Faculty Profile

Johannes Langguth

Researcher 2 - Department of Data Science and Analytics

Publications

Sourouri, Mohammed; Raknes, Espen Birger, Reissmann, Nico, Langguth, Johannes, Hackenberg, Daniel, Schöne, Robert & Kjeldsberg, Per Gunnar (2017)

Towards Fine-Grained Dynamic Tuning of HPC Applications on Modern Multi-Core Architectures

Raghavan, Padma (red.). Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

There is a consensus that exascale systems should operate within a power envelope of 20MW. Consequently, energy conservation is still considered as the most crucial constraint if such systems are to be realized. So far, most research on this topic has focused on strategies such as power capping and dynamic power management. Although these approaches can reduce power consumption, we believe that they might not be sufficient to reach the exascale energy-efficiency goals. Hence, we aim to adopt techniques from embedded systems, where energy-efficiency has always been the fundamental objective. A successful energy-saving technique used in embedded systems is to integrate fine-grained autotuning with dynamic voltage and frequency scaling. In this paper, we apply a similar technique to a real-world HPC application. Our experimental results on a HPC cluster indicate that such an approach can save up to 19% of energy compared to the baseline configuration, with negligible performance loss.

Lagraviere, Jeremie Alexandre Emilien; Langguth, Johannes, Sourouri, Mohammed, Ha, Hoai Phuong & Cai, Xing (2016)

On the performance and energy efficiency of the PGAS programming model on multicore architectures

Fox, Geoffrey C. & Nygård, Mads (red.). Proceedings of the 14th IEEE International Conference on High Performance Computing & Simulation (HPCS 2016)

Naim, Md.; Manne, Fredrik, Halappanavar, Mahantesh, Tumeo, Antonino & Langguth, Johannes (2015)

Optimizing Approximate Weighted Matching on Nvidia Kepler K40

Ranka, Sanjay (red.). 2015 IEEE 22nd International Conference on High Performance Computing (HiPC 2015)

Langguth, Johannes & Cai, Xing (2015)

Heterogeneous CPU-GPU computing for the finite volume method on 3D unstructured meshes

Proceedings of the International Conference on Parallel and Distributed Systems, 2015-April, s. 191- 199. Doi: 10.1109/PADSW.2014.7097808

Sourouri, Mohammed; Langguth, Johannes, Spiga, Filippo, Baden, Scott & Cai, Xing (2015)

CPU+GPU Programming of Stencil Computations for Resource-Efficient Use of GPU Clusters

Plessl, Christian (red.). Proceedings of the 2015 IEEE 18th International Conference on Computational Science and Engineering

Lan, Qiang; Gaur, Namit, Langguth, Johannes & Cai, Xing (2015)

Towards Detailed Tissue-Scale 3D Simulations of Electrical Activity and Calcium Handling in the Human Cardiac Ventricle

Wang, Guojun; Zomaya, Albert, Perez, Gregorio Martinez & Li, Kenli (red.). Algorithms and Architectures for Parallel Processing

Sourouri, Mohammed; Raknes, Espen Birger, Reissmann, Nico, Langguth, Johannes, Hackenberg, Daniel, Schöne, Robert & Kjeldsberg, Per Gunnar (2017)

Towards Fine-Grained Dynamic Tuning of HPC Applications on Modern Multi-Core Architectures

[Academic lecture]. SC17, The International Conference for High Performance Computing, Networking, Storage and Analysis.

There is a consensus that exascale systems should operate within a power envelope of 20MW. Consequently, energy conservation is still considered as the most crucial constraint if such systems are to be realized. So far, most research on this topic has focused on strategies such as power capping and dynamic power management. Although these approaches can reduce power consumption, we believe that they might not be sufficient to reach the exascale energy-efficiency goals. Hence, we aim to adopt techniques from embedded systems, where energy-efficiency has always been the fundamental objective. A successful energy-saving technique used in embedded systems is to integrate fine-grained autotuning with dynamic voltage and frequency scaling. In this paper, we apply a similar technique to a real-world HPC application. Our experimental results on a HPC cluster indicate that such an approach can save up to 19% of energy compared to the baseline configuration, with negligible performance loss.

Lagraviere, Jeremie Alexandre Emilien; Prugger, Martina, Einkemmer, Lukas, Langguth, Johannes, Ha, Hoai Phuong & Cai, Xing (2016)

Implementing and optimizing a Sparse Matrix-Vector Multiplication with UPC

[Report]. Institutt for informatikk Tromsø Tromsø.

Academic Degrees
Year Academic Department Degree
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