Scientific Computing, High-Performance Computing and Data Science in Higher Education

Ponce, Marcelo; Spence, E. J.; Grüner, Daniel; Zon, Ramses van

doi:10.22369/issn.2153-4136/10/1/5

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…One might wonder whether or not this kind of training should be part of the graduate curriculum [10], or whether perhaps it should be provided by the Computer Science department. While parallel computing and concurrency are active lines of research in computer science, users are often interested in practical techniques and desire training in efficient ways to enable larger scientific computations.…”

Section: Discussionmentioning

confidence: 99%

Trends in Demand, Growth, and Breadth in Scientific Computing Training Delivered by a High-Performance Computing Center

Zon

Ponce

Spence

et al. 2019

JOCSE

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We analyze the changes in the training and educational efforts of the SciNet HPC Consortium, a Canadian academic High Performance Computing center, in the areas of Scientific Computing and High-Performance Computing, over the last six years. Initially, SciNet offered isolated training events on how to use HPC systems and write parallel code, but the training program now consists of a broad range of workshops and courses that users can take toward certificates in scientific computing, data science, or high-performance computing. Using data on enrollment, attendence, and certificate numbers from SciNet's education website, used by almost 1800 users so far, we extract trends on the growth, demand, and breadth of SciNet's training program. Among the results are a steady overall growth, a sharp and steady increase in the demand for data science training, and a wider participation of 'non-traditional' computing disciplines, which has motivated an increasingly broad spectrum of training offerings. Of interest is also that many of the training initiatives have evolved into courses that can be taken as part of the graduate curriculum at the University of Toronto.

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Section: Discussionmentioning

confidence: 99%

Trends in Demand, Growth, and Breadth in Scientific Computing Training Delivered by a High-Performance Computing Center

Zon

Ponce

Spence

et al. 2019

JOCSE

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“…A third demonstration of the room's capabilities are in usage as a host site in a remote instruction event. We should also note that the teaching/visualization room is actively used in the education and training program of SciNet [22]. While most events in this program are face-to-face, lectures are broadcast, recorded and posted on-line as well on our education website [5].…”

Section: Demonstrationsmentioning

confidence: 99%

Software-Enhanced Teaching and Visualization Capabilities of an Ultra-High-Resolution Video Wall

Zon

Ponce

2019

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning)

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This paper presents a modular approach to enhance the capabilities and features of a visualization and teaching room using software. This approach was applied to a room with a large, high resolution (7680×4320 pixels), tiled screen of 13 × 7.5 feet as its main display, and with a variety of audio and video inputs, connected over a network. Many of the techniques described are possible because of a software-enhanced setup, utilizing existing hardware and a collection of mostly open-source tools, allowing to perform collaborative, high-resolution visualizations as well as broadcasting and recording workshops and lectures. The software approach is flexible and allows one to add functionality without changing the hardware.

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“…• Burrows-Wheeler Alignment (BWA) version 0.7.13 [12] • Sequence Alignment/Map (SAMtools) version 1.3.1 [13] • the R statistical language [14] Our pipeline is open source, and the scripts are available to download and accessibles from any of the following repositories [2] : https://gitrepos.scinet.utoronto.ca/public/?a=summary&p=RACS or https://bitbucket.org/mjponce/racs The repository includes the core or main scripts placed in the "core" directory. The comparison and auxilary tools are placed in a "tools" directory.…”

Section: Racs Pipeline Implementationmentioning

confidence: 99%

“…[1] Alternatively, we have also included an auxiliary spreadsheet that could be used instead of the script to perform the post-processing and normalization manually, as well as, to check against negative controls. [2] Both repositories are synchronized, so that the latest version of RACS is available and can be obtained from both of them.…”

Section: Racs Pipeline Implementationmentioning

confidence: 99%

“…In the last few years the traditional High Performance Computing (HPC) centres, such as SciNet at the University of Toronto [1], have been witnessing the emergence of increasing amounts of work-flows from non-typical disciplines in the field of computational science [2]. Among those, disciplines related to bioinformatics appear to be the most prominent in terms of demanding resources and tackling complex problems related to Next Generation Sequencing (NGS).…”

Section: Introductionmentioning

confidence: 99%

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RACS: Rapid Analysis of ChIP-Seq data for contig based genomes

Ponce,

Saettone,

Nabeel-Shah

et al. 2019

Preprint

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Background: Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-Seq) is a widely used technique to investigate the function of chromatin-related proteins in a genome-wide manner. ChIP-Seq generates large quantities of data which can be difficult to process and analyse, particularly for organisms with contig based genomes. Contig-based genomes often have poor annotations for cis-elements, for example enhancers, that are important for gene expression. Poorly annotated genomes make a comprehensive analysis of ChIP-Seq data difficult and as such standardized analysis pipelines are lacking.Methods: We report a computational pipeline that utilizes traditional High-Performance Computing techniques and open source tools for processing and analysing data obtained from ChIP-Seq.We applied our computational pipeline "Rapid Analysis of ChIP-Seq data" (RACS) to ChIP-Seq data that was generated in the model organism Tetrahymena thermophila, an example of an organism with a genome that is available in contigs.Results: To test the performance and efficiency of RACs, we performed control ChIP-Seq experiments allowing us to rapidly eliminate false positives when analyzing our previously published data set. Our pipeline segregates the found read accumulations between genic and intergenic regions and is highly efficient for rapid downstream analyses.Conclusions: Altogether, the computational pipeline presented in this report is an efficient and highly reliable tool to analyze genome-wide ChIP-Seq data generated in model organisms with contig-based genomes.

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Scientific Computing, High-Performance Computing and Data Science in Higher Education

Cited by 7 publications

References 9 publications

Trends in Demand, Growth, and Breadth in Scientific Computing Training Delivered by a High-Performance Computing Center

Trends in Demand, Growth, and Breadth in Scientific Computing Training Delivered by a High-Performance Computing Center

Software-Enhanced Teaching and Visualization Capabilities of an Ultra-High-Resolution Video Wall

RACS: Rapid Analysis of ChIP-Seq data for contig based genomes

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