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2023 |
Dopke, Luan; Griebler, Dalvan Estudo Sobre Spark nas Aplicações de Processamento de Log e Análise de Cliques Inproceedings doi Anais da XXIII Escola Regional de Alto Desempenho da Região Sul, pp. 85-88, Sociedade Brasileira de Computação, Porto Alegre, Brazil, 2023. Abstract | Links | BibTeX | Tags: Benchmark, Stream processing @inproceedings{larcc:DOPKE:ERAD:23, title = {Estudo Sobre Spark nas Aplicações de Processamento de Log e Análise de Cliques}, author = {Luan Dopke and Dalvan Griebler}, url = {https://doi.org/10.5753/eradrs.2023.229298}, doi = {10.5753/eradrs.2023.229298}, year = {2023}, date = {2023-05-01}, booktitle = {Anais da XXIII Escola Regional de Alto Desempenho da Região Sul}, pages = {85-88}, publisher = {Sociedade Brasileira de Computação}, address = {Porto Alegre, Brazil}, abstract = {O uso de aplicações de processamento de dados de fluxo contínuo vem crescendo cada vez mais, dado este fato o presente estudo visa mensurar a desempenho do framework Apache Spark Strucutured Streaming perante o framework Apache Storm nas aplicações de fluxo contínuo de dados, estas sendo processamento de logs e análise de cliques. Os resultados demonstram melhor desempenho para o Apache Storm em ambas as aplicações.}, keywords = {Benchmark, Stream processing}, pubstate = {published}, tppubtype = {inproceedings} } O uso de aplicações de processamento de dados de fluxo contínuo vem crescendo cada vez mais, dado este fato o presente estudo visa mensurar a desempenho do framework Apache Spark Strucutured Streaming perante o framework Apache Storm nas aplicações de fluxo contínuo de dados, estas sendo processamento de logs e análise de cliques. Os resultados demonstram melhor desempenho para o Apache Storm em ambas as aplicações. |
Garcia, Adriano Marques; Griebler, Dalvan; Schepke, Claudio; García, José Daniel; Muñoz, Javier Fernández; Fernandes, Luiz Gustavo A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores Inproceedings doi 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 164-168, IEEE, Naples, Italy, 2023. Abstract | Links | BibTeX | Tags: Benchmark, Stream processing @inproceedings{GARCIA:PDP:23, title = {A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores}, author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and José Daniel García and Javier Fernández Muñoz and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1109/PDP59025.2023.00033}, doi = {10.1109/PDP59025.2023.00033}, year = {2023}, date = {2023-03-01}, booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)}, pages = {164-168}, publisher = {IEEE}, address = {Naples, Italy}, series = {PDP'23}, abstract = {Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.}, keywords = {Benchmark, Stream processing}, pubstate = {published}, tppubtype = {inproceedings} } Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications. |
Garcia, Adriano Marques; Griebler, Dalvan; Schepke, Claudio; Fernandes, Luiz Gustavo SPBench: a framework for creating benchmarks of stream processing applications Journal Article doi Computing, 105 (5), pp. 1077-1099, 2023. Abstract | Links | BibTeX | Tags: Benchmark, Stream processing @article{GARCIA:Computing:23, title = {SPBench: a framework for creating benchmarks of stream processing applications}, author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1007/s00607-021-01025-6}, doi = {10.1007/s00607-021-01025-6}, year = {2023}, date = {2023-01-01}, journal = {Computing}, volume = {105}, number = {5}, pages = {1077-1099}, publisher = {Springer}, abstract = {In a fast-changing data-driven world, real-time data processing systems are becoming ubiquitous in everyday applications. The increasing data we produce, such as audio, video, image, and, text are demanding quickly and efficiently computation. Stream Parallelism allows accelerating this computation for real-time processing. But it is still a challenging task and most reserved for experts. In this paper, we present SPBench, a framework for benchmarking stream processing applications. It aims to support users with a set of real-world stream processing applications, which are made accessible through an Application Programming Interface (API) and executable via Command Line Interface (CLI) to create custom benchmarks. We tested SPBench by implementing parallel benchmarks with Intel Threading Building Blocks (TBB), FastFlow, and SPar. This evaluation provided useful insights and revealed the feasibility of the proposed framework in terms of usage, customization, and performance analysis. SPBench demonstrated to be a high-level, reusable, extensible, and easy of use abstraction to build parallel stream processing benchmarks on multi-core architectures.}, keywords = {Benchmark, Stream processing}, pubstate = {published}, tppubtype = {article} } In a fast-changing data-driven world, real-time data processing systems are becoming ubiquitous in everyday applications. The increasing data we produce, such as audio, video, image, and, text are demanding quickly and efficiently computation. Stream Parallelism allows accelerating this computation for real-time processing. But it is still a challenging task and most reserved for experts. In this paper, we present SPBench, a framework for benchmarking stream processing applications. It aims to support users with a set of real-world stream processing applications, which are made accessible through an Application Programming Interface (API) and executable via Command Line Interface (CLI) to create custom benchmarks. We tested SPBench by implementing parallel benchmarks with Intel Threading Building Blocks (TBB), FastFlow, and SPar. This evaluation provided useful insights and revealed the feasibility of the proposed framework in terms of usage, customization, and performance analysis. SPBench demonstrated to be a high-level, reusable, extensible, and easy of use abstraction to build parallel stream processing benchmarks on multi-core architectures. |
Araujo, Gabriell; Griebler, Dalvan; Rockenbach, Dinei A; Danelutto, Marco; Fernandes, Luiz Gustavo NAS Parallel Benchmarks with CUDA and Beyond Journal Article doi Software: Practice and Experience, 53 (1), pp. 53-80, 2023. Abstract | Links | BibTeX | Tags: Benchmark, GPGPU, Parallel programming @article{ARAUJO:SPE:23, title = {NAS Parallel Benchmarks with CUDA and Beyond}, author = {Gabriell Araujo and Dalvan Griebler and Dinei A Rockenbach and Marco Danelutto and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1002/spe.3056}, doi = {10.1002/spe.3056}, year = {2023}, date = {2023-01-01}, journal = {Software: Practice and Experience}, volume = {53}, number = {1}, pages = {53-80}, publisher = {Wiley}, abstract = {NAS Parallel Benchmarks (NPB) is a standard benchmark suite used in the evaluation of parallel hardware and software. Several research efforts from academia have made these benchmarks available with different parallel programming models beyond the original versions with OpenMP and MPI. This work joins these research efforts by providing a new CUDA implementation for NPB. Our contribution covers different aspects beyond the implementation. First, we define design principles based on the best programming practices for GPUs and apply them to each benchmark using CUDA. Second, we provide ease of use parametrization support for configuring the number of threads per block in our version. Third, we conduct a broad study on the impact of the number of threads per block in the benchmarks. Fourth, we propose and evaluate five strategies for helping to find a better number of threads per block configuration. The results have revealed relevant performance improvement solely by changing the number of threads per block, showing performance improvements from 8% up to 717% among the benchmarks. Fifth, we conduct a comparative analysis with the literature, evaluating performance, memory consumption, code refactoring required, and parallelism implementations. The performance results have shown up to 267% improvements over the best benchmarks versions available. We also observe the best and worst design choices, concerning code size and the performance trade-off. Lastly, we highlight the challenges of implementing parallel CFD applications for GPUs and how the computations impact the GPU's behavior.}, keywords = {Benchmark, GPGPU, Parallel programming}, pubstate = {published}, tppubtype = {article} } NAS Parallel Benchmarks (NPB) is a standard benchmark suite used in the evaluation of parallel hardware and software. Several research efforts from academia have made these benchmarks available with different parallel programming models beyond the original versions with OpenMP and MPI. This work joins these research efforts by providing a new CUDA implementation for NPB. Our contribution covers different aspects beyond the implementation. First, we define design principles based on the best programming practices for GPUs and apply them to each benchmark using CUDA. Second, we provide ease of use parametrization support for configuring the number of threads per block in our version. Third, we conduct a broad study on the impact of the number of threads per block in the benchmarks. Fourth, we propose and evaluate five strategies for helping to find a better number of threads per block configuration. The results have revealed relevant performance improvement solely by changing the number of threads per block, showing performance improvements from 8% up to 717% among the benchmarks. Fifth, we conduct a comparative analysis with the literature, evaluating performance, memory consumption, code refactoring required, and parallelism implementations. The performance results have shown up to 267% improvements over the best benchmarks versions available. We also observe the best and worst design choices, concerning code size and the performance trade-off. Lastly, we highlight the challenges of implementing parallel CFD applications for GPUs and how the computations impact the GPU's behavior. |
Garcia, Adriano Marques; Griebler, Dalvan; Schepke, Claudio; Fernandes, Luiz Gustavo Micro-batch and data frequency for stream processing on multi-cores Journal Article doi The Journal of Supercomputing, 79 (8), pp. 9206-9244, 2023. Abstract | Links | BibTeX | Tags: Benchmark, Self-adaptation, Stream processing @article{GARCIA:JS:23, title = {Micro-batch and data frequency for stream processing on multi-cores}, author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1007/s11227-022-05024-y}, doi = {10.1007/s11227-022-05024-y}, year = {2023}, date = {2023-01-01}, journal = {The Journal of Supercomputing}, volume = {79}, number = {8}, pages = {9206-9244}, publisher = {Springer}, abstract = {Latency or throughput is often critical performance metrics in stream processing. Applications’ performance can fluctuate depending on the input stream. This unpredictability is due to the variety in data arrival frequency and size, complexity, and other factors. Researchers are constantly investigating new ways to mitigate the impact of these variations on performance with self-adaptive techniques involving elasticity or micro-batching. However, there is a lack of benchmarks capable of creating test scenarios to further evaluate these techniques. This work extends and improves the SPBench benchmarking framework to support dynamic micro-batching and data stream frequency management. We also propose a set of algorithms that generates the most commonly used frequency patterns for benchmarking stream processing in related work. It allows the creation of a wide variety of test scenarios. To validate our solution, we use SPBench to create custom benchmarks and evaluate the impact of micro-batching and data stream frequency on the performance of Intel TBB and FastFlow. These are two libraries that leverage stream parallelism for multi-core architectures. Our results demonstrated that our test cases did not benefit from micro-batches on multi-cores. For different data stream frequency configurations, TBB ensured the lowest latency, while FastFlow assured higher throughput in shorter pipelines.}, keywords = {Benchmark, Self-adaptation, Stream processing}, pubstate = {published}, tppubtype = {article} } Latency or throughput is often critical performance metrics in stream processing. Applications’ performance can fluctuate depending on the input stream. This unpredictability is due to the variety in data arrival frequency and size, complexity, and other factors. Researchers are constantly investigating new ways to mitigate the impact of these variations on performance with self-adaptive techniques involving elasticity or micro-batching. However, there is a lack of benchmarks capable of creating test scenarios to further evaluate these techniques. This work extends and improves the SPBench benchmarking framework to support dynamic micro-batching and data stream frequency management. We also propose a set of algorithms that generates the most commonly used frequency patterns for benchmarking stream processing in related work. It allows the creation of a wide variety of test scenarios. To validate our solution, we use SPBench to create custom benchmarks and evaluate the impact of micro-batching and data stream frequency on the performance of Intel TBB and FastFlow. These are two libraries that leverage stream parallelism for multi-core architectures. Our results demonstrated that our test cases did not benefit from micro-batches on multi-cores. For different data stream frequency configurations, TBB ensured the lowest latency, while FastFlow assured higher throughput in shorter pipelines. |
2022 |
Garcia, Adriano Marques; Griebler, Dalvan; Schepke, Claudio; Fernandes, Luiz Gustavo Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores Inproceedings doi 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 10-17, IEEE, Valladolid, Spain, 2022. Abstract | Links | BibTeX | Tags: Benchmark, Stream processing @inproceedings{GARCIA:PDP:22, title = {Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores}, author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1109/PDP55904.2022.00011}, doi = {10.1109/PDP55904.2022.00011}, year = {2022}, date = {2022-04-01}, booktitle = {30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)}, pages = {10-17}, publisher = {IEEE}, address = {Valladolid, Spain}, series = {PDP'22}, abstract = {In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. Therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations.}, keywords = {Benchmark, Stream processing}, pubstate = {published}, tppubtype = {inproceedings} } In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. Therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations. |
Scheer, Claudio; Araujo, Gabriell; Griebler, Dalvan; Meneguzzi, Felipe; Fernandes, Luiz Gustavo Encontrando a Configuração de Threads por Bloco para os Kernels NPB-CUDA com Q-Learning Inproceedings doi Anais da XXII Escola Regional de Alto Desempenho da Região Sul, pp. 119-120, Sociedade Brasileira de Computação, Curitiba, Brazil, 2022. Abstract | Links | BibTeX | Tags: Benchmark, Deep learning, GPGPU @inproceedings{SCHEER:ERAD:22, title = {Encontrando a Configuração de Threads por Bloco para os Kernels NPB-CUDA com Q-Learning}, author = {Claudio Scheer and Gabriell Araujo and Dalvan Griebler and Felipe Meneguzzi and Luiz Gustavo Fernandes}, url = {https://doi.org/10.5753/eradrs.2022.19191}, doi = {10.5753/eradrs.2022.19191}, year = {2022}, date = {2022-04-01}, booktitle = {Anais da XXII Escola Regional de Alto Desempenho da Região Sul}, pages = {119-120}, publisher = {Sociedade Brasileira de Computação}, address = {Curitiba, Brazil}, abstract = {Este trabalho apresenta um novo método que utiliza aprendizado de máquina para prever a melhor configuração de threads por bloco para aplicações de GPUs. Os resultados foram similares a estratégias manuais.}, keywords = {Benchmark, Deep learning, GPGPU}, pubstate = {published}, tppubtype = {inproceedings} } Este trabalho apresenta um novo método que utiliza aprendizado de máquina para prever a melhor configuração de threads por bloco para aplicações de GPUs. Os resultados foram similares a estratégias manuais. |
2021 |
Löff, Júnior; Griebler, Dalvan; Mencagli, Gabriele; de Araujo, Gabriell ; Torquati, Massimo; Danelutto, Marco; Fernandes, Luiz Gustavo The NAS parallel benchmarks for evaluating C++ parallel programming frameworks on shared-memory architectures Journal Article doi Future Generation Computer Systems, 125 , pp. 743-757, 2021. Abstract | Links | BibTeX | Tags: Benchmark, Parallel programming @article{LOFF:FGCS:21, title = {The NAS parallel benchmarks for evaluating C++ parallel programming frameworks on shared-memory architectures}, author = {Júnior Löff and Dalvan Griebler and Gabriele Mencagli and Gabriell {de Araujo} and Massimo Torquati and Marco Danelutto and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1016/j.future.2021.07.021}, doi = {10.1016/j.future.2021.07.021}, year = {2021}, date = {2021-07-01}, journal = {Future Generation Computer Systems}, volume = {125}, pages = {743-757}, publisher = {Elsevier}, abstract = {The NAS Parallel Benchmarks (NPB), originally implemented mostly in Fortran, is a consolidated suite containing several benchmarks extracted from Computational Fluid Dynamics (CFD) models. The benchmark suite has important characteristics such as intensive memory communications, complex data dependencies, different memory access patterns, and hardware components/sub-systems overload. Parallel programming APIs, libraries, and frameworks that are written in C++ as well as new optimizations and parallel processing techniques can benefit if NPB is made fully available in this programming language. In this paper we present NPB-CPP, a fully C++ translated version of NPB consisting of all the NPB kernels and pseudo-applications developed using OpenMP, Intel TBB, and FastFlow parallel frameworks for multicores. The design of NPB-CPP leverages the Structured Parallel Programming methodology (essentially based on parallel design patterns). We show the structure of each benchmark application in terms of composition of few patterns (notably Map and MapReduce constructs) provided by the selected C++ frameworks. The experimental evaluation shows the accuracy of NPB-CPP with respect to the original NPB source code. Furthermore, we carefully evaluate the parallel performance on three multi-core systems (Intel, IBM Power and AMD) with different C++ compilers (gcc, icc and clang) by discussing the performance differences in order to give to the researchers useful insights to choose the best parallel programming framework for a given type of problem.}, keywords = {Benchmark, Parallel programming}, pubstate = {published}, tppubtype = {article} } The NAS Parallel Benchmarks (NPB), originally implemented mostly in Fortran, is a consolidated suite containing several benchmarks extracted from Computational Fluid Dynamics (CFD) models. The benchmark suite has important characteristics such as intensive memory communications, complex data dependencies, different memory access patterns, and hardware components/sub-systems overload. Parallel programming APIs, libraries, and frameworks that are written in C++ as well as new optimizations and parallel processing techniques can benefit if NPB is made fully available in this programming language. In this paper we present NPB-CPP, a fully C++ translated version of NPB consisting of all the NPB kernels and pseudo-applications developed using OpenMP, Intel TBB, and FastFlow parallel frameworks for multicores. The design of NPB-CPP leverages the Structured Parallel Programming methodology (essentially based on parallel design patterns). We show the structure of each benchmark application in terms of composition of few patterns (notably Map and MapReduce constructs) provided by the selected C++ frameworks. The experimental evaluation shows the accuracy of NPB-CPP with respect to the original NPB source code. Furthermore, we carefully evaluate the parallel performance on three multi-core systems (Intel, IBM Power and AMD) with different C++ compilers (gcc, icc and clang) by discussing the performance differences in order to give to the researchers useful insights to choose the best parallel programming framework for a given type of problem. |
Leonarczyk, Ricardo; Griebler, Dalvan Implementação MPIC++ e HPX dos Kernels NPB Inproceedings doi 21th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS), pp. 81-84, Sociedade Brasileira de Computação, Joinville, RS, Brazil, 2021. Abstract | Links | BibTeX | Tags: Benchmark, Parallel programming @inproceedings{larcc:NPB_HPX_MPI:ERAD:21, title = {Implementação MPIC++ e HPX dos Kernels NPB}, author = {Ricardo Leonarczyk and Dalvan Griebler}, url = {https://doi.org/10.5753/eradrs.2021.14780}, doi = {10.5753/eradrs.2021.14780}, year = {2021}, date = {2021-04-01}, booktitle = {21th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS)}, pages = {81-84}, publisher = {Sociedade Brasileira de Computação}, address = {Joinville, RS, Brazil}, abstract = {Este artigo apresenta a implementação paralela dos cinco kernels pertencentes ao NAS Parallel Benchmarks (NPB) com MPIC++ e HPX para execução em arquiteturas de cluster. Os resultados demonstraram que o modelo de programação HPX pode ser mais eficiente do que MPIC++ em algoritmos tais como transformada rápida de Fourier, ordenação e Gradiente Conjugado.}, keywords = {Benchmark, Parallel programming}, pubstate = {published}, tppubtype = {inproceedings} } Este artigo apresenta a implementação paralela dos cinco kernels pertencentes ao NAS Parallel Benchmarks (NPB) com MPIC++ e HPX para execução em arquiteturas de cluster. Os resultados demonstraram que o modelo de programação HPX pode ser mais eficiente do que MPIC++ em algoritmos tais como transformada rápida de Fourier, ordenação e Gradiente Conjugado. |
2020 |
Bordin, Maycon Viana; Griebler, Dalvan; Mencagli, Gabriele; Geyer, Claudio F R; Fernandes, Luiz Gustavo DSPBench: a Suite of Benchmark Applications for Distributed Data Stream Processing Systems Journal Article doi IEEE Access, 8 (na), pp. 222900-222917, 2020. Abstract | Links | BibTeX | Tags: Benchmark, Stream processing @article{BORDIN:IEEEAccess:20, title = {DSPBench: a Suite of Benchmark Applications for Distributed Data Stream Processing Systems}, author = {Maycon Viana Bordin and Dalvan Griebler and Gabriele Mencagli and Claudio F R Geyer and Luiz Gustavo Fernandes}, doi = {10.1109/ACCESS.2020.3043948}, year = {2020}, date = {2020-12-01}, journal = {IEEE Access}, volume = {8}, number = {na}, pages = {222900-222917}, publisher = {IEEE}, abstract = {Systems enabling the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders. Data Stream Processing Systems (DSPSs) are complex and powerful frameworks able to ease the development of streaming applications in distributed computing environments like clusters and clouds. Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming. Some benchmark applications have often been used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs. However, the existing benchmark suites lack of representative workloads coming from the wide set of application domains that can leverage the benefits offered by the stream processing paradigm in terms of near real-time performance. The goal of this paper is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunications, Sensor Networks, Social Networks and others. This paper describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation. In addition, it exemplifies the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis.}, keywords = {Benchmark, Stream processing}, pubstate = {published}, tppubtype = {article} } Systems enabling the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders. Data Stream Processing Systems (DSPSs) are complex and powerful frameworks able to ease the development of streaming applications in distributed computing environments like clusters and clouds. Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming. Some benchmark applications have often been used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs. However, the existing benchmark suites lack of representative workloads coming from the wide set of application domains that can leverage the benefits offered by the stream processing paradigm in terms of near real-time performance. The goal of this paper is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunications, Sensor Networks, Social Networks and others. This paper describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation. In addition, it exemplifies the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis. |
Leonarczyk, Ricardo; Griebler, Dalvan Implementação MPIC++ dos kernels NPB EP, IS e CG Inproceedings doi 20th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS), pp. 101-104, Sociedade Brasileira de Computação, Santa Maria, RS, Brazil, 2020. Abstract | Links | BibTeX | Tags: Benchmark @inproceedings{larcc:NPB_MPI:ERAD:20, title = {Implementação MPIC++ dos kernels NPB EP, IS e CG}, author = {Ricardo Leonarczyk and Dalvan Griebler}, url = {https://doi.org/10.5753/eradrs.2020.10766}, doi = {10.5753/eradrs.2020.10766}, year = {2020}, date = {2020-04-01}, booktitle = {20th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS)}, pages = {101-104}, publisher = {Sociedade Brasileira de Computação}, address = {Santa Maria, RS, Brazil}, abstract = {Este trabalho busca contribuir com prévios esforços para disponibilizar os NAS Parallel benchmarks na linguagem C++, focando-se no aspecto memória distribuída com MPI. São apresentadas implementações do CG, EP e IS portadas da versão MPI original do NPB. Os experimentos realizados demonstram que a versão proposta dos benchmarks obteve um desempenho próximo da original.}, keywords = {Benchmark}, pubstate = {published}, tppubtype = {inproceedings} } Este trabalho busca contribuir com prévios esforços para disponibilizar os NAS Parallel benchmarks na linguagem C++, focando-se no aspecto memória distribuída com MPI. São apresentadas implementações do CG, EP e IS portadas da versão MPI original do NPB. Os experimentos realizados demonstram que a versão proposta dos benchmarks obteve um desempenho próximo da original. |
Maliszewski, Anderson M; Roloff, Eduardo; Griebler, Dalvan; Navaux, Philippe O A Avaliando o Impacto da Rede no Desempenho e Custo de Execução de Aplicações HPC Inproceedings doi 20th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS), pp. 159-160, Sociedade Brasileira de Computação, Santa Maria, RS, Brazil, 2020. Abstract | Links | BibTeX | Tags: Benchmark, Cloud computing @inproceedings{larcc:network_impact:ERAD:20, title = {Avaliando o Impacto da Rede no Desempenho e Custo de Execução de Aplicações HPC}, author = {Anderson M Maliszewski and Eduardo Roloff and Dalvan Griebler and Philippe O A Navaux}, url = {https://doi.org/10.5753/eradrs.2020.10786}, doi = {10.5753/eradrs.2020.10786}, year = {2020}, date = {2020-04-01}, booktitle = {20th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS)}, pages = {159-160}, publisher = {Sociedade Brasileira de Computação}, address = {Santa Maria, RS, Brazil}, abstract = {O desempenho das aplicações HPC depende de dois componentes principais; poder de processamento e interconexão de rede. Este artigo avalia o impacto que a interconexão de rede exerce em programas paralelos usando um cluster homogêneo, em relação a desempenho e custo de execução estimado.}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {inproceedings} } O desempenho das aplicações HPC depende de dois componentes principais; poder de processamento e interconexão de rede. Este artigo avalia o impacto que a interconexão de rede exerce em programas paralelos usando um cluster homogêneo, em relação a desempenho e custo de execução estimado. |
de Araujo, Gabriell ; Griebler, Dalvan; Danelutto, Marco; Fernandes, Luiz Gustavo Efficient NAS Parallel Benchmark Kernels with CUDA Inproceedings doi 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 9-16, IEEE, Västerås, Sweden, Sweden, 2020. Abstract | Links | BibTeX | Tags: Benchmark, GPGPU @inproceedings{ARAUJO:PDP:20, title = {Efficient NAS Parallel Benchmark Kernels with CUDA}, author = {Gabriell {de Araujo} and Dalvan Griebler and Marco Danelutto and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1109/PDP50117.2020.00009}, doi = {10.1109/PDP50117.2020.00009}, year = {2020}, date = {2020-03-01}, booktitle = {28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)}, pages = {9-16}, publisher = {IEEE}, address = {Västerås, Sweden, Sweden}, series = {PDP'20}, abstract = {NAS Parallel Benchmarks (NPB) are one of the standard benchmark suites used to evaluate parallel hardware and software. There are many research efforts trying to provide different parallel versions apart from the original OpenMP and MPI. Concerning GPU accelerators, there are only the OpenCL and OpenACC available as consolidated versions. Our goal is to provide an efficient parallel implementation of the five NPB kernels with CUDA. Our contribution covers different aspects. First, best parallel programming practices were followed to implement NPB kernels using CUDA. Second, the support of larger workloads (class B and C) allow to stress and investigate the memory of robust GPUs. Third, we show that it is possible to make NPB efficient and suitable for GPUs although the benchmarks were designed for CPUs in the past. We succeed in achieving double performance with respect to the state-of-the-art in some cases as well as implementing efficient memory usage. Fourth, we discuss new experiments comparing performance and memory usage against OpenACC and OpenCL state-of-the-art versions using a relative new GPU architecture. The experimental results also revealed that our version is the best one for all the NPB kernels compared to OpenACC and OpenCL. The greatest differences were observed for the FT and EP kernels.}, keywords = {Benchmark, GPGPU}, pubstate = {published}, tppubtype = {inproceedings} } NAS Parallel Benchmarks (NPB) are one of the standard benchmark suites used to evaluate parallel hardware and software. There are many research efforts trying to provide different parallel versions apart from the original OpenMP and MPI. Concerning GPU accelerators, there are only the OpenCL and OpenACC available as consolidated versions. Our goal is to provide an efficient parallel implementation of the five NPB kernels with CUDA. Our contribution covers different aspects. First, best parallel programming practices were followed to implement NPB kernels using CUDA. Second, the support of larger workloads (class B and C) allow to stress and investigate the memory of robust GPUs. Third, we show that it is possible to make NPB efficient and suitable for GPUs although the benchmarks were designed for CPUs in the past. We succeed in achieving double performance with respect to the state-of-the-art in some cases as well as implementing efficient memory usage. Fourth, we discuss new experiments comparing performance and memory usage against OpenACC and OpenCL state-of-the-art versions using a relative new GPU architecture. The experimental results also revealed that our version is the best one for all the NPB kernels compared to OpenACC and OpenCL. The greatest differences were observed for the FT and EP kernels. |
2019 |
Maliszewski, Anderson M; Fim, Gabriel R; Maron, Carlos A F; Vogel, Adriano; Griebler, Dalvan Avaliação de Desempenho em Contêineres LXD com Aplicações Científicas na Nuvem OpenNebula Inproceedings 19th Escola Regional de Alto Desempenho da Região Sul (ERAD/RS), Sociedade Brasileira de Computação, Três de Maio, RS, Brazil, 2019. Abstract | Links | BibTeX | Tags: Benchmark, Cloud computing @inproceedings{larcc:desempenho_LXD_Opennebula:ERAD:19, title = {Avaliação de Desempenho em Contêineres LXD com Aplicações Científicas na Nuvem OpenNebula}, author = {Anderson M Maliszewski and Gabriel R Fim and Carlos A F Maron and Adriano Vogel and Dalvan Griebler}, url = {http://larcc.setrem.com.br/wp-content/uploads/2019/04/192099.pdf}, year = {2019}, date = {2019-04-01}, booktitle = {19th Escola Regional de Alto Desempenho da Região Sul (ERAD/RS)}, publisher = {Sociedade Brasileira de Computação}, address = {Três de Maio, RS, Brazil}, abstract = {As nuvens privadas IaaS podem fornecer um ambiente atrativo paraaplicações científicas. No entanto, como existem diversos modelos de implan-tação e configuração, avaliar o desempenho dessas aplicações é um desafio.Este artigo tem como objetivo avaliar o desempenho de contêineres LXD ge-renciados pelo OpenNebula, utilizando os benchmarks da suite NPB-MPI. Osresultados mostram que o LXD não induz a grandes overheads no desempenho}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {inproceedings} } As nuvens privadas IaaS podem fornecer um ambiente atrativo paraaplicações científicas. No entanto, como existem diversos modelos de implan-tação e configuração, avaliar o desempenho dessas aplicações é um desafio.Este artigo tem como objetivo avaliar o desempenho de contêineres LXD ge-renciados pelo OpenNebula, utilizando os benchmarks da suite NPB-MPI. Osresultados mostram que o LXD não induz a grandes overheads no desempenho |
Maron, Carlos A F; Vogel, Adriano; Griebler, Dalvan; Fernandes, Luiz Gustavo Should PARSEC Benchmarks be More Parametric? A Case Study with Dedup Inproceedings doi 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 217-221, IEEE, Pavia, Italy, 2019. Abstract | Links | BibTeX | Tags: Benchmark @inproceedings{MARON:parametric-parsec:PDP:19, title = {Should PARSEC Benchmarks be More Parametric? A Case Study with Dedup}, author = {Carlos A F Maron and Adriano Vogel and Dalvan Griebler and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1109/EMPDP.2019.8671592}, doi = {10.1109/EMPDP.2019.8671592}, year = {2019}, date = {2019-02-01}, booktitle = {27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)}, pages = {217-221}, publisher = {IEEE}, address = {Pavia, Italy}, series = {PDP'19}, abstract = {Parallel applications of the same domain can present similar patterns of behavior and characteristics. Characterizing common application behaviors can help for understanding performance aspects in the real-world scenario. One way to better understand and evaluate applications' characteristics is by using customizable/parametric benchmarks that enable users to represent important characteristics at run-time. We observed that parameterization techniques should be better exploited in the available benchmarks, especially on stream processing domain. For instance, although widely used, the stream processing benchmarks available in PARSEC do not support the simulation and evaluation of relevant and modern characteristics. Therefore, our goal is to identify the stream parallelism characteristics present in PARSEC. We also implemented a ready to use parameterization support and evaluated the application behaviors considering relevant performance metrics for stream parallelism (service time, throughput, latency). We choose Dedup to be our case study. The experimental results have shown performance improvements in our parameterization support for Dedup. Moreover, this support increased the customization space for benchmark users, which is simple to use. In the future, our solution can be potentially explored on different parallel architectures and parallel programming frameworks.}, keywords = {Benchmark}, pubstate = {published}, tppubtype = {inproceedings} } Parallel applications of the same domain can present similar patterns of behavior and characteristics. Characterizing common application behaviors can help for understanding performance aspects in the real-world scenario. One way to better understand and evaluate applications' characteristics is by using customizable/parametric benchmarks that enable users to represent important characteristics at run-time. We observed that parameterization techniques should be better exploited in the available benchmarks, especially on stream processing domain. For instance, although widely used, the stream processing benchmarks available in PARSEC do not support the simulation and evaluation of relevant and modern characteristics. Therefore, our goal is to identify the stream parallelism characteristics present in PARSEC. We also implemented a ready to use parameterization support and evaluated the application behaviors considering relevant performance metrics for stream parallelism (service time, throughput, latency). We choose Dedup to be our case study. The experimental results have shown performance improvements in our parameterization support for Dedup. Moreover, this support increased the customization space for benchmark users, which is simple to use. In the future, our solution can be potentially explored on different parallel architectures and parallel programming frameworks. |
Maliszewski, Anderson M; Griebler, Dalvan Avaliação de Desempenho da Agregação de Interfaces de Rede em Ambientes de Nuvem Privada HiPerfCloud: High Performance in Cloud Technical Report doi Laboratory of Advanced Research on Cloud Computing (LARCC) 2019. Links | BibTeX | Tags: Benchmark, Cloud computing @techreport{larcc:rt5:19, title = {Avaliação de Desempenho da Agregação de Interfaces de Rede em Ambientes de Nuvem Privada HiPerfCloud: High Performance in Cloud}, author = {Anderson M Maliszewski and Dalvan Griebler}, url = {http://larcc.setrem.com.br/wp-content/uploads/2019/12/LARCC_HiPerfCloud_RT5_2019.pdf}, doi = {10.13140/RG.2.2.14800.87044}, year = {2019}, date = {2019-01-01}, institution = {Laboratory of Advanced Research on Cloud Computing (LARCC)}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {techreport} } |
2018 |
Klein, Maikel; Maliszewski, Anderson Mattheus; Griebler, Dalvan Avaliação do Desempenho do Protocolo Bonding em Máquinas Virtuais LXC e KVM Inproceedings 15th Escola Regional de Redes de Computadores (ERRC), pp. 1-8, Sociedade Brasileira de Computação, Pelotas, BR, 2018. Abstract | Links | BibTeX | Tags: Benchmark, Cloud computing @inproceedings{larcc:link_agreggation:ERRC:18, title = {Avaliação do Desempenho do Protocolo Bonding em Máquinas Virtuais LXC e KVM}, author = {Maikel Klein and Anderson Mattheus Maliszewski and Dalvan Griebler}, url = {http://larcc.setrem.com.br/wpcontent/uploads/2018/11/ERRC_2018__Link_Aggregation_.pdf}, year = {2018}, date = {2018-07-01}, booktitle = {15th Escola Regional de Redes de Computadores (ERRC)}, pages = {1-8}, publisher = {Sociedade Brasileira de Computação}, address = {Pelotas, BR}, abstract = {O processamento de grandes volumes de dados (Big Data) e seu armazenamento distribuído vem aumentado gradualmente o uso da rede. Com isso, torna-se necessário o uso de tecnologias para otimizar a largura de banda. Uma das soluções de baixo custo e fácil implementação é a agregação de link. Além disso, a virtualização, usada como base na computação em nuvem, oferece vários benefícios utilizados no Big Data. O objetivo deste trabalho é avaliar o desempenho de rede usando a agregação de link com o protocolo bonding em máquinas virtuais LXC e KVM. Os resultados mostram que o protocolo bonding tem comportamento similar com ambos tipos de virtualização.}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {inproceedings} } O processamento de grandes volumes de dados (Big Data) e seu armazenamento distribuído vem aumentado gradualmente o uso da rede. Com isso, torna-se necessário o uso de tecnologias para otimizar a largura de banda. Uma das soluções de baixo custo e fácil implementação é a agregação de link. Além disso, a virtualização, usada como base na computação em nuvem, oferece vários benefícios utilizados no Big Data. O objetivo deste trabalho é avaliar o desempenho de rede usando a agregação de link com o protocolo bonding em máquinas virtuais LXC e KVM. Os resultados mostram que o protocolo bonding tem comportamento similar com ambos tipos de virtualização. |
Maliszewski, Anderson M; Griebler, Dalvan; Schepke, Claudio; Ditter, Alexander; Fey, Dietmar; Fernandes, Luiz Gustavo The NAS Benchmark Kernels for Single and Multi-Tenant Cloud Instances with LXC/KVM Inproceedings doi International Conference on High Performance Computing & Simulation (HPCS), pp. 359-366, IEEE, Orleans, France, 2018. Abstract | Links | BibTeX | Tags: Benchmark, Cloud computing @inproceedings{larcc:NAS_cloud_LXC_KVM:HPCS:2018, title = {The NAS Benchmark Kernels for Single and Multi-Tenant Cloud Instances with LXC/KVM}, author = {Anderson M Maliszewski and Dalvan Griebler and Claudio Schepke and Alexander Ditter and Dietmar Fey and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1109/HPCS.2018.00066}, doi = {10.1109/HPCS.2018.00066}, year = {2018}, date = {2018-07-01}, booktitle = {International Conference on High Performance Computing & Simulation (HPCS)}, pages = {359-366}, publisher = {IEEE}, address = {Orleans, France}, series = {HPCS'18}, abstract = {Private IaaS clouds are an attractive environment for scientific workloads and applications. It provides advantages such as almost instantaneous availability of high-performance computing in a single node as well as compute clusters, easy access for researchers, and users that do not have access to conventional supercomputers. Furthermore, a cloud infrastructure provides elasticity and scalability to ensure and manage any software dependency on the system with no third-party dependency for researchers. However, one of the biggest challenges is to avoid significant performance degradation when migrating these applications from physical nodes to a cloud environment. Also, we lack more research investigations for multi-tenant cloud instances. In this paper, our goal is to perform a comparative performance evaluation of scientific applications with single and multi-tenancy cloud instances using KVM and LXC virtualization technologies under private cloud conditions. All analyses and evaluations were carried out based on NAS Benchmark kernels to simulate different types of workloads. We applied statistic significance tests to highlight the differences. The results have shown that applications running on LXC-based cloud instances outperform KVM-based cloud instances in 93.75% of the experiments w.r.t single tenant. Regarding multi-tenant, LXC instances outperform KVM instances in 45% of the results, where the performance differences were not as significant as expected.}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {inproceedings} } Private IaaS clouds are an attractive environment for scientific workloads and applications. It provides advantages such as almost instantaneous availability of high-performance computing in a single node as well as compute clusters, easy access for researchers, and users that do not have access to conventional supercomputers. Furthermore, a cloud infrastructure provides elasticity and scalability to ensure and manage any software dependency on the system with no third-party dependency for researchers. However, one of the biggest challenges is to avoid significant performance degradation when migrating these applications from physical nodes to a cloud environment. Also, we lack more research investigations for multi-tenant cloud instances. In this paper, our goal is to perform a comparative performance evaluation of scientific applications with single and multi-tenancy cloud instances using KVM and LXC virtualization technologies under private cloud conditions. All analyses and evaluations were carried out based on NAS Benchmark kernels to simulate different types of workloads. We applied statistic significance tests to highlight the differences. The results have shown that applications running on LXC-based cloud instances outperform KVM-based cloud instances in 93.75% of the experiments w.r.t single tenant. Regarding multi-tenant, LXC instances outperform KVM instances in 45% of the results, where the performance differences were not as significant as expected. |
Griebler, Dalvan; Vogel, Adriano; Maron, Carlos A F; Maliszewski, Anderson M; Schepke, Claudio; Fernandes, Luiz Gustavo Performance of Data Mining, Media, and Financial Applications under Private Cloud Conditions Inproceedings doi IEEE Symposium on Computers and Communications (ISCC), pp. 1530-1346, IEEE, Natal, Brazil, 2018. Abstract | Links | BibTeX | Tags: Benchmark, Cloud computing @inproceedings{larcc:parsec_cloudstack_lxc_kvm:ISCC:2018, title = {Performance of Data Mining, Media, and Financial Applications under Private Cloud Conditions}, author = {Dalvan Griebler and Adriano Vogel and Carlos A F Maron and Anderson M Maliszewski and Claudio Schepke and Luiz Gustavo Fernandes}, url = {https://dx.doi.org/10.1109/ISCC.2018.8538759}, doi = {10.1109/ISCC.2018.8538759}, year = {2018}, date = {2018-06-01}, booktitle = {IEEE Symposium on Computers and Communications (ISCC)}, pages = {1530-1346}, publisher = {IEEE}, address = {Natal, Brazil}, series = {ISCC'18}, abstract = {This paper contributes to a performance analysis of real-world workloads under private cloud conditions. We selected six benchmarks from PARSEC related to three mainstream application domains (financial, data mining, and media processing). Our goal was to evaluate these application domains in different cloud instances and deployment environments, concerning container or kernel-based instances and using dedicated or shared machine resources. Experiments have shown that performance varies according to the application characteristics, virtualization technology, and cloud environment. Results highlighted that financial, data mining, and media processing applications running in the LXC instances tend to outperform KVM when there is a dedicated machine resource environment. However, when two instances are sharing the same machine resources, these applications tend to achieve better performance in the KVM instances. Finally, financial applications achieved better performance in the cloud than media and data mining.}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {inproceedings} } This paper contributes to a performance analysis of real-world workloads under private cloud conditions. We selected six benchmarks from PARSEC related to three mainstream application domains (financial, data mining, and media processing). Our goal was to evaluate these application domains in different cloud instances and deployment environments, concerning container or kernel-based instances and using dedicated or shared machine resources. Experiments have shown that performance varies according to the application characteristics, virtualization technology, and cloud environment. Results highlighted that financial, data mining, and media processing applications running in the LXC instances tend to outperform KVM when there is a dedicated machine resource environment. However, when two instances are sharing the same machine resources, these applications tend to achieve better performance in the KVM instances. Finally, financial applications achieved better performance in the cloud than media and data mining. |
Rockenbach, Dinei A; Anderle, Nadine; Griebler, Dalvan; Souza, Samuel Estudo Comparativo de Bancos de Dados NoSQL Journal Article doi Revista Eletrônica Argentina-Brasil de Tecnologias da Informação e da Comunicação (REABTIC), 1 (8), 2018. Abstract | Links | BibTeX | Tags: Benchmark, Databases, NoSQL databases @article{larcc:comparativo_nosql:REABTIC:18, title = {Estudo Comparativo de Bancos de Dados NoSQL}, author = {Dinei A Rockenbach and Nadine Anderle and Dalvan Griebler and Samuel Souza}, url = {https://revistas.setrem.com.br/index.php/reabtic/article/view/286}, doi = {10.5281/zenodo.1228503}, year = {2018}, date = {2018-04-01}, journal = {Revista Eletrônica Argentina-Brasil de Tecnologias da Informação e da Comunicação (REABTIC)}, volume = {1}, number = {8}, publisher = {SETREM}, address = {Três de Maio, RS, Brazil}, abstract = {NoSQL databases emerged to fill limitations of the relational databases. The many options for each one of the categories, and their distinct characteristics and focus makes this assessment very difficult for decision makers. Most of the time, decisions are taken without the attention and background deserved due to the related complexities. This article aims to compare the relevant characteristics of each database, abstracting the information that bases the market marketing of them. We concluded that although the databases are labeled in a specific category, there is a significant disparity in the functionalities offered by each of them. Also, we observed that new databases are emerging even though there are well-established databases in each one of the categories studied. Finally, it is very challenging to suggest the best database for each category because each scenario has its requirements, which requires a careful analysis where our work can help to simplify this kind of decision.}, keywords = {Benchmark, Databases, NoSQL databases}, pubstate = {published}, tppubtype = {article} } NoSQL databases emerged to fill limitations of the relational databases. The many options for each one of the categories, and their distinct characteristics and focus makes this assessment very difficult for decision makers. Most of the time, decisions are taken without the attention and background deserved due to the related complexities. This article aims to compare the relevant characteristics of each database, abstracting the information that bases the market marketing of them. We concluded that although the databases are labeled in a specific category, there is a significant disparity in the functionalities offered by each of them. Also, we observed that new databases are emerging even though there are well-established databases in each one of the categories studied. Finally, it is very challenging to suggest the best database for each category because each scenario has its requirements, which requires a careful analysis where our work can help to simplify this kind of decision. |
Maron, Carlos A F; Vogel, Adriano; Griebler, Dalvan Caracterizando a Implantação e o Desempenho de Aplicações em Ambientes de Nuvem Privada com Recursos Compartilhados e Dedicados Technical Report doi Laboratory of Advanced Research on Cloud Computing (LARCC) 2018. Links | BibTeX | Tags: Benchmark, Cloud computing @techreport{larcc:rt4:18, title = {Caracterizando a Implantação e o Desempenho de Aplicações em Ambientes de Nuvem Privada com Recursos Compartilhados e Dedicados}, author = {Carlos A F Maron and Adriano Vogel and Dalvan Griebler}, url = {http://larcc.setrem.com.br/wp-content/uploads/2018/12/LARCC_HiPerfCloud_RT4_2017.pdf}, doi = {10.13140/RG.2.2.14176.74240}, year = {2018}, date = {2018-01-01}, institution = {Laboratory of Advanced Research on Cloud Computing (LARCC)}, keywords = {Benchmark, Cloud computing}, pubstate = {published}, tppubtype = {techreport} } |