Scalable and Distributed Resource Management for Many-Core Systems

Many-core systems provide researchers with important new challenges, including the handling of very dynamic and hardly predictable computational loads. The large number of applications and cores causes scalability issues for centrally acting heuristics, which always must retain a global view of the entire system. Resource management itself can become a bottleneck which limits the achievable performance of the system. The focus of this work is to achieve scalability of resource management

Resource allocation schemes for gang scheduling

Gang scheduling is currently the most popular scheduling scheme for parallel processing in a time shared environment. In this paper we first describe the ideas of job re-packing and workload tree for efficiently allocating resources to enhance the performance of gang scheduling. We then present some experimental results obtained by implementing four different resource allocation schemes. These results show how the ideas, such as re-packing jobs, running jobs in multiple slots and minimising the average number of time slots in the system, affect system and job performance when incorporated into the buddy based allocation scheme for gang scheduling.