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Navigation: Home
\ Development
\ Compilers & Interpreters \ ScalaLabLight
ScalaLabLight2.11 by Stergios Papadimitriou |
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Software Description:
... the Scala programming language that enables you to write scientific code. It intends to provide you with an integrated working environment similar with Matlab. ...
type: Open Source
Download ScalaLabLightcategories: development tool, programming environment, code compiler, compile, programmer, developer Add to Download Basket Report virus or spyware |
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| Software Info | |
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Best Vista Download periodically updates pricing and software information of ScalaLabLight full version from the publisher,
but some information may be out-of-date. You should confirm all information. Software piracy is theft, using crack, warez passwords, patches, serial numbers, registration codes, key generator, keymaker or keygen for ScalaLabLight license key is illegal and prevent future development of ScalaLabLight. Download links are directly from our mirrors or publisher's website, ScalaLabLight torrent files or shared files from rapidshare, yousendit or megaupload are not allowed! |
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| Released: | January 30, 2014 |
| Filesize: | 128.00 MB |
| Platform: | Windows XP, Windows Vista, Windows Vista x64, Windows 7, Windows 7 x64, Windows 8, Windows 8 x64, Windows 10, Windows 10 x64 |
| Install | Instal And Uninstall |
| Add Your Review or Windows Vista Compatibility Report |
ScalaLabLight- Releases History |
| Software: | ScalaLabLight 2.11 |
| Date Released: | Jan 30, 2014 |
| Status: | New Release |
| Release Notes: | minor improvements |
| Software: | ScalaLabLight 211 |
| Date Released: | Nov 5, 2013 |
| Status: | New Release |
| Release Notes: | Support for CUDAMat matrix type, that aims to facilitate operations exploiting the CUDA parallel computing framework. |
| Software: | ScalaLabLight 211 Multithr |
| Date Released: | Oct 22, 2013 |
| Status: | New Release |
| Release Notes: | Multithreading is used automatically at the multiplication of large Array[Array[Double]] and RichDouble2DArrays. For small matrices serial implementation is used. However, multithreading does not improve significantly the addition/subtraction of large matrices (for small ones, the execution time becomes worse). Therefore, we use multithreading only for multiplication. |
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