As technology constantly strengthens its presence in all aspects of human life, computing systems integrate a high number of processing cores, whereas applications become more complex and greedy for computational resources. Inevitably, this high increase in processing elements combined with the unpredictable resource requirements of executed applications at design time impose new design constraints to resource management of many-core systems, turning the distributed functionality into a necessity. In this work, we present a distributed runtime resource management framework for many-core systems utilizing a network-on-chip (NoC) infrastructure. Specifically, we couple the concept of distributed management with parallel applications by assigning different roles to the available computing resources. The presented design is based on the idea of local controllers and managers, whereas an on-chip intercommunication scheme ensures decision distribution. The evaluation of the proposed framework was performed on an Intel Single-Chip Cloud Computer, an actual NoC-based, many-core system. Experimental results show that the proposed scheme manages to allocate resources efficiently at runtime, leading to gains of up to 30% in application execution latency compared to relevant state-of-the-art distributed resource management frameworks.
This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE's accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE's cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE's infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.
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