The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems

Silvano, Cristina; Agosta, Giovanni; Cherubin, Stefano; Gadioli, Davide; Palermo, Gianluca; Bartolini, Andrea; Benini, Luca; Martinovič, Jan; Palkovic, Martin; Slaninová, Kateřina; Bispo, João; Cardoso, João M. P.; Abreu, Rui; Pinto, Pedro; Cavazzoni, Carlo; Sanna, Nico; Beccari, Andrea R.; Cmar, Radim; Rohou, Erven

doi:10.1145/2903150.2903470

Cited by 26 publications

(28 citation statements)

References 18 publications

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“…The ANTAREX project [10] takes a similar approach. Using a Domain Specific Language (DSL) code can be distributed between multi-core CPUs and accelerators.…”

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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“…The ANTAREX project [10] takes a similar approach. Using a Domain Specific Language (DSL) code can be distributed between multi-core CPUs and accelerators.…”

Section: Related Workmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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“…Adaptive mapping of computations to the processing units is proposed by Luk et al [58]. The Antarex [84] project aims at providing means for application tuning and adaptation for energy efficient heterogeneous high-performance computing systems, by providing a domain specific language that allows specifying adaptation goals at compile-time.…”

Section: Rq1: Software Optimization Goals For Run-time Adaptationmentioning

confidence: 99%

“…Luk et al [58] rely on the execution time of parts of the program, whereas the Antarex framework uses contextual information, requirements, and resource availability for monitoring the program behavior. As tuning parameters, the following are considered, selecting the algorithm implementation [47,91], adjusting the clock speed, core allocation, select algorithm [47], change lock scheduling policy [28], adjust the scancount [29], change mapping scheme [58], and altering resource allocation and task mapping [84].…”

Section: Rq3: Considered Features During Run-time Dynamic Schedulingmentioning

confidence: 99%

“…Zhang et al [102] focus on applications that consist of single-loops [15] Determining thread mapping strategy for TM applications Since it uses static features, it can not change the mapping strategy when the parallelism degree changes at runtime [56] Determining near-optimal system configuration parameters Requires extensive profiling data collection and analysis, which may introduce significant run-time overhead [24,74,75] Dynamic task scheduling The proposed approaches do not consider dependencies between tasks [80] Dividing tasks into chunks and scheduling in task-farm way Task-farm or master-slave like scheduling techniques requires no profiling, however they introduce communication overheads [2,104,105] Task scheduling Assume that communication time is known prior execution, processing units have equal computing power and are always ready to perform tasks, and scheduling can be determined off-line and it can not be changed at run-time [71] Reducing the number of required training data This solution requires additional programming investment to build the prediction committee [4] Mapping of computation to heterogeneous systems Collecting profiling information for each kernel on every device represent huge overhead, especially on heterogeneous systems that may comprise larger number of non-identical CPUs and/or GPUs [91] Adaptive algorithm selection Considers hardware characteristics, but not the program input characteristics. Some algorithms perform better for smaller input sizes, whereas others perform better with larger ones [28,29,46,47,58,84] Self-adaptation Require adding additional information to the code. Require running the application for a certain amount of time (often with non-optimal parameters) until the framework takes a more optimal decision and applications (data-bound or compute-bound).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

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“…The ANTAREX project [13] creates a Domain Specific Language (DSL), which distributes the code between multicore CPUs and accelerators. An extra compilation step is introduced to translate the DSL into the intended programming language.…”

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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The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems

Cited by 26 publications

References 18 publications

The READEX formalism for automatic tuning for energy efficiency

The READEX formalism for automatic tuning for energy efficiency

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

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

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