Auto-tuning for Energy Usage in Scientific Applications

Tiwari, Ananta; Laurenzano, Michael A.; Carrington, Laura; Snavely, Allan

doi:10.1007/978-3-642-29740-3_21

Cited by 34 publications

(26 citation statements)

References 23 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [40] a threshold value is changed while executing parallel Monte Carlo ocean color simulations, while [18] presents a study on tuning Fast Fourier Transformations on graphic processing units. Also, Rahman et al [56] and Tiwari et al [65] studied the effect of compiler parameters on both performance and power/energy consumption for scientific computing. A lot of modeling and tuning effort has recently been devoted to the specific application of MapReduce [5,15,30,49,57,58].…”

Section: Related Workmentioning

confidence: 99%

Automated design of self-adaptive software with control-theoretical formal guarantees

Filieri

Hoffmann

Maggio

2014

Proceedings of the 36th International Conference on Software Engineering

121

123

View full text Add to dashboard Cite

Self-adaptation enables software to execute successfully in dynamic, unpredictable, and uncertain environments.Control theory provides a broad set of mathematically grounded techniques for adapting the behavior of dynamic systems. While it has been applied to specific software control problems, it has proved difficult to define methodologies allowing non-experts to systematically apply control techniques to create adaptive software. These difficulties arise because computer systems are usually non-linear, with varying workloads and heterogeneous components, making it difficult to model software as a dynamic system; i.e., by means of differential or difference equations. This paper proposes a broad scope methodology for automatically constructing both an approximate dynamic model of a software system and a suitable controller for managing its non-functional requirements. Despite its generality, this methodology provides formal guarantees concerning the system's dynamic behavior by keeping its model continuously updated to compensate for changes in the execution environment and effects of the initial approximation.We apply the methodology to three case studies, demonstrating its generality by tackling different domains (and different non-functional requirements) with the same approach. Being broadly applicable and fully automated, this methodology may allow the adoption of control theoretical solutions (and their formal properties) for a wide range of software adaptation problems.

show abstract

Section: Related Workmentioning

confidence: 99%

Automated design of self-adaptive software with control-theoretical formal guarantees

Filieri

Hoffmann

Maggio

2014

Proceedings of the 36th International Conference on Software Engineering

121

123

View full text Add to dashboard Cite

show abstract

“…Paper [16] considers tuning of application execution by proper tiling in the code (cache usage) and CPU frequency. It considers impact on the execution time and energy usage using an example of Poisson's equation with stencil computations.…”

Section: Related Workmentioning

confidence: 99%

Modeling energy consumption of parallel applications

Czarnul

Kuchta

Rościszewski

et al. 2016

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The paper presents modeling and simulation of energy consumption of two types of parallel applications: geometric Single Program Multiple Data (SPMD) and divide-and-conquer (DAC). Simulation is performed in a new MERPSYS (Modeling Efficiency, Reliability and Power consumption of multilevel parallel HPC SYStems using CPUs and GPUs) environment. Model of an application uses the Java language with extensions representing message exchange between processes working in parallel. Simulation is performed by running threads representing distinct process codes of an application, with consideration of process counts. Instead of running time consuming calculations, their times are simulated using functions representing computational time dependent on input data sizes. The simulator considers performance and power consumption values for compute devices stored in its database. We performed verification of running the two applications on up to 512 and 1024 processes respectively on a large cluster from Academic Computer Center in Gdansk demonstrating a high degree of accuracy between simulated and measured results.

show abstract

“…Historically, a notion of framing autonomic computing systems was highly appreciated by researchers for decades [11,29]. Moreover, researchers had shown their keen interest in counteracting the scalability and the energy consumption issues of applications [2,4,15,31] at various levels of computing systems so that the existing applications could be executed on future machines at ease.…”

Section: S Benedictmentioning

confidence: 99%

SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications

Benedict¹

2018

SCPE

View full text Add to dashboard Cite

Abstract. HPC application developers, including OpenMP-based application developers, have stepped forward to endeavor the future design trends of exa-scale machines, such as, increased number of threads/cores, heterogeneous architectures, multiple levels of memories, and so forth; and, they have initiated procedures to address application level challenges, such as, data-driven scalability issues, energy consumption requirements, data availability needs, and so forth. Despite the existence of manual performance tuning solutions, users still deem it to be an intricate process. This paper proposes a scalability aware autotuning framework (SCALE-EA) that automatically identifies an efficient number of threads for OpenMP parallel regions using a Firefly Algorithm ( 1. Introduction. High Performance Computing (HPC) application developments are invariably cropping up among various scientific domains, such as, High Energy Physics (HEP), bioinformatics, eyewear computing, visualizations, electronic automation, graph-based machine learning, and so forth. OpenMP based programming model is indeed reaching out to become a prominent programming model among a sector of HPC application developers owing to the adequate doctrine of standards (OpenMP 4.0 and 4.5), ease of use, controlled programming support, smooth applicability to programmers belonging to various scientific disciplines, and due to the notion of having millions of cores in future exascale machines.However, the realization of efficiently utilizing HPC applications in its present form for future large scale machines requires innovative approaches to mitigate the following possible risky scenarios:1. the performance of applications becomes more sensitive to data movement, data availability, data provenance, data management policies, and so forth -a future software-cum-hardware computing system must consider the massive storage options of machines, resiliency nature of applications, dynamic computing behavior of applications, and the dynamic nature of the data access patterns of applications (big data). 2. the current implementations of OpenMP applications might not have considered the design aspects of emerging memory models (including data persistence of modern memory architectures), infrastructural improvements, future parallel data structures, and so forth. 3. the scalability of applications might get an impoverished lead as applications are usually not ported and tested for scalable machines. 4. the energy efficiency of applications could exhibit a daunting scenario when executed on machines with varying degrees of parallelism -smaller or larger. 5. the current OpenMP application developers might not have quantified the possible uncertainties that might evolve due to the underlying future parallel software frameworks. In short, to mitigate these challenges, programmers or developers have to diligently write scalable and energy efficient parallel algorithms by employing the apt scalability features of programming languages and by considering the underlying require...

show abstract

Auto-tuning for Energy Usage in Scientific Applications

Cited by 34 publications

References 23 publications

Automated design of self-adaptive software with control-theoretical formal guarantees

Automated design of self-adaptive software with control-theoretical formal guarantees

Modeling energy consumption of parallel applications

SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications

Contact Info

Product

Resources

About