Francisco Heron de Carvalho Junior

Bio: Francisco Heron de Carvalho Junior is an academic researcher from Federal University of Ceará. The author has contributed to research in topics: Supercomputer & Cloud computing. The author has an hindex of 6, co-authored 28 publications receiving 110 citations. Previous affiliations of Francisco Heron de Carvalho Junior include Federal University of Pernambuco.

Translating Haskell# programs into Petri nets

Abstract: Haskell# is a concurrent programming environment aimed at parallel distributed architectures. Haskell# programs may be automatically translated to Petri nets, an important formalism for analysis of properties of concurrent and non-determinisc systems. This paper motivates and formalizes the translation of Haskell# programs into Petri nets, providing some examples of their usage.

Haskell#: Parallel Programming Made Simple and Efficient

Abstract: This paper presents the final result of the designing of a new specification for the Haskell# Language, including new features to increase its expressiveness, but without losing either efficiency or obedience to its original premisses.

GPU‐accelerated backtracking using CUDA Dynamic Parallelism

Abstract: New GPGPU technologies, such as CUDA Dynamic Parallelism (CDP), can help dealing with recursive patterns of computation, such as divide-and-conquer, used by backtracking algorithms. In this paper, we propose a GPU-accelerated backtracking algorithm using CDP that extends a well-known parallel backtracking model. The search starts on CPU, processing the search tree until a first cutoff depth. Based on this partial backtracking tree, the algorithm analyzes the memory requirements of subsequent kernel generations. The proposed algorithm performs no dynamic allocation of memory on GPU, unlike related works from the literature. The proposed algorithm has been extensively tested using the N-Queens Puzzle problem and instances of the Asymmetric Traveling Salesman Problem (ATSP) as test-cases. The proposed CDP algorithm may, under some conditions, outperform its non-CDP counterpart by a factor up to 25. But, it may also be up to twice slower. The CDP-based implementation has much better worst case execution times and makes algorithm's performance less dependent on the tuning of parameters. Compared to other CDP-based strategies from the literature, the proposed algorithm is on average 8× faster. The proposed algorithm is also hybridized with another CDP-based strategy from the literature. The combination of strategies is in average 4.5× faster than the related strategy. We also identify some difficulties, limitations, and bottlenecks concerning the CDP programming model which may be useful for helping potential users.

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Abstract: ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

Abstract: Google Colaboratory (also known as Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared with a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory’s accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.

Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm

Abstract: Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management Resource management is a major problem especially for scientific workflows due to their heavy calculations and dependency between their operations Several algorithms and methods have been developed to manage cloud resources In this paper, the combination of state-action-reward-state-action learning and genetic algorithm is used to manage cloud resources At the first step, the intelligent agents schedule the tasks during the learning process by exploring the workflow Then, in the resource provisioning step, each resource is assigned to an agent, and its utilization is attempted to be maximized in the learning process of its corresponding agent This is conducted by selecting the most appropriate set of the tasks that maximizes the utilization of the resource Genetic algorithm is utilized for convergence of the agents of the proposed method, and to achieve global optimization The fitness function that has been exploited by this genetic algorithm seeks to achieve more efficient resource utilization and better load balancing by observing the deadlines of the tasks The experimental results show that the proposed algorithm reduces makespan, enhances resource utilization, and improves load balancing, compared to MOHEFT and MCP, the well-known workflow scheduling algorithms of the literature