatch), for concurrency on multicore hardware

Concurrency primitives, safe memory reclamation mechanisms and non-blocking (including lock-free) data structures designed to aid in the research, design and implementation of high performance concurrent systems developed in C99+.

A bleeding-edge, lock-free, wait-free, continuation-stealing tasking library built on C++20's coroutines

High performance Transactional Memory for Common Lisp

The project alpaka has moved to https://github.com/alpaka-group/alpaka

This project is a single-producer single-consumer concurrent queue for C++ designed for lock-free data exchange between threads. It provides a thread-safe mechanism to transfer data without the use of mutexes or locks. The queue is implemented as a contiguous circular buffer that supports dynamic capacity growth to prevent data loss when the queue reaches its limit. It utilizes atomic synchronization and wait-free index management to coordinate data access between the writing and reading threads. The library covers inter-thread communication and buffer management, offering both blocking and

Modern concurrency for C++. Tasks, executors, timers and C++20 coroutines to rule them all

A full-featured NATS messaging client for Common Lisp.

Fine-grained concurrency for Ruby

EventMachine is a reactor-pattern network framework for Ruby that provides an asynchronous I/O library for performing non-blocking network and file operations. It functions as a network server framework used to build scalable TCP and UDP servers and clients that process multiple simultaneous requests. The framework implements a concurrency model that dispatches network events to registered handlers using a single-threaded event loop. This approach allows for the management of high-concurrency network connections without the overhead of multi-threaded programming. The library covers the devel

Thin, unified, C++-flavored wrappers for the CUDA APIs

ConcurrentQueue is a header-only C++ template library that provides a lock-free data structure for multi-producer multi-consumer thread communication. It functions as a synchronization primitive designed to coordinate data flow between concurrent execution units using atomic operations rather than traditional mutex locking. The library distinguishes itself through a design that minimizes contention and synchronization overhead. It utilizes sub-queue token mapping to distribute workloads across partitioned internal queues and supports bulk operations to transfer multiple data elements in singl