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Achim Losch
University of Paderborn
6Publications
2H-index
23Citations
Publications 6
Newest
#1Achim Losch (University of Paderborn)H-Index: 2
#2Marco Platzner (University of Paderborn)H-Index: 25
Heterogeneous computing with CPUs, GPUs, and FPGAs has strongly gained interest in the last years. While scheduling and optimization problems for runtime have been widely studied, optimizing for energy-related metrics has become an emerging topic only recently due to rising electricity costs and the difficulties of thermal management. Energy-optimizing schedulers need to predict the effect of single task-resource assignment decisions on the consumed energy as well as the energy consumptions for ...
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#1Achim Losch (University of Paderborn)H-Index: 2
#2Alex Wiens (University of Paderborn)H-Index: 2
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 3 authors...
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 Ampehr...
2 CitationsSource
Jul 1, 2017 in ASAP (Application-Specific Systems, Architectures, and Processors)
#1Achim Losch (University of Paderborn)H-Index: 2
#2Marco Platzner (University of Paderborn)H-Index: 25
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...
2 CitationsSource
Mar 1, 2016 in DATE (Design, Automation, and Test in Europe)
#1Achim Losch (University of Paderborn)H-Index: 2
#2Tobias Beisel (University of Paderborn)H-Index: 6
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 5 authors...
The use of heterogeneous computing resources, such as Graphic Processing Units or other specialized coprocessors, has become widespread in recent years because of their performance 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 exploi...
8 CitationsSource
#1Andreas Agne (University of Paderborn)H-Index: 8
#2Markus Happe (ETH Zurich)H-Index: 10
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 5 authors...
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 sys...
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#1Andreas Agne (University of Paderborn)H-Index: 8
#2Markus Happe (University of Paderborn)H-Index: 10
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 5 authors...
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...
11 CitationsSource
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