This paper studies the problem of scheduling a set of hard real-time sporadic tasks that may access CPU cores and a shared resource. Motivated by the observation that the CPU resource is often abundant compared to the shared resources in multi-core and many-core systems, we propose to resolve this problem from a counter-intuitive shared-resource-centric perspective, focusing on judiciously prioritizing and scheduling tasks’ requests in a limited preemptive manner on the shared resource while viewing the worst-case latency a task may experience on the CPU cores as suspension delays. We develop a rather comprehensive set of task partitioning algorithms that partition tasks onto the shared resource with the objective of guaranteeing schedulability while minimizing the required size of the shared resource, which plays a critical role in reducing the overall cost and complexity of building resource-constrained embedded systems in many application domains. A GPU-based prototype case study and extensive simulation-based experiments have been conducted, which validate both our shared-resource-centric scheduling philosophy and the efficiency of our suspension-based partitioning solutions in practice.