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Implementing Parallel Scalable Distribution Counting Algorithm (DCA) with CUDA 8.0 Runtime API

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9 Dec 2016CPOL24 min read
In this article, we'll demonstrate an approach the allows to increase the performance (up to 600%) of the code that implements the conventional distribution counting algorithm (DCA) using NVIDIA CUDA 8.0 Runtime API

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Author

Arthur V. Ratz
Software Developer (Senior) EpsilonDev
Ukraine Ukraine
I’m software developer, system analyst and network engineer, with over 20 years experience, graduated from L’viv State Polytechnic University and earned my computer science and information technology master’s degree in January 2004. My professional career began as a financial and accounting software developer in EpsilonDev company, located at L’viv, Ukraine. My favorite programming languages - C/C++, C#.NET, Java, ASP.NET, Node.js/JavaScript, PHP, Perl, Python, SQL, HTML5, etc. While developing applications, I basically use various of IDE’s and development tools, including Microsoft Visual Studio/Code, Eclipse IDE for Linux, IntelliJ/IDEA for writing code in Java. My professional interests basically include data processing and analysis algorithms, artificial intelligence and data mining, system analysis, modern high-performance computing (HPC), development of client-server web-applications using various of libraries, frameworks and tools. I’m also interested in cloud-computing, system security audit, IoT, networking architecture design, hardware engineering, technical writing, etc. Besides of software development, I also admire to write and compose technical articles, walkthroughs and reviews about the new IT- technological trends and industrial content. I published my first article at CodeProject in June 2015.

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