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The recent years saw an evolution of Grid technologies from early ideas to production deployments. At the same time, the expectations for Grids shifted from idealistic hopes — buoyed by the successes of the initial testbeds — to disillusionment with available implementations when applied to large-scale general purpose computing. In this paper, we argue that a mature e-Infrastructure aiming to bridge the gaps between visions and realities cannot be delivered without introducing Service Level Management (SLM). To support this thesis, we present an analysis of the Grid foundations and definitions that shows that SLM-related ideas were incorporated in them from the beginning. Next, we describe how implementing SLM in Grids could improve the usability and user-experience of the infrastructure - both for its customers and service providers. We also present a selection of real-life Grid application scenarios that are important for the research communities supported by the Grid, but cannot be efficiently supported without the SLM process in place. In addition, the paper contains introduction to SLM, a discussion on what introducing SLM to Grids might mean in practice, and what were the current efforts already applied in this field.

In this paper, we present two new approaches while rendering necessary extensions to Periscope to perform scalability and performance analysis on OpenMP codes. Periscope is an online-based performance analysis toolkit which consists of a user defined number of analysis agents that automatically search for the performance properties while the application is running. In order to detect the scalability and performance bottlenecks of OpenMP codes using Periscope, a few newly defined performance properties and meta properties are formalized. We manifest our implementation by evaluating NAS OpenMP benchmarks. As shown in our results, our approach identifies the code regions which do not scale well and other performance problems, e.g. load imbalance in NAS parallel benchmarks.

We investigate several dense bit-wise implementations of Boolean arrays in the context of the functional data-parallel array programming language SAC. A particular problem arises in compiler or directive based parallelisation as the scheduling of loops over Boolean arrays is unaware of the restricted addressability of densely stored Boolean arrays. We discuss our proposed solution and report on experiments analysing the impact of the runtime representation of Boolean arrays both on sequential performance as well as on scalability using up to 32 cores of a large ccNUMA multi-core system.

Reproducibility of Science is considered as one of the main principles of the scientific method, and refers to the ability of an experiment to be accurately reproduced, by third person, in complex experiment every detail matters to ensure the correct reproducibility. In the context of the ICCS 2011, Elsevier organized the executable paper grand challenge a contest to improve the way scientific information is communicated and used. While during this contest the focus was on developing methods and technique to realize the idea of executable papers, in this paper we focus on the operational issues related to the creation a viable service with a predefined QoS.

Programming distributed memory systems forces the user to handle the problem of data locality. With message passing the user has not only to map the application to the different processors in a way that optimizes data locality but also has to explicitly program access to remote data. Shared virtual memory (SVM) systems free him from the second task; nevertheless, he is still responsible to optimize data locality by selecting a well-suited work distribution. We describe a programming environment that is based on the Advanced Shared Virtual Memory system, a SVM implementation for the Paragon (TM) Supercomputer, and on SVM-Fortran, a shared memory parallel programming language with language constructs for work distribution. Programming tools integrate program text and dynamic performance data to help the user in optimizing data locality.

Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important R&D challenges in streamlining the optimization efforts across the layers of the PowerStack., to be published in Energy Efficient HPC State of Practice 2020