Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Last revision Both sides next revision | ||
|
projetos:printcap [2021/11/05 14:55] karine [Janeiro - 2020] |
projetos:printcap [2021/11/05 14:56] karine [2020] |
||
|---|---|---|---|
| Line 134: | Line 134: | ||
| * Type of event: Seminário | * Type of event: Seminário | ||
| * Title: Reproducible Data Science with Gigantum | * Title: Reproducible Data Science with Gigantum | ||
| - | This talk presents the Gigantum open-source data science work environment that automates the best practices and skill-intensive tasks that are crucial to good data science. The data scientist works in familiar tools, such as RStudio and Jupyter and Gigantum makes sure that all aspects of a data science project--code, data, and environment--portable, shareable, and continuously versioned. Gigantum runs on locally (on laptops) as well as the cloud so that the data scientist can work without incurring cloud computing costs. Users can collaborate in groups or on public projects, exploring by launching on the cloud, contributing in their own branch, or customizing with new code or private data in their own fork. | ||
| * Other infos (links, documents, fotos, etc.): | * Other infos (links, documents, fotos, etc.): | ||
| https://1drv.ms/b/s!AlgXW7dbUWkTvYEsLpigXo4FxvQmcg?e=iclyp5 | https://1drv.ms/b/s!AlgXW7dbUWkTvYEsLpigXo4FxvQmcg?e=iclyp5 | ||
| Line 143: | Line 142: | ||
| * Type of event: Seminário | * Type of event: Seminário | ||
| * Title: External Memory Systems for Data Science and Machine Learning | * Title: External Memory Systems for Data Science and Machine Learning | ||
| - | In modern computer architectures, the movement of data from storage or memory to the processor limits the performance and scale of scientific data analysis and machine learning. This bottleneck has grown much more acute as we use multicore processors and GPUs. This talk covers a decade of research in my lab that redesigned computer systems to move data through the memory hierarchy and applied these systems to make data science (graph analytics and sparse linear algebra) and machine learning (k-means and random forests) more efficient. | ||
| - | The lecture is self-contained and designed for the computational scientist that is familiar with using data science and machine learning programming tools. It will lightly review computer science concepts, including the memory hierarchy, external memory algorithms, and non-uniform memory architectures. | ||
| * Other infos (links, documents, fotos, etc.): | * Other infos (links, documents, fotos, etc.): | ||
| * Date: **13 de Janeiro de 2020** | * Date: **13 de Janeiro de 2020** | ||
