Preparing HPC Applications for Exascale: Challenges and Recommendations

Ábrahám, Erika; Bekas, Costas; Brandić, Ivona; Genaim, Samir; Johnsen, Einar Broch; Kondov, Ivan; Pllana, Sabri; Streit, Achim

doi:10.1109/nbis.2015.61

Cited by 25 publications

(13 citation statements)

References 28 publications

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“…Future work could address generalization of our approach for DNA sequence analysis for various types of accelerated systems using techniques that ensure performance portability [8], [17], [24], [29]. The use of modeling and simulation techniques [10], [15], [25], [26] could help to reason about the performance on extreme-scale computing architectures [4].…”

Section: Discussionmentioning

confidence: 99%

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Memeti

Pllana

2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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Abstract-Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speedups of up to 17.6×. Our approach is up to 3× faster than a patternbased parallel approach that uses the RE2 library.

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Section: Discussionmentioning

confidence: 99%

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Memeti

Pllana

2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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“…However, if we look at the nature of scientific and engineering applications that need Exascale systems, we will realize that the purpose of running these applications is to discover the rules governing a natural event [5]. One of the available solutions for HPC systems to support dynamic and interactive applications is to increase system scalability during runtime [20]. Paper [5] addresses the issue that how multiscale computing systems can optimally be used in current Petascale systems as well as HPC Exascale systems through increasing their capabilities.…”

Section: Related Workmentioning

confidence: 99%

“…Since the system under study is a distributed Exascale system, the load balancer is implemented independently in each computing element, and it attempts to manage the processes in the local computing element [20].…”

Section: What Does Dynamic and Interactive Mean In Load Balancing?mentioning

confidence: 99%

Load Balancing in Distributed Exascale Computing Based on Process Requirements

Shahrabi¹,

Khaneghah

Mollasalehi³

et al. 2018

AzJHPC

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In distributed Exascale systems, the occurrence of a dynamic and interactive nature changes the workload of the system's computing elements. Because of this, the load balancer needs to collect information on the system state. Activating the load balancer increases the runtime of the scientific application. While analyzing the impact of the dynamic and interactive nature on the load balancer functionality, this paper also attempts to provide a mathematical definition for load balancer based on the concept of dynamic and interactive nature. This makes it possible to describe and examine the load balancer functionality by considering the impacts of the dynamic and interactive nature. As a result, decisions can be made on the behavior of the load balancer when a dynamic and interactive nature occurs. According to the mentioned operational function, this paper has analyzed the load balancer's behavior in processes with a dynamic and interactive nature. The introduced operational function for the load balancer in this paper enables the management element to separate the requests of processes with a dynamic and interactive nature and normal processes that leads to redistribution.

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“…In the meantime, technological advances in hardware architectures are nearing exascale speed through co-design architectural designs, abundant General Purpose Graphical Processing Units (GPGPUs), hierarchical clustering of heterogeneous machines, and so forth. Despite the growth seen in the application sector and in the hardware architectural design sector of HPC, the performances of applications, including the The HPC community, therefore, oriented their mindset to mitigate the effects of known performance issues of large scale systems such as the dynamic nature of big data in applications (data sizes), heterogeneous hardware architectures [7], energy consumption issues, scalability issues [22,39], uncertainty of resources (including data resources), and so forth [18].…”

Section: S Benedictmentioning

confidence: 99%

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

Benedict¹

2018

SCPE

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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...

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Preparing HPC Applications for Exascale: Challenges and Recommendations

Cited by 25 publications

References 28 publications

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Load Balancing in Distributed Exascale Computing Based on Process Requirements

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

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