In this paper an ARM big.LITTLE system with Global Task Scheduling is evaluated in terms of its energy efficiency and performance measured in execution time. The big.LITTLE system is evaluated against the same system but with only the big or LITTLE processor active. The evaluation is done by performing experiments that target three different levels of load: full load, varying load and low load. The benchmarking software PARSEC Blackscholes and BBench are used to put the system under a synthetic workload in the tests. The results show that overall big.LITTLE achieves an improvement in execution time for all test scenarios, although very slim for varying load, and is more energy efficient than the big processor with the possible exception of a low load scenario. However, the LITTLE processor by itself is found to be the most energy efficient system even though it showed the slowest execution time.

Accurate virtual platforms allow for crucial, early, and inexpensive assessments about the viability and hardware constraints of software/hardware applications. The growth of multicore architectures in both number of cores and relevance in the industry, in turn, demands the emergence of faster and more efficient virtual platforms to make the benefits of single core simulation and emulation available to their multicore successors whilst maintaining accuracy, development costs, time, and efficiency at acceptable levels. The goal of this thesis is to find optimal virtual platforms to perform hardware design space exploration for multi-core architectures running filtering functions, particularly, a discrete signal filtering Matlab algorithm used for oil surveying applications running on an ARM Cortex-A53 quadcore CPU. In addition to the filtering algorithm, the PARSEC benchmark suite was also used to test platform compliance under workloads with diverse characteristics. Upon reviewing multiple virtual platforms, the gem5 simulator and the QEMU emulator were chosen to be tested due to their ubiquitousness, prominence and flexibility. A Raspberry Pi Model B was used as reference to measure how closely these tools can model a commonly used embedded platform. The results show that each of the virtual platforms is best suited for different scenarios. The QEMU emulator with KVM support yielded the best performance, albeit requiring access to a host with the same architecture as the target, and not guaranteeing timing accuracy. The most accurate setup was the gem5 simulator using a simplified cache system and an Out-of-Order detailed ARM CPU model.

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