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The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated

Single-ISA heterogeneous multi-core processors trade-off power with performance; however, threads that co-\ud run on shared resources suffer from resource contention, which induces performance degradation and energy\ud inefficiency. The authors introduce a novel approach to optimise the co-scheduling of multi-threaded applications on\ud heterogeneous processors. The approach is based on the concept of stakes function, which represents the trade-off\ud between isolation and sharing of resources. The authors also develop a co-scheduling algorithm that use stakes\ud functions to optimise resource usage while mitigating resource contention, thus improving performance and energy\ud efficiency. They validated the approach using applications from the Princeton Application Repository for Shared-\ud Memory Computers (PARSEC) benchmark suite, obtaining up to 12.88% performance speed-up, 13.65% energy speed-\ud up and 28.29% energy delay speed-up with respect to the standard Linux heterogeneous multi-processing scheduler

Nowadays Heterogeneous System Architectures (HSAs) are becoming very attractive in the embedded and mobile markets thanks to the possibility to select the best computational resource among the available compute units to optimize the performance per Watt figure of merit. In this scenario, OpenCL is becoming the standard paradigm for heterogeneous computing supporting the programming of all types of units with a single abstraction level. However, the decision of the resource to use together with its architectural tuning is still left to the programmer; this issue is even more exacerbated when considering the fact that the choice depends also on the actual conditions in which the system is operating. This work aims at proposing a runtime controller, integrated in Linux Operating System (OS), for optimizing the power efficiency of a running OpenCL application deciding the system configuration. Our experimental results over a set of applications from the Polybench suite on the Odroid XU3 board show that our controller is able to obtain a power efficiency of more than 90% of the one achievable via offline profiling.

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