This paper presents VCMalloc, a custom memory allocator based on a new dynamic memory management approach that keeps data allocated in a contiguous form in memory. It can preserve the virtual contiguity of data even after performing different memory operations like “Reallocation” and “Free” without consuming much system resources. A novel control plane that we call a “Memory Management System” is introduced with a set of algorithms for the physical-virtual memory mappings, assuring fast, efficient, and safe tracking of data and user pointers. The proposed allocator prioritizes virtual contiguity while it keeps the physical memory completely up to the operating system to manage. It was tested and compared with the current implementation of Malloc as well as a state-of-the-art memory allocator MIMalloc in the recent versions of the Microsoft Windows operating system. For the allocator itself, a considerable performance increase has been achieved in basic operations of up to 28% increase in allocation speed and 26% in reallocation speed. The data structures allocated by VCMalloc have shown remarkable performance increases of up to 31% faster for matrix multiplication. Furthermore, VCMalloc has also demonstrated its superiority in real-world SPEC CPU 2017 benchmark applications.

A recently developed sparse representation algorithm, has been proved to be useful for multi‐object tracking and this study is a proposal for developing its parallelisation. An online dictionary learning is used for object recognition. After detection, each moving object is represented by a descriptor containing its appearance features and its position feature. Any detected object is classified and indexed according to the sparse solution obtained by an orthogonal matching pursuit (OMP) algorithm. For a real‐time tracking, the visual information needs to be processed very fast without reducing the results accuracy. However, both the large size of the descriptor and the growth of the dictionary after each detection, slow down the system process. In this work, a novel accelerating OMP algorithm implementation on a graphics processing unit is proposed. Experimental results demonstrate the efficiency of the parallel implementation of the used algorithm by significantly reducing the computation time.