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Application specific MPSoCs are often used to implement high-performance data-intensive applications. MPSoC design requires a rapid and efficient exploration of the hardware architecture possibilities to adequately orchestrate the data distribution and architecture of parallel MPSoC computing resources. Behavioral specifications of data-intensive applications are usually given in the form of a loop-based sequential code, which requires parallelization and task scheduling for an efficient MPSoC implementation. Existing approaches in application specific hardware synthesis, use loop transformations to efficiently parallelize single nested loops and use Synchronous Data Flows to statically schedule and balance the data production and consumption of multiple communicating loops. This creates a separation between data and task parallelism analyses, which can reduce the possibilities for throughput optimization in high-performance data-intensive applications. This paper proposes a method for a concurrent exploration of data and task parallelism when using loop transformations to optimize data transfer and storage mechanisms for both single and multiple communicating nested loops. This method provides orchestrated application specific decisions on communication architecture, memory hierarchy and computing resource parallelism. It is computationally efficient and produces high-performance architectures.