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READEX

Title
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
Funding
EC | H2020 | RIA
Call
H2020-FETHPC-2014
Contract (GA) number
671657
Start Date
2015/09/01
End Date
2018/08/31
Open Access mandate
yes
Data Pilot
no
Organizations
NUI Galway, VSB, GNS, INTEL, TUM, NTNU, TUD
More information
Detailed project information (CORDIS)

 

  • Autotuning of MPI Applications Using PTF

    Sikora, Anna;César, Eduardo;Comprés, Isaías;Gerndt, Michael (2017)
    Projects: EC | READEX (671657)

    The READEX formalism for automatic tuning for energy efficiency

    Schuchart, Joseph; Gerndt, Michael; Kjeldsberg, Per Gunnar; Lysaght, Michael; Horák, David; Říha, Lubomír; Gocht, Andreas; Sourouri, Mohammed; Kumaraswamy, Madhura; Chowdhury, Anamika; Jahre, Magnus; Diethelm, Kai; Bouizi, Othman; Mian, Umbreen Sabir; Kružík, Jakub;... (2017)
    Projects: EC | READEX (671657)
    Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today’s clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, m...

    The energy consumption optimization of the BLAS routines

    Sojka, Radim; Riha, Lubomir; Horak, David; Kruzik, Jakub; Beseda, Martin; Cermak, Martin (2017)
    Projects: EC | READEX (671657)
    The paper deals with the energy consumption evaluation of selected Sparse and Dense BLAS Level 1, 2 and 3 routines. Authors employed AXPY, Sparse Matrix-Vector, Sparse Matrix-Matrix, Dense Matrix-Vector, Dense Matrix-Matrix and Sparse Matrix-Dense Matrix multiplication routines from Intel Math Kernel Library (MKL). The measured characteristics illustrate the different energy consumption of BLAS routines, as some operations are memory-bounded and others are compute-bounded. Based o...

    Domain Knowledge Specification for Energy Tuning

    Chowdhury, Anamika; Gerndt, Michael (2017)
    Projects: EC | READEX (671657)
    The European Horizon 2020 project READEX is developing a tool suite for dynamic energy tuning of HPC applications. While the tool suite supports an automatic approach, domain knowledge can significantly help in the analysis and the runtime tuning phase. This paper presents the means available in READEX for the application expert to provide his expert knowledge to the tool suite.

    Software Controlled Clock Modulation for Energy Efficiency Optimization on Intel Processors

    Schöne, Robert; Ilsche, Thomas; Bielert, Mario; Molka, Daniel; Hackenberg, Daniel (2017)
    Projects: EC | READEX (671657)
    Current Intel processors implement a variety of power saving features like frequency scaling and idle states. These mechanisms limit the power draw and thereby decrease the thermal dissipation of the processors. However, they also have an impact on the achievable performance. The various mechanisms significantly differ regarding the amount of power savings, the latency of mode changes, and the associated overhead. In this paper, we describe and closely examine the so-called software controlle...

    Energy consumption optimization of the total-FETI solver by changing the CPU frequency

    Horak, David; Riha, Lubomir; Sojka, Radim; Kruzik, Jakub; Beseda, Martin; Cermak, Martin; Schuchart, Joseph (2017)
    Projects: EC | READEX (671657)
    The paper deals with the energy consumption evaluation of the Finite Element Tearing and Interconnect (FETI) based solvers of linear systems, which is an established method for solving real-world engineering problems. Authors evaluated the effect of the CPU frequency on the energy consumption of the FETI solver using a linear elasticity 3D cube synthetic benchmark. In this problem, the effect of frequency tuning on the energy consumption of the essential processing kernels of the FETI method ...

    Tools for assessing and optimizing the energy requirements of high performance scientific computing software

    Diethelm, Kai (2016)
    Projects: EC | READEX (671657)
    Score-P is a measurement infrastructure originally designed for the analysis and optimization of the performance of HPC codes. Recent extensions of Score-P and its associated tools now also allow the investigation of energy-related properties and support the user in the implementation of corresponding improvements. Since it would be counterproductive to completely ignore performance issues in this connection, the focus should not be laid exclusively on energy. We therefore aim to optimize sof...

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

    Sourouri, Mohammed; Raknes, Espen Birger; Reissmann, Nico; Langguth, Johannes; Hackenberg, Daniel; Schöne, Robert; Kjeldsberg, Per Gunnar (2017)
    Projects: EC | READEX (671657)
    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 ...

    Domain Knowledge Specification for Energy Tuning

    Chowdhury, Anamika; Kumaraswamy, Madhura; Gerndt, Michael; Bendifallah, Zakaria; Bouizi, Othman; Řı́ha, Lubomı́r; Vysocký, Ondřej; Beseda, Martin; Zapletal, Jan (2017)
    Projects: EC | READEX (671657)
    The European Horizon 2020 project READEX is developing a tool suite for dynamic energy tuning of HPC applications. While the tool suite supports an automatic approach, domain knowledge can significantly help in the analysis and the runtime tuning phase. This paper presents the means available in READEX for the application expert to provide his expert knowledge to the tool suite.
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