Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU

A Simulation Framework for Scheduling Performance Evaluation on CPU-GPU Heterogeneous System

Modern PCs are equipped with multi-many core capabilities which enhance their computational power and address important issues related to the efficiency of the scheduling processes of the modern operating system in such hybrid architectures. The aim of our work is to implement a simulation framework devoted to the study of the scheduling process in hybrid systems in order to improve the system ...

Runtime Performance Evaluation of GPU and CPU using a Genetic Algorithm Based on Neighborhood Model

Bio-inspired techniques like Genetic Algorithms have a comprehensive applicability to optimization problems. Given the ease of parallelism implementation inherent of these techniques several researches have been developed in such area making use of parallel platforms, especially the CUDA platform. However, the majority of these works are focused on strategies to improve the algorithms convergen...

Fast bio-inspired computation using a GPU-based systemic computer

Biology is inherently parallel. Models of biological systems and bio-inspired algorithms also share this parallelism, although most are simulated on serial computers. Previous work created the systemic computer – a new model of computation designed to exploit many natural properties observed in biological systems, including parallelism. The approach has been proven through two existing implemen...

Runtime specialization for heterogeneous CPU-GPU platforms

Cache optimization for CPU - GPU heterogeneous processors ∗

Microprocessors combining CPU and GPU cores using a common last-level cache pose new challenges to cache management algorithms. Since GPU cores feature much higher data access rates than CPU cores, the majority of the available cache space will be used by GPU applications, leaving only very limited cache capacity for CPU applications, which may be disadvantageous for overall system performance....