Search Results for "multicore"
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Showing 44 open source projects for "multicore"
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1
future
R package: future: Unified Parallel and Distributed Processing in R
...It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc. Automatic detection and export of global objects and functions needed by future expressions, so the user doesn’t need to manage that manually. ... -
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SteelMC
A Minecraft server in Rust focused on a strong foundation
SteelMC is an independent Minecraft Java Edition server implementation written in Rust. It aims to reproduce vanilla behavior while using modern multicore processors more efficiently. Gameplay updates remain synchronous, while chunk generation, lighting, packet processing, and chunk transmission can run outside the main server tick. Players can join persistent multiplayer worlds, move, interact with blocks, manage inventories, use commands, chat, and return to previously saved areas. ... -
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
...Note the log scale on both the points and time axis, and that the scale of the x-axis is in thousands of points (thus, the values on the x-axis range from 1K to 10M points. Dashed lines on SkLearn, BH-TSNE, and MULTICORE-4 represent projected times. Projected scaling assumes an O(nlog(n)) implementation. -
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OpenCL.jl
OpenCL Julia bindings
...This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to PyOpenCL for Python. It provides a high level API for OpenCL to make programing hardware accelerators, such as GPUs, FPGAs, and DSPs, as well as multicore CPUs much less onerous. -
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Codon
A high-performance, zero-overhead, extensible Python compiler
Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 100x or more, on a single thread. Codon supports native multithreading which can lead to speedups many times higher still. The Codon framework is fully modular and extensible, allowing for the seamless integration of new modules, compiler optimizations, domain-specific languages and so on. We actively develop Codon... -
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gensim
Topic Modelling for Humans
Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is the natural language processing (NLP) and information retrieval (IR) community. -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. HeavyDB was originally developed as part of the OmniSci platform (formerly MapD) and is commonly used for large-scale analytics and geospatial data processing. ... -
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MongoDB Rust Driver
The official MongoDB Rust Driver
...Because it’s asynchronous by design, it works well with Rust async runtimes like Tokio and async-std, letting developers build highly concurrent networked services that efficiently use modern multicore hardware. The crate also includes BSON encoding and decoding that maps cleanly to Rust types, so developers can work with rich document structures while retaining Rust’s performance guarantees. -
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Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
Simple source files and compiled JAR Java programs, for benchmark 50 000 000 cycle finding prime numbers. On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). Surprise, same code in JavaScript for M2 chip in Safari: 12,5 sec single core and 3,3 sec multi core. Python version with numba and numpy on MacOS with M2: 3,78 sec, Intel Ultra 5 225F Linux Fedora 43 GNOME(*Intel): 3,64 sec., W11Intel: 3,73; Faster style in python, MacOS M2: 1,81 sec, *Intel & W11Intel: 2,02 sec.; Ultra faster style in python, MacOS M2: 1,24 s - 1,26 s - 1,34 s, *Intel: 1,48 s - 1,50 s, W11Intel: 1,53 - 1,63. -
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LDPartEditor
Create and edit LDraw™ parts quick and easy.
...Minimum System Requirements: - OpenGL 2.1 compatible Graphics Card - Operating System (64-bit): Windows [7 or newer], Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor e.g. Intel Core 2 Duo or AMD Athlon II (>2.0Ghz) - RAM: 4 GB - Video-Memory: 1 GB - Free Disk Space: 150 MB Recommended Requirements: - OpenGL 3.3 compatible Graphics Card - Operating System (64-bit): Windows 7,8,10,11, Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor with 4 cores (or more) - RAM: >4 GB - Video-Memory: >1 GB - Free Disk Space: 512 MB The sourcecode is now hosted on GitHub here: http://nilsschmidt1337.github.io/ldparteditor/ -
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ZeroMQ
ZeroMQ core engine in C++, implements ZMTP/3.1
...You can connect sockets N-to-N with patterns like fan-out, pub-sub, task distribution, and request-reply. It's fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built as asynchronous message-processing tasks. It has a score of language APIs and runs on most operating systems. Libzmq is mainly written in C++98 with some optional C++11-fragments. For configuration either autotools or CMake is employed. For Linux users, pre-built binary packages are available for most distributions. ... -
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Thrust
The C++ parallel algorithms library
Thrust is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. It builds on top of established parallel programming frameworks (such as CUDA, TBB, and OpenMP). It also provides a number of general-purpose facilities similar to those found in the C++ Standard Library. The NVIDIA C++ Standard Library is an open-source project; it is available on GitHub and included in the NVIDIA HPC SDK and CUDA Toolkit. ... -
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Accelerate
Embedded language for high-performance array computations
...Accelerate is a free, general-purpose, open-source library that simplifies the process of developing software that targets massively parallel architectures including multicore CPUs and GPUs. Embedded in the advanced functional programming language Haskell, Accelerate programs are declarative, statically-typed, pure, functional, and ready to exploit all of the performance of modern parallel hardware. The combination of a strong type system, high-level code, and interactive development environment, allows you to develop code quickly with the confidence that it is correct. -
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Pharos
RTOS for Secure, Safe and Real-Time Systems
Pharos is a free open-source RTOS for secure, safe and real-time systems with the following characteristics: - Memory and enhanced time partitioning (TSP) - Native support for sporadic, periodic and aperiodic threads - Fixed-priority preemptive scheduler - Execution time protection (threads are prevented from executing for more time than they are configured to) - Small size (full image has ~100 to 300KiB text, ~30KiB data) - Hard real-time determinism - Fast critical sections - Prioritized nested interrupts - inter-partition communication with filters to protect unwanted access - Multicore support (RMP) - Supports ARM9, Cortex-A/R/M and Aarch64, RISC-V64 (64 bits) - The complete TestSuite (see below information) is run successfully for each platform in each release - Tested: more than 170 KLOC of requirement-based tests, executed for each board at each release - Up-to-date User Manual also available See our wiki page for more information. -
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Tiramisu
Polyhedral compiler for expressing fast and portable data algorithms
...The Tiramisu compiler is based on the polyhedral model thus it can express a large set of loop optimizations and data layout transformations. Currently, it targets (1) multicore X86 CPUs, (2) Nvidia GPUs, (3) Xilinx FPGAs (Vivado HLS) and (4) distributed machines (using MPI). It is designed to enable easy integration of code generators for new architectures. -
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Mini Psiphon Launcher with auto Reconnect in ~Mb. You can combine it with SSH, Proxifier and other to tunnel your connection or use own forwarder. #New Version: V-1.4
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DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
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PREESM provides a complete framework for fast prototyping. It generates a simulation and an optimized multiprocessor code from graph descriptions of algorithm and architecture. Please visit the project homepage at http://preesm.sourceforge.net.
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CRUSH
Data reduction and imaging for select astronomical cameras
CRUSH is a pioneering reduction and imaging suite for many astronomical cameras operating at far-infrared, submillimeter and millimeter wavelengths, such as SHARC-2, SCUBA-2, LABOCA, GISMO, or SOFIA/HAWC+ (to name a few). -
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JavaCá&Lá
A java middleware for HPC and IoT
The Java Cá&Lá (or just JCL - www.javacaela.org) is a middleware that integrates high performance computing (HPC) and Internet of things (IoT) in a unique API. It enables asynchronous remote method invocation and distributed shared memory services over multicore computer architectures or over multicomputer clusters. Sensing services are also feasible in JCL IoT version. An API for internal control enables collecting several times and memory usage of tasks submitted to the JCL cluster. Finally, a distributed map implementation adopts de Java Map interface, a common data structure for Java developers. ... -
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Chapel
a Productive Parallel Programming Language
Chapel is an emerging parallel programming language whose design and development are being led by HPE in collaboration with academia, computing labs, and industry. Chapel's goal is to improve the productivity of parallel programmers, from laptops to supercomputers. **Please note that Chapel development has moved to GitHub** -
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Leaf
A game server framework in Go (golang)
Leaf, written in Go, is a open-source game server framework aiming to boost efficiency both in development and runtime. Simple APIs. Leaf tends to provide simple and plain interfaces which are always best for use. Self-healing. Leaf always tries to salvage the process from runtime errors instead of leaving it to crash. Multi-core support. Leaf utilize its modules and leaf/go to make use of CPU resouces at maximum while avoiding varieties of side effects may be caused. Each module runs inside... -
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JSensor
high performance computing, Wireless Sensors Networks Simulators
...JSensor is a sensor network simulator implemented at Computer Science Department of Federal University of Ouro Preto (DECOM-UFOP), Brazil. In contrast to JSensor counterparts, it is designed to simulate millions of sensors in multicore and, in near future, multicomputer architectures. -
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JavaCá&Lá
A Java Distributed Shared Memory Middleware
How can we easily develop applications for multicore or multicomputer architectures? We frequently use middlewares for that problem, but they are often complex and less productive. This way, at DECOM-UFOP, we have a middleware, named JavaCa&La or just JCL, to build portable and scalable solutions over many high performance computer architectures. Our middleware APIs is simple, intuitive and designed for young and senior programmers who don’t have high performance programming skills. ... -
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MuffinEC
Multi-technology, INDEL aware error correction for NGS data
...It officially supports four technologies (Illumina, 454, ion Torrent and PacBio - experimental) and it also has a generic setup for others (old and/or new). It is released under LGPL version 3.0. MuffinEC can use multicore systems, thanks to its OpenMP implementation. We are developing the 2nd version of MuffinEC. The beta version is already available online. The changes are listed in the VERSION_HISTORY file. Please report any problem you may encounter at the email address specified in the README file. We really appreciate your help.
