Search Results for "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.

...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/

...Also, sequences in GenBank format can be downloaded directly from NCBI using the NCBI access ID. Requirements JAVA Runtime 8. It's highly recommended to have a multicore processor to process large sequences. * Currently, this version only runs in Windows x64.

...This tool allows bioinformatics to avoid the analysis of not necessary reads, reducing the time of subsequent procedures with the dataset. MarDRe is the Big Data counterpart of ParDRe (link above), which employs HPC technologies (i.e., hybrid MPI/multithreading) to reduce runtime on multicore systems. Instead, MarDRe takes advantage of the MapReduce programming model to significantly improve ParDRe performance on distributed systems, especially on cloud-based infrastructures. Written in pure Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for Big Data processing.

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.

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).

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. ...

An Open Source Java Software for Visual-Artists like VJs written in Java. Using xuggle as video decoder, we are able to read most popular videocodecs. Supports iGuard IP-250E IP/Network Camera. With MJPEG-Stream-Server for multiple clients like VLC. GPU support with OpenCL and jogamp.

Modern software systems and application are commonly written in multiple languages, include scripting engines, and are frequently build on multiple specialized frameworks and middleware for a considerable diversity of runtime environments. The latest influencing update in development paradigm is the application of multicore processors. This projects is aimed to unify the required trace and logging output and integrate into debugging environments. The target is to provide general development, test, and production support of software environments based on multiple programming languages for distributed multicore environments.

Cacoj is a Creol language tool set to deploy active concurrent objects onto multicore technology. It provides a Creol to Java compiler to map asynchronous concurrent model of Creol into Java concurrent facilities.

GridABM is an easy-to-use toolkit to develop high-performance agent-based models (ABMs) in Repast. GridABM facilitates large-scale simulation of ABM on multicore computers, local computing clusters, or computational grids.

The intention of this project is to develop a library and framework which enables Java applications to use multicore processors. Several Ideas to help developers are as follows: Parallel For Work-Stealing Profiler for Parallel Applications ...

A .Net Library that lets you make full use of multi-core and multiprocessor computers. It is very efficient, and easily integrated into existing applications. It is modeled after the Parallel FX usage model, and is faster than the PFX too.