PEPPHER: Efficient and Productive Usage of Hybrid Computing Systems

Benkner, Siegfried; Pllana, Sabri; Träff, Jesper Larsson; Tsigas, Philippas; Dolinsky, Uwe; Augonnet, Cédric; Bachmayer, Beverly; Keßler, Christoph; Moloney, David; Osipov, Vitaly

doi:10.1109/mm.2011.67

Cited by 76 publications

(41 citation statements)

References 17 publications

(18 reference statements)

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“…The PEPPHER project [9] implements a framework that compiles multiple variants of the code for different types of architectures such as CPU's and GPU's. During execution, a runtime system decides which code path to execute.…”

Section: Related Workmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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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, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

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Section: Related Workmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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“…The Performance Portability and Programmability for Heterogeneous Many-core Architectures PEPPHER project [12] has developed a methodology and framework for programming and optimizing applications for single-node heterogeneous many-core processors to ensure performance portability. With Intel as a key partner of the READEX project, we will go one step further and provide a framework that supports the heterogeneity of the system in the form of tuning parameters that allows large-scale heterogeneous applications to dynamically (and automatically) adapt heterogeneous resources according to runtime requirements.…”

Section: Related Workmentioning

confidence: 99%

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Kjeldsberg

Gocht

Gerndt

et al. 2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

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Abstract-In both the embedded systems and High Performance Computing domains, energy-efficiency has become one of the main design criteria. Efficiently utilizing the resources provided in computing systems ranging from embedded systems to current petascale and future Exascale HPC systems will be a challenging task. Suboptimal designs can potentially cause large amounts of underutilized resources and wasted energy. In both domains, a promising potential for improving efficiency of scalable applications stems from the significant degree of dynamic behaviour, e.g., runtime alternation in application resource requirements and 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 analysed at design time and used to optimize system configurations at runtime.The European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) project will develop a tools-aided auto-tuning methodology inspired by the system scenario methodology used in embedded systems. Dynamic behaviour of HPC applications will be exploited 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 to split design time and runtime automatic tuning while targeting heterogeneous system at the Exascale level. This paper describes plans for the project as well as early results achieved during its first year. Furthermore, it is shown how project results will be brought back into the embedded systems domain.

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“…During 2010-2012, we worked in the PEPPHER project [32] which is a European FP7 project. In the PEPPHER project, we proposed a unified framework for programming and optimizing applications for heterogeneous many-core systems to enable both programmability and performance portability.…”

Section: Peppher Component Modelmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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This thesis adresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations for each computation; performance is achieved/retained either a) by selecting a suitable implementation for each computation on a given platform or b) by dividing the computation work across different implementations running on CPU and GPU devices in parallel.In the first approach, we work on a skeleton programming library (SkePU) that provides high-level abstraction while making intelligent implementation selection decisions underneath either before or during the actual program execution. In the second approach, we develop a composition tool that parses extra information (metadata) from XML files, makes certain decisions offline, and, in the end, generates code for making the final decisions at runtime. The third approach is a framework that uses source-code annotations and program analysis to generate code for the runtime library to make the selection decision at runtime. With a generic performance modeling API alongside program analysis capabilities, it supports online tuning as well as complex program transformations.These approaches differ in terms of genericity, intrusiveness, capabilities and knowledge about the program source-code; however, they all demonstrate usefulness of component programming techniques for programming GPU-based systems. With experimental evaluation, we demonstrate how all three approaches, although different in their own way, provide good performance on different GPU-based systems for a variety of applications.This work has been supported by two EU FP7 projects (PEP-PHER, EXCESS) and by SeRC. Populärvetenskaplig sammanfattningAtt få varje generation av datorer att fungera snabbareär viktigt för samhä-llets utveckling och tillväxt. Traditionellt hade de flesta datorer bara en general-purpose processor (den så kallade CPU:n) som bara kunde exekvera en beräkningsuppgift i taget. Under det senasteårtiondet har dock flerkärniga och mångkärniga processorer blivit vanliga, och datorer har också blivit mer heterogena. Ett modernt datorsystem innehåller vanligtvis flerän en CPU, tillsammans med specialprocessorer såsom grafikprocessorer (GPU:er) som ar anpassade för att kunna exekvera vissa typer av beräkningar effektivarë an CPU:er. Vi kallar ett sådant system med en eller flera GPU:er för ett GPU-baserat system. GPU:er i sådana system har sitt eget separata minne, och för att kunna köra en beräkning på en GPU så behöver man vanligtvis flytta all indata till GPU:ns minne och sedan hämta tillbaka resultatet när beräkningenär klar.Programmeringen av GPU-baserade systemär icke-trivialt av flera anledningar: (1) CPU:er och GPU:er kräver olika programmeringsexper...

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PEPPHER: Efficient and Productive Usage of Hybrid Computing Systems

Cited by 76 publications

References 17 publications

The READEX formalism for automatic tuning for energy efficiency

The READEX formalism for automatic tuning for energy efficiency

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Performance-aware component composition for GPU-based systems

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