Automatic parallelism for C++ code

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Hi, We are a few software developers and researchers working on automatic parallelism for C++ code in Scotland. https://www.paraformance.com/ We have two plugins, one for Eclipse and another for Visual Studio. We are supporting various operating systems (e.g. Windows 8, 10, OSX, Ubuntu, Fedora, etc.). We can give command line application but you have to email us. We support features like, finding hotspots for parallelism, detecting race conditions and dead locks as well as suggesting and repairing where possible, and refactoring sequential code to parallel code. https://www.paraformance.com/videos.html We have some applications where we tried our code https://github.com/Paraformance/example-use-cases . You can signup for a trial at https://www.paraformance.com/try-it-for-free.html Or you can directly download the plugins from the marketplace : Visual Studio : https://marketplace.visualstudio.com/items?itemName=vs-publi... Eclipse: https://marketplace.eclipse.org/content/paraformance You can also contact us at for a demo or even just for command line application: chris (at) paraformance (dot) com enquiries (at) paraformance (dot) com dib (at) paraformance (dot) com

Visit product View on Hacker NewsMar 1, 2018

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: email, visual, code · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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