HPIC LAB

Heterogeneous Distributed Computing (HDC) is a research area that utilizes different types of computing resources (CPUs, GPUs, FPGAs, etc.) to efficiently process tasks in a distributed environment. In modern computing environments, with hardware increasingly optimized for specific tasks, it has become essential to learn how to effectively leverage these heterogeneous resources to maximize computational performance, increase resource utilization, and improve power efficiency. The goal of the HDC is to research new approaches for scheduling and load-balancing, optimizing energy efficiency, resource scalability, and parallel algorithms for heterogeneous architectures. Heterogeneous Multi-core systems have cores which are not identical in terms of micro-architecture, clock frequency, cache size, etc, whereas homogeneous multi-core systems include only identical cores. Heterogeneous Multi-core systems have cores which are not identical in terms of micro-architecture, clock frequency, cache size, etc, whereas homogeneous multi-core systems include only identical cores. Current operating systems are not designed to handle heterogeneous multi-core processor. Especially, it is difficult to exploit the heterogeneity/affinity of resources and tasks. To fully exploit heterogeneous multi-core systems, intelligent scheduling of tasks becomes one of the critical issues. Robots have significant advantages over humans at performing tasks in extreme environments such as object collection in disaster sites. In such cases terrain of the disaster site is very uneven and can cost differently for different robots to travel over. Also in many cases, robots are required to move to multiple locations and perform multiple different tasks. In these cases robots performing the task to be composed