BalticLSC: A low-code HPC platform for small and medium research teams

Roszczyk, Radosław; Wdowiak, Marek; Śmiałek, Michał; Rybiński, Kamil; Marek, Krzysztof

doi:10.1109/vl/hcc51201.2021.9576305

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…BalticLSC [16] is a platform for developing or using largescale computing applications. The system is designed to provide access to large-scale computing resources to small businesses and institutions that often do not have the resources to buy and maintain the desired infrastructure, as well as the ability to make unused computing resources available to companies and institutions in exchange for financial benefits.…”

Section: Balticlsc Platform Characteristicsmentioning

confidence: 99%

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Comparative Analysis of Low-Code Computation Systems

Rosłan,

Śmiałek

2023

Position Papers of the 18th Conference on Computer Science and Intelligence Systems

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The paper aims to systematically compare computation platforms where the development of custom computation applications is done visually. By this, we mean platforms equipped with a visual language to define the flow of actions or data, thus allowing us to treat them as low-code systems. The chosen platforms include two mature systems: Orange and Azure Machine Learning Studio, and also a newcomer -BalticLSC. For the purpose of the study, two sample computing tasks were created and executed on the three platforms. Based on this, the platforms were compared with each other taking into account the following characteristics: versatility, scalability, user entry barrier, cost of use, availability of documentation, maintainability and extensibility, availability, security, user interface friendliness, and variety of interfaces for input data.

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Section: Balticlsc Platform Characteristicsmentioning

confidence: 99%

“…One such solution is the BalticLSC platform [16], which is a result of a research project to apply the low-code approach to large-scale computations. The claim of BalticLSC is that it should be simple to use and easy to understand by scientists, researchers and other people not proficient in programming.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Low-Code Computation Systems

Rosłan,

Śmiałek

2023

Position Papers of the 18th Conference on Computer Science and Intelligence Systems

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“…Cost prediction can be facilitated if the computation algorithms are implemented in a modularized and standardized form. Such an approach is taken in the Baltic Large Scale Computing system (BalticLSC [1,2], https:// www.balticlsc.eu). It uses a visual language, called the Computation Application Language (CAL), to define datadriven computation applications.…”

Section: Contextmentioning

confidence: 99%

Estimation of execution time for computing tasks

Bielecki

Śmiałek

2022

Cluster Comput

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This work aims to estimate the execution time of data processing tasks (specific executions of a program or an algorithm) before their execution. The paper focuses on the estimation of the average-case execution time (ACET). This metric can be used to predict the approximate cost of computations, e.g. when resource consumption in a High-Performance Computing system has to be known in advance. The presented approach proposes to create machine learning models using historical data. The models use program metadata (properties of input data) and parameters of the run-time environment as their explanatory variables. Moreover, the set of these variables can be easily expanded with additional parameters of the specific programs. The program code itself is treated as a black box. The response variable of the model is the execution time. The models have been validated within a Large-Scale Computing system that allows for a unified treatment of programs as computation modules. We present the process of training and validation for several different computation modules and discuss the suitability of the proposed models for ACET estimation in various computing environments.

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“…In a data flow-driven approach to LSC, computation applications are divided into computation steps, with data flowing as messages between these steps. An example of such a system is the Balti-cLSC platform [18], [13] (www.balticlsc.eu). It is a low-code computation environment created as part of a project intended to facilitate easier access to LSC.…”

Section: Introductionmentioning

confidence: 99%

Multi-queue service for task scheduling based on data availability

Rybiński,

Śmiałek

2023

Annals of Computer Science and Information Systems

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Large-scale computation (LSC) systems are often performed in distributed environments where message passing is the key to orchestrating computations. In this paper, we present a new message queue concept developed within the context of an LSC system (BalticLSC). The concept consists in proposing a multi-queue, where queues are grouped into families. A queue family can be used to distribute messages of the same kind to multiple computation modules distributed between various nodes. Such message families can be synchronised to implement a mechanism for initiating computation jobs based on multiple data inputs. Moreover, the proposed multi-queue has built-in mechanisms for controlling message sequences in applications where complex data set splitting is necessary. The presented multi-queue concept was successfully implemented and applied in a working LSC system.

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BalticLSC: A low-code HPC platform for small and medium research teams

Cited by 5 publications

References 10 publications

Comparative Analysis of Low-Code Computation Systems

Comparative Analysis of Low-Code Computation Systems

Estimation of execution time for computing tasks

Multi-queue service for task scheduling based on data availability

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