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Dalvan Griebler
Associate Professor and Computer Scientist

Dalvan Griebler

I am an Associate Professor and Computer Scientist at the Pontifical Catholic University of Rio Grande do Sul (PUCRS), Brazil. I received a Ph.D. in computer science from PUCRS and the University of Pisa (UNIPI) in 2016. I have been affiliated with the School of Technology at PUCRS since 2021, lecturing in the Computer Science and Data Science Bachelor courses. I also supervise academic Master's and Ph.D. students from the Computer Sciences Graduate Program (PPG-CC) and the Ecology and Evolution of Biodiversity Graduate Program (PPG-EEB). I am coordinating the Artificial Intelligence and Data Science Center (CIACD), natural language processing group (PLN), parallel application modeling group (GMAP), and the laboratory of artificial intelligence (Lab AI). I have been developing research projects with industry partners and governmental agencies. In the academic community, I act on the editorial board of scientific journals, on technical program committees of conferences, and as a reviewer of scientific journals. As a result of my research work, open-source software is made publicly available. In the past, I was an associate professor at the Três de Maio Educational Society (Setrem) from 2012 to 2023, where I created both the Laboraty of Advanced Research on Cloud Computing (LARCC), coordinating it form 2014 to 2023, and a Professional Master in Cloud Computing (PMCC), coordinating it from 2019 to 2023.

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Research Interests

Data science

Big data, data stream, data engineering, data analytics, time series, neural networks, big data frameworks (Hadoop, Flink, and Spark, Storm, and Kafka), and machine learning framework (Torch, Keras, and TensorFlow).

Internet of Things

Agriculture, biology, health care, industry, stream and complex event processing, mobile and embedded systems.

Parallel programming

Parallel Patterns, Algorithmic Skeletons, Task Scheduling, TBB, FastFlow, OpenMP, MPI, HPX, CUDA, OpenCL, and OpenACC.

Programming Languages

C/C++, Python, Shell Script, Rust, Java

Parallel Programming Effort

Parallelism abstractions, high-level parallel programming, programming effort, coding productivity​, metrics, and methodologies.

Cloud computing

IaaS cloud platforms (OpenStack, OpenNebula, and CloudStack), performance evaluation, deployment optimizations (virtualization, containers, networking, storage), virtualization technologies (KVM, Docker, and LXD), and PaaS platforms (AWS, Azure, GCP).

Parallel and Distributed Systems

Benchmarking, distributed platforms, high availability, fault tolerance, resource management, and energy efficiency.

High-Performance Architectures

Cluster computing, cloud infrastructure, multi-core systems, and hardware accelerators (GPU, FPGA).

Computer Networking

Application protocol, high-speed network, virtual network, and cloud network tools.

Compilers and Optimizations

Parallel code generation, code autotuning, source-to-source transformation, static code analysis. Flex, Bison, ANTLR, Clang, and GCC Plugins.

Latest Publications

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