Lock-free programming patterns

Namiot Dmitry. On lock-free programming patterns. WSEAS Transactions on Computers, 15:117–124, 2016.

Lock-free programming is a well-known technique for multithreaded programming. Lock-free programming is a way to share changing data among several threads without paying the cost of acquiring and releasing locks. On practice, parallel programming models must include scalable concurrent algorithms and patterns. Lock-free programming patterns play an important role in scalability. This paper is devoted to lockfree data structures and algorithms. Our task was to choose the data structures for the concurrent garbage collector. We aim to provide a survey of lock-free patterns and approaches, estimate the potential performance gain for lock-free solutions. By our opinion, the most challenging problem for concurrent programming is the coordination and data flow organizing, rather than relatively low-level data structures. So, as the most promising from the practical point of view lock-free programming pattern, we choose the framework based on the Software Transactional Memory.

From WSEAS page

More about LSTM

A new set of papers aboit LSTM networks:

Teaching machines to process text
A Beginner’s Guide to Recurrent Networks and LSTMs

See also the previous post about Long Short-Term Memory-Networks for Machine Reading

Data Science interview

On hiring Data Science staff

The Physical Web

Our new paper in arxiv: Manfred Sneps-Sneppe, Dmitry Namiot "On Physical Web models"

The Physical Web is a generic term describes interconnection of physical objects and web. The Physical Web lets present physical objects in a web. There are different ways to do that and we will discuss them in our paper. Usually, the web presentation for a physical object could be implemented with the help of mobile devices. The basic idea behind the Physical Web is to navigate and control physical objects in the world surrounding mobile devices with the help of web technologies. Of course, there are different ways to identify and enumerate physical objects. In this paper, we describe the existing models as well as related challenges. In our analysis, we will target objects enumeration and navigation as well as data retrieving and programming for the Physical Web.

NIPS 2015: Deep Learning

NIPS’2015 Tutorial by Geoff Hinton, Yoshua Bengio & Yann LeCun: video & slides