Future integrated systems will contain billions of transistors, composing tens to hundreds of IP cores. Modern computing platforms take advantage of this manufacturing technology advancement and are moving from Multi-Processor Systems-on-Chip (MPSoC) towards Many-Core architectures employing high numbers of processing cores. These hardware changes are also driven by application changes. The main characteristic of modern applications is the increased parallelism and the need for data storage and transfer. Resource management is a key technology for the successful use of such many-core platforms. The thread to core mapping can deal with the run-time dynamics of applications and platforms. Thus, the efficient resource management enables the efficient usage of the platform resources. maximizing platform utilization, minimizing interconnection network communication load and energy budget. In this thesis, we present a performance-aware resource management scheme for many- core architectures. Particular, the developed framework takes as input parallel applications and performs an application profiling. Based on that profile information, a thread to core mapping algorithm finds (i) the appropriate number of threads that this application will have in order to maximize the utilization of the system and (ii) the best mapping for maximizing the performance of the application under the selected number of threads. In order to validate the proposed algorithm, we used and extended the Sniper, state-of-art, many-core simulator. Last, we developed a discrete event simulator, on top of Sniper simulator, in order to test and validate multiple scenarios faster. The results show that the the proposed methodology, achieves on average a gain of 23% compared to a performance oriented mapping presented and each application completes its workload 18% faster on average.