The energy consumption optimization of the BLAS routines

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 on their recommendations one can explore dynamic frequency switching to achieve significant energy savings up to 23%

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Domain Knowledge Specification for Energy Tuning

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

Domain knowledge specification for energy tuning

To overcome the challenges of energy consumption of HPC systems, the European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy-efficient Exascale computing) project uses an online auto-tuning approach to improve energy efficiency of HPC applications. The READEX methodology pre-computes optimal system configurations at design-time, such as the CPU frequency, for instances of program regions and switches at runtime to the configuration given in the tuning model when the region is executed. READEX goes beyond previous approaches by exploiting dynamic changes of a region's characteristics by leveraging region and characteristic specific system configurations. While the tool suite supports an automatic approach, specifying domain knowledge such as the structure and characteristics of the application and application tuning parameters can significantly help to create a more refined tuning model. This paper presents the means available for an application expert to provide domain knowledge and presents tuning results for some benchmarks.Web of Science316art. no. E465

Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX)

Efficiently utilizing the resources provided on current petascale and future exascale systems will be a challenging task, potentially causing a large amount of underutilized resources and wasted energy. A promising potential to improve efficiency of HPC applications stems from the significant degree of dynamic behavior, e.g., run-time alternation in application resource requirements in HPC workloads. Manually detecting and leveraging this dynamism to improve performance and energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be analyzed at design-time and used to optimize system configurations at run-time. The European Union Horizon 2020 READEX project will develop a tools-aided scenario based auto-tuning methodology to exploit the dynamic behavior of HPC applications to achieve improved energy-efficiency and performance. Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project aims at developing the first of its kind generic framework for split design-time runtime automatic tuning for heterogeneous system at the exascale level