Ampehre: An Open Source Measurement Framework for Heterogeneous Compute Nodes - Lösch, Achim; Wiens, Alex; Platzner, Marco (Proceedings of the International Conference on Architecture of Computing Systems (ARCS))
Profiling applications on a heterogeneous compute node is challenging since the way to retrieve data from the resources and interpret them varies between resource types and manufacturers. This holds especially true for measuring the energy consumption. In this paper we present Ampehre, a novel open source measurement framework that allows developers to gather comparable measurements from heterogeneous compute nodes, e.g., nodes comprising CPU, GPU, and FPGA. We explain the architecture of Ampehre and detail the measurement process on the example of energy measurements on CPU and GPU. To characterize the probing effect, we quantitatively analyze the trade-off between the accuracy of measurements and the CPU load imposed by Ampehre. Based on this analysis, we are able to specify reasonable combinations of sampling periods for the different resource types of a compute node.

A Highly Accurate Energy Model for Task Execution on Heterogeneous Compute Nodes - Lösch, Achim; Platzner, Marco (2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP))

reMinMin: A Novel Static Energy-Centric List Scheduling Approach Based on Real Measurements - Lösch, Achim; Platzner, Marco (Proceedings of the 28th Annual IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP))
Heterogeneous compute nodes in form of CPUs with attached GPU and FPGA accelerators have strongly gained interested in the last years. Applications differ in their execution characteristics and can therefore benefit from such heterogeneous resources in terms of performance or energy consumption. While performance optimization has been the only goal for a long time, nowadays research is more and more focusing on techniques to minimize energy consumption due to rising electricity costs.This paper presents reMinMin, a novel static list scheduling approach for optimizing the total energy consumption for a set of tasks executed on a heterogeneous compute node. reMinMin bases on a new energy model that differentiates between static and dynamic energy components and covers effects of accelerator tasks on the host CPU. The required energy values are retrieved by measurements on the real computing system. In order to evaluate reMinMin, we compare it with two reference implementations on three task sets with different degrees of heterogeneity. In our experiments, MinMin is consistently better than a scheduler optimizing for dynamic energy only, which requires up to 19.43% more energy, and very close to optimal schedules.

Self-aware Compute Nodes - Agne, Andreas; Happe, Markus; Lösch, Achim; Plessl, Christian; Platzner, Marco (Self-aware Computing Systems)
Many modern compute nodes are heterogeneous multi-cores that integrate several CPU cores with fixed function or reconfigurable hardware cores. Such systems need to adapt task scheduling and mapping to optimise for performance and energy under varying workloads and, increasingly important, for thermal and fault management and are thus relevant targets for self-aware computing. In this chapter, we take up the generic reference architecture for designing self-aware and self-expressive computing systems and refine it for heterogeneous multi-cores. We present ReconOS, an architecture, programming model and execution environment for heterogeneous multi-cores, and show how the components of the reference architecture can be implemented on top of ReconOS. In particular, the unique feature of dynamic partial reconfiguration supports self-expression through starting and terminating reconfigurable hardware cores. We detail a case study that runs two applications on an architecture with one CPU and 12 reconfigurable hardware cores and present self-expression strategies for adapting under performance, temperature and even conflicting constraints. The case study demonstrates that the reference architecture as a model for self-aware computing is highly useful as it allows us to structure and simplify the design process, which will be essential for designing complex future compute nodes. Furthermore, ReconOS is used as a base technology for flexible protocol stacks in Chapter 10, an approach for self-aware computing at the networking level.

Performance-centric scheduling with task migration for a heterogeneous compute node in the data center - Lösch, Achim; Beisel, Tobias; Kenter, Tobias; Plessl, Christian; Platzner, Marco (Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE))
The use of heterogeneous computing resources, such as Graphic Processing Units or other specialized coprocessors, has become widespread in recent years because of their per- formance and energy efficiency advantages. Approaches for managing and scheduling tasks to heterogeneous resources are still subject to research. Although queuing systems have recently been extended to support accelerator resources, a general solution that manages heterogeneous resources at the operating system- level to exploit a global view of the system state is still missing.In this paper we present a user space scheduler that enables task scheduling and migration on heterogeneous processing resources in Linux. Using run queues for available resources we perform scheduling decisions based on the system state and on task characterization from earlier measurements. With a pro- gramming pattern that supports the integration of checkpoints into applications, we preempt tasks and migrate them between three very different compute resources. Considering static and dynamic workload scenarios, we show that this approach can gain up to 17% performance, on average 7%, by effectively avoiding idle resources. We demonstrate that a work-conserving strategy without migration is no suitable alternative.

Self-awareness as a Model for Designing and Operating Heterogeneous Multicores - Agne, Andreas; Happe, Markus; Lösch, Achim; Plessl, Christian; Platzner, Marco (ACM Transactions on Reconfigurable Technology and Systems (TRETS))
Self-aware computing is a paradigm for structuring and simplifying the design and operation of computing systems that face unprecedented levels of system dynamics and thus require novel forms of adaptivity. The generality of the paradigm makes it applicable to many types of computing systems and, previously, researchers started to introduce concepts of self-awareness to multicore architectures. In our work we build on a recent reference architectural framework as a model for self-aware computing and instantiate it for an FPGA-based heterogeneous multicore running the ReconOS reconfigurable architecture and operating system. After presenting the model for self-aware computing and ReconOS, we demonstrate with a case study how a multicore application built on the principle of self-awareness, autonomously adapts to changes in the workload and system state. Our work shows that the reference architectural framework as a model for self-aware computing can be practically applied and allows us to structure and simplify the design process, which is essential for designing complex future computing systems.