Enabling EASEY deployment of containerized applications for future HPC systems

The upcoming exascale era will push the changes in computing architecture from classical CPU-based systems in hybrid GPU-heavy systems with much higher levels of complexity. While such clusters are expected to improve the performance of certain optimized HPC applications, it will also increase the difficulties for those users who have yet to adapt their codes or are starting from scratch with new programming paradigms. Since there are still no comprehensive automatic assistance mechanisms to enhance application performance on such systems, we are proposing a support framework for future HPC architectures, called EASEY (Enable exASclae for EverYone). The solution builds on a layered software architecture, which offers different mechanisms on each layer for different tasks of tuning. This enables users to adjust the parameters on each of the layers, thereby enhancing specific characteristics of their codes. We introduce the framework with a Charliecloud-based solution, showcasing the LULESH benchmark on the upper layers of our framework. Our approach can automatically deploy optimized container computations with negligible overhead and at the same time reduce the time a scientist needs to spent on manual job submission configurations.Comment: International Conference on Computational Science ICCS2020, 13 page

Predicting the Probability of Exceeding Critical System Thresholds

In this paper we show how regression modelling can be combined with a special kind of data transformation technique that improves model precision and produces several “preliminary” estimates of the target value. These preliminary estimates can be used for interval estimates of the target value as well as for predicting the probability that it has or will exceed arbitrary predefined thresholds. Our approach can be combined with various regression models and applied in many domains that need to estimate the probability of system malfunctions or other hazardous states brought about by system variables exceeding critical safety thresholds. We rigorously derive the formulas for the probability of crossing an upper bound and a lower bound both separately (one-sided intervals) and together (a two-sided interval), and verify the approach experimentally on a real dataset from the electric power industry.У цій статті показано, як регресійне моделювання можна комбінувати зі спеціальним видом перетворення даних, яке покращує точність моделі і дає кілька «попередніх» оцінок цільового значення. Ці попередні оцінки можна використовувати для інтервальних оцінок цільового значення, а також для прогнозування ймовірності того, що воно прийме або перевищить довільні попередньо визначені порогові значення. Наш підхід можна комбінувати з різними регресійними моделями і застосовувати в багатьох областях, які повинні оцінювати вірогідність збоїв системи або інших небезпечних станів, викликаних системними змінними, що перевищують критичні пороги безпеки. Ми строго виводимо формули для ймовірності перетину верхньої та нижньої межі як окремо (односторонні інтервали), так і разом (двосторонній інтервал) і перевіряємо наш підхід експериментально на реальному наборі даних з електроенергетики.В этой статье показано, как регрессионное моделирование можно комбинировать со специальным видом преобразования данных, которое улучшает точность модели и дает несколько «предварительных» оценок целевого значения. Эти предварительные оценки могут использоваться для интервальных оценок целевого значения, а также для прогнозирования вероятности того, что оно примет или превысит произвольные предопределенные пороговые значения. Наш подход можно комбинировать с различными регрессионными моделями и применять во многих областях, которые должны оценивать вероятность сбоев системы или других опасных состояний, вызванных системными переменными, превышающими критические пороги безопасности. Мы строго выводим формулы для вероятности пересечения верхней и нижней границы как отдельно (односторонние интервалы), так и вместе (двухсторонний интервал), и проверяем наш подход экспериментально на реальном наборе данных из электроэнергетики

Hybrid approach to task allocation in distributed systems

This paper describes the hybrid approach to task allocation in distributed systems by using problem solving methods of the artificial intelligence. For static mapping the objective function is used to evaluate the optimality of the allocation of a task graph onto a processor graph. Together with our optimization method also augmented simulated annealing and heuristic move exchange methods in distributed form are implemented.  For dynamic task allocation the semidistributed approach was designed based on the division of processor network topology into independent and symmetric spheres. Distributed static mapping (DSM) and  dynamic load balancing (DLB) tools are controlled by user window interface. DSM and DLB tools are integrated together with software monitor (PG_PVM) in the graphical GRAPNEL environment

Distributed Mapping Tool under PVM

: This paper describes the mapping algorithm for distributed memory, parallel message passing systems using the objective function to evaluate the optimality of the mapping of a task graph onto a processor graph. Our optimisation method is compared with other ones, some of them issuing from artificial intelligence or operations research. In our experiments randomly generated tasks and processor graphs are used. Keywords: static and dynamic mapping, multicomputer, scheduling, load balancing, smoothing. 1 Introduction Optimal planning of parallel program execution in distributed environment of a multicomputer on the basis of message passing solves the response speed. The theory of optimal mapping comes out from the assumption that the program execution time depends upon uniform load of the processors and upon interprocessor communication minimisation. In this paper, our attention is concentrated on the diffusion method for static mapping. For static mapping a parallel program can be re..

Hybrid Task Allocation Tool for Distributed Systems

This paper describes the hybrid approach to task allocation in distributed systems by using problem-solving methods of the artificial intelligence. For the static mapping the objective function is used to evaluate the optimality of the allocation of a task graph onto a processor graph. Together with our optimization method also augmented simulated annealing and heuristic move exchange methods in distributed form are implemented. For dynamic task allocation the semidistributed approach was designed based on the division of processor network topology into independent and symmetric spheres. Distributed static mapping (DSM) and dynamic load balancing (DLB) tools are controlled by user window interface. DSM and DLB tools are integrated together with software monitor (PG PVM) in the graphical GRAPNEL environment. 1. Introduction Optimal planning of parallel program execution in parallel and distributed systems solves the problem of minimization of execution time. The theory of optimal alloc..

Automatic Configuration of Parallel Programs for Processor Networks

: This paper describes the mapping algorithm for distributed memory, parallel message passing systems using the objective function to evaluate the optimality of the mapping of a task graph onto a processor graph. Our optimization method is compared with other ones, some of them issuing from artificial intelligence or operations research. In our experiments randomly generated tasks and processor graphs are used. Keywords: static and dynamic mapping, multicomputer, scheduling, load balancing, smoothing. 1 Introduction Optimal planning of parallel program execution in distributed environment of a multicomputer on the basis of message passing solves the response speed. Static and dynamic mappings can be available and effective way of solution. The theory of optimal mapping comes out from the assumption that the program execution time depends upon uniform load of the processors and upon interprocessor communication minimization. In this paper, our attention is concentrated on the diffusio..