Equivalence of Deterministic One-Counter Automata is NL-complete

We prove that language equivalence of deterministic one-counter automata is NL-complete. This improves the superpolynomial time complexity upper bound shown by Valiant and Paterson in 1975. Our main contribution is to prove that two deterministic one-counter automata are inequivalent if and only if they can be distinguished by a word of length polynomial in the size of the two input automata

Bisimulation equivalence and regularity for real-time one-counter automata

A one-counter automaton is a pushdown automaton with a singleton stack alphabet, where stack emptiness can be tested; it is a real-time automaton if it contains no ε -transitions. We study the computational complexity of the problems of equivalence and regularity (i.e. semantic finiteness) on real-time one-counter automata. The first main result shows PSPACEPSPACE-completeness of bisimulation equivalence; this closes the complexity gap between decidability [23] and PSPACEPSPACE-hardness [25]. The second main result shows NLNL-completeness of language equivalence of deterministic real-time one-counter automata; this improves the known PSPACEPSPACE upper bound (indirectly shown by Valiant and Paterson [27]). Finally we prove PP-completeness of the problem if a given one-counter automaton is bisimulation equivalent to a finite system, and NLNL-completeness of the problem if the language accepted by a given deterministic real-time one-counter automaton is regular.Web of Science80474372

Unifying framework for development of message-passing applications

Import 19/05/2014This thesis focuses on the development of applications for distributed memory systems in the area of scientific and engineering computations. The work addresses the problems inherent to rapid development of parallel applications and the complexity of tools used during their implementation. Herein, an abstract computation model for describing MPI (Message Passing Interface) applications is proposed. In the described topic of interest, MPI is the de facto API. The proposed approach is based on the usage of visual programming for expressing parallel aspects and communication in a developed application. The intention is not to create a complete program visually; rather, a user writes sequential parts of the application in C++ and puts them into the visual program. This allows simple modification of the communication structure and provides immediate visual feedback of the program's behavior through a visual debugger. An abstract model is also designed in a way that provides a unified view of activities that are used during development, including debugging, performance analyses, and verifications. While tools exist in all of these areas, they are usually single-purpose tools. In the proposed approach, all these activities are roofed by a single conceptual frame; everything from configurations to providing results of analyses uses one set of terms. The thesis makes the following contributions: It introduces the syntax and semantics of the visual language for describing parallel aspects and communication in applications for distributed memory systems. The semantics of the language is formalized and the usage of the language is demonstrated for various problems. These examples show that the sizes of diagrams describing the visual programs remain reasonably small for many practical parallel applications. Measurements show that the performance of the resulting programs is close to manually created C++ applications. Also, the features of the proposed approach may help in various ways in common activities that occur during development. The ideas presented in this thesis are implemented as a tool named Kaira (\url{http://verif.cs.vsb.cz/kaira/}); it is an open source tool released under GPL (GNU General Public License).Tato práce se zabývá vývojem aplikací pro systémy s distribuovanou pamětí v oblasti vědecko-technických výpočtů. Práce se zaměřuje na rychlý vývoj para\-lelních aplikací a integraci nástrojů používaných při vývoji. Navržený přístup je založen na abstraktním výpočetním modelu a vizuálním programování. Tyto prostředky slouží k vyjádření paralelismu a komunikaci ve vyvíjené aplikaci. Sekvenční části aplikace jsou psány přímo v C++ a tento kód je kombinován s~vizuálním modelem. Navržený přístup dovoluje jednoduchou manipulaci s~programem a umožňuje poskytnout přímočarou vizualizaci chování aplikace. Abstraktní model je také navržen za účelem sjednocení aktivit, které se objevují při vývoji aplikací: debugování, analýza výkonu, predikce výkonu a verifikace. Pro tyto aktivity existuje mnoho různých etablovaných nástrojů, které jsou mnohem vyzrálejší a nabízejí více funkcí než zde předkládaná implementace. Z~pohledu této práce se ale většinou jedná o~programy zaměřené pouze na jednu z oblastí. Navrhovaný přístup spojuje všechny tyto aktivity do jednoho myšlenkového rámce s jednotnou terminologií, konfigurací a prezentací výsledků. Hlavní přínosy této práce spočívají ve vytvoření syntaxe a sémantiky vizuálního jazyka pro popis paralelních částí a komunikace v aplikacích pro systémy s distribuovanou pamětí. Sémantika jazyka je plně formalizována a využití jazyka je demonstrováno na různých příkladech. Tyto příklady ukazují, že diagramy reprezentující program zůstávají relativně malé a přehledné při řešení různých praktických problémů. V této práci je dále ukázáno, že navržený přístup může sjednotit a zjednodušit mnoho různých činností objevujících se v souvislosti s vývojem softwaru v oblasti zájmu této práce. Prezentované myšlenky jsou implementovány v podobě nástroje Kaira (\url{http://verif.cs.vsb.cz/kaira/}). Kaira je uvolněna jako open-source nástroj pod GPL (GNU General Public License).Prezenční460 - Katedra informatikyvyhově

HyperLoom possibilities for executing scientific workflows on the cloud

We have developed HyperLoom - a platform for defining and executing scientific workflows in large-scale HPC systems. The computational tasks in such workflows often have non-trivial dependency patterns, unknown execution time and unknown sizes of generated outputs. HyperLoom enables to efficiently execute the workflows respecting task requirements and cluster resources agnostically to the shape or size of the workflow. Although HPC infrastructures provide an unbeatable performance, they may be unavailable or too expensive especially for small to medium workloads. Moreover, for some workloads, due to HPCs not very flexible resource allocation policy, the system energy efficiency may not be optimal at some stages of the execution. In contrast, current public cloud providers such as Amazon, Google or Exoscale allow users a comfortable and elastic way of deploying, scaling and disposing a virtualized cluster of almost any size. In this paper, we describe HyperLoom virtualization and evaluate its performance in a virtualized environment using workflows of various shapes and sizes. Finally, we discuss the Hyperloom potential for its expansion to cloud environments.61140639

HyperLoom: A platform for defining and executing scientific pipelines in distributed environments

Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery.6

D4.4 API and runtime (complete with documentation and basic unit testing) for IO employing fast local storage

This deliverable presents the activities performed on the ExaQUte project task 4.5 Development of interface to fast local storage. The activities have been focused in two aspects: reduction of the storage space used by applications and design and implementation of an interface that optimizes the use of fast local storage by MPI simulations involved in the project applications. In the rst case, for one of the environments involved in the project (PyCOMPSs) the default behavior is to keep all intermediate les until the end of the execution, in case these les are reused later by any additional task. In the case of the other environment (HyperLoom), all les are deleted by default. To unify these two behaviours, the calls \delete object" and \detele le"have been added to the API and a  ag \keep" that can be set to true to keep the les and objects that maybe needed later on. We are reporting results on the optimization of the storage needed by a small case of the project application that reduces the storage needed from 25GB to 350MB. The second focus has been on the de nition of an interface that enables the optimization of the use of local storage disk. This optimization focuses on MPI simulations that may be executed across multiple nodes. The added annotation enables to de ne access patters of the processes in the MPI simulations, with the objective of giving hints to the runtime of where to allocate the di erent MPI processes and reduce the data transfers, as well as the storage usage

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

D4.3 Benchmarking report as tested on the available infrastructure

The main focus of this deliverable is testing and benchmarking the available infrastructure using the execution frameworks PyCOMPSs and HyperLoom. A selected benchmark employing the Multi Level Monte Carlo (MLMC) algorithm was run on two systems: TIER-0 (MareNostrum4) and TIER-1 (Salomon) supercomputers. In both systems, good performance scalability was achieved

D4.5 Framework development and release

This deliverable presents the nal release of the ExaQUte framework as result of task 4.6 of the project focused on the framework development and optimization. The rst part of the document presents an overview of the dierent parts of the ExaQUte framework providing the links to the repositories where the code of the dierent components can be found as well as the installation and usage guidelines. These repositories will include the nal version of the ExaQUte API and its implementation for the runtimes provided in the project (PyCOMPSs/COMPSs and Quake). The second part of the document presents a performance analysis of the framework by performing strong and weak scaling experiments. In this case, we have focused on the analysis of the new features introduced during the last part of the project to support and optimize the execution of MPI solvers inside the framework. The support for OpenMP was already reported in Deliverable D4.3 [21]. The results of the experiments demonstrate that the proposed framework allow to reach very good scalability for the analysed Monte Carlo problems