| Helena Kotthaus, Ingo Korb and Peter Marwedel.
Performance Analysis for Parallel R Programs: Towards Efficient Resource Utilization.
In Abstract Booklet of the International R User Conference (UseR!), pages 66
Aalborg, Denmark, June 2015
[BibTeX][Link]@inproceedings { kotthaus/2015a,
author = {Kotthaus, Helena and Korb, Ingo and Marwedel, Peter},
title = {Performance Analysis for Parallel R Programs: Towards Efficient Resource Utilization},
booktitle = {Abstract Booklet of the International R User Conference (UseR!)},
year = {2015},
pages = {66},
address = {Aalborg, Denmark},
month = {June},
url = {http://user2015.math.aau.dk/docs/useR2015-BookOfAbstracts.pdf},
confidential = {n},
} |
| Andreas Heinig, Florian Schmoll, Björn Bönninghoff, Peter Marwedel and Michael Engel.
FAME: Flexible Real-Time Aware Error Correction by Combining Application Knowledge and Run-Time Information.
In Proceedings of the 11th Workshop on Silicon Errors in Logic - System Effects (SELSE)
2015
[BibTeX][PDF]@inproceedings { heinig:2015:selse,
author = {Heinig, Andreas and Schmoll, Florian and B\"onninghoff, Bj\"orn and Marwedel, Peter and Engel, Michael},
title = {FAME: Flexible Real-Time Aware Error Correction by Combining Application Knowledge and Run-Time Information},
booktitle = {Proceedings of the 11th Workshop on Silicon Errors in Logic - System Effects (SELSE)},
year = {2015},
file = {http://ls12-www.cs.tu-dortmund.de/daes/media/documents/publications/downloads/2015-heinig-selse2015.pdf},
confidential = {n},
} |
| Olaf Neugebauer, Michael Engel and Peter Marwedel.
Multi-Objective Aware Communication Optimization for Resource-Restricted Embedded Systems.
In Proceedings of Architecture of Computing Systems. Proceedings, ARCS
2015
[BibTeX][PDF][Abstract]@inproceedings { neugebauer:2015:arcs,
author = {Neugebauer, Olaf and Engel, Michael and Marwedel, Peter},
title = {Multi-Objective Aware Communication Optimization for Resource-Restricted Embedded Systems},
booktitle = {Proceedings of Architecture of Computing Systems. Proceedings, ARCS},
year = {2015},
file = {http://ls12-www.cs.tu-dortmund.de/daes/media/documents/publications/downloads/2015-neugebauer-arcs.pdf},
confidential = {n},
abstract = {Creating efficient parallel software for current embedded multicore systems is a complex and error-prone task. While automatic parallelization tools help to exploit the performance of multicores, most of these systems waste optimization opportunities since they neglect to consider hardware details such as communication performance and memory hierarchies. In addition, most tools do not allow multi-criterial optimization for objectives such as performance and energy. These approaches are especially relevant in the embedded domain. In this paper we present PICO, an approach that enables multi-objective optimization of embedded parallel programs. In combination with a state-of-the-art parallelization approach for sequential C code, PICO uses high-level models and simulators for performance and energy consumption optimization. As a result, PICO generates a set of Pareto-optimal solutions using a genetic algorithm-based optimization. These solutions allow an embedded system designer to choose a parallelization solution which exhibits a suitable trade-off between the required speedup and the resulting energy consumption according to a given system's requirements. Using PICO, we were able to reduce energy consumption by about 35% compared to the sequential execution for a heterogeneous architecture. Further, runtime reductions by roughly 55% were achieved for a benchmark on a homogeneous platform.},
}
Creating efficient parallel software for current embedded multicore systems is a complex and error-prone task. While automatic parallelization tools help to exploit the performance of multicores, most of these systems waste optimization opportunities since they neglect to consider hardware details such as communication performance and memory hierarchies. In addition, most tools do not allow multi-criterial optimization for objectives such as performance and energy. These approaches are especially relevant in the embedded domain. In this paper we present PICO, an approach that enables multi-objective optimization of embedded parallel programs. In combination with a state-of-the-art parallelization approach for sequential C code, PICO uses high-level models and simulators for performance and energy consumption optimization. As a result, PICO generates a set of Pareto-optimal solutions using a genetic algorithm-based optimization. These solutions allow an embedded system designer to choose a parallelization solution which exhibits a suitable trade-off between the required speedup and the resulting energy consumption according to a given system's requirements. Using PICO, we were able to reduce energy consumption by about 35% compared to the sequential execution for a heterogeneous architecture. Further, runtime reductions by roughly 55% were achieved for a benchmark on a homogeneous platform.
|
| Olaf Neugebauer, Pascal Libuschewski, Michael Engel, Heinrich Mueller and Peter Marwedel.
Plasmon-based Virus Detection on Heterogeneous Embedded Systems.
In Proceedings of Workshop on Software & Compilers for Embedded Systems (SCOPES)
2015
[BibTeX][PDF][Abstract]@inproceedings { neugebauer2015:scopes,
author = {Neugebauer, Olaf and Libuschewski, Pascal and Engel, Michael and Mueller, Heinrich and Marwedel, Peter},
title = {Plasmon-based Virus Detection on Heterogeneous Embedded Systems},
booktitle = {Proceedings of Workshop on Software \& Compilers for Embedded Systems (SCOPES) },
year = {2015},
file = {http://ls12-www.cs.tu-dortmund.de/daes/media/documents/publications/downloads/2015-neugebauer-scopes.pdf},
confidential = {n},
abstract = {Embedded systems, e.g. in computer vision applications, are expected to provide significant amounts of computing power to process large data volumes. Many of these systems, such as used in medical diagnosis, are mobile devices and face significant challenges to provide sufficient performance while operating on a constrained energy budget.
Modern embedded MPSoC platforms use heterogeneous CPU and GPU cores providing a large number of optimization parameters. This allows to find useful trade-offs between energy consumption and performance for a given application. In this paper, we describe how the complex data processing required for PAMONO, a novel type of biosensor for the detection of biological viruses, can efficiently be implemented on a state-of-the-art heterogeneous MPSoC platform. An additional optimization dimension explored is the achieved quality of service. Reducing the virus detection accuracy enables additional optimizations not achievable by modifying hardware or software parameters alone.
Instead of relying on often inaccurate simulation models, our design space exploration employs a hardware-in-the-loop approach to evaluate the performance and energy consumption on the embedded target platform. Trade-offs between performance, energy and accuracy are controlled by a genetic algorithm running on a PC control system which deploys the evaluation tasks to a number of connected embedded boards. Using our optimization approach, we are able to achieve frame rates meeting the requirements without losing accuracy. Further, our approach is able to reduce the energy consumption by 93% with a still reasonable detection quality.},
}
Embedded systems, e.g. in computer vision applications, are expected to provide significant amounts of computing power to process large data volumes. Many of these systems, such as used in medical diagnosis, are mobile devices and face significant challenges to provide sufficient performance while operating on a constrained energy budget.
Modern embedded MPSoC platforms use heterogeneous CPU and GPU cores providing a large number of optimization parameters. This allows to find useful trade-offs between energy consumption and performance for a given application. In this paper, we describe how the complex data processing required for PAMONO, a novel type of biosensor for the detection of biological viruses, can efficiently be implemented on a state-of-the-art heterogeneous MPSoC platform. An additional optimization dimension explored is the achieved quality of service. Reducing the virus detection accuracy enables additional optimizations not achievable by modifying hardware or software parameters alone.
Instead of relying on often inaccurate simulation models, our design space exploration employs a hardware-in-the-loop approach to evaluate the performance and energy consumption on the embedded target platform. Trade-offs between performance, energy and accuracy are controlled by a genetic algorithm running on a PC control system which deploys the evaluation tasks to a number of connected embedded boards. Using our optimization approach, we are able to achieve frame rates meeting the requirements without losing accuracy. Further, our approach is able to reduce the energy consumption by 93% with a still reasonable detection quality.
|
| Helena Kotthaus, Ingo Korb and Peter Marwedel.
Distributed Performance Analysis for R.
In R Implementation, Optimization and Tooling Workshop (RIOT)
Prag, Czech, July 2015
[BibTeX][Link]@inproceedings { kotthaus/2015b,
author = {Kotthaus, Helena and Korb, Ingo and Marwedel, Peter},
title = {Distributed Performance Analysis for R},
booktitle = {R Implementation, Optimization and Tooling Workshop (RIOT)},
year = {2015},
address = {Prag, Czech},
month = {July},
url = {http://2015.ecoop.org/track/RIOT-2015-papers#program},
confidential = {n},
} |
DAES