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| September,2023 | |
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19 Sep 9:00 am 10:30 amMSC1090 lecture 3The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
21 Sep 9:00 am 10:30 amMSC1090 lecture 4The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
25 Sep 12:30 pm 2:00 pmIntro to Supercomputing, session 1/3An introduction to basic concepts in High-Performance Computing (HPC). This is intended to be a high-level primer for those largely new to HPC. Topic will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism and a high-level overview of parallel programming models.Format: Virtual | HPC101 - Sep 2023 |
26 Sep 9:00 am 10:30 amMSC1090 lecture 5The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
27 Sep 12:30 pm 2:00 pmIntro to Supercomputing, session 2/3An introduction to basic concepts in High-Performance Computing (HPC). This is intended to be a high-level primer for those largely new to HPC. Topic will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism and a high-level overview of parallel programming models.Format: Virtual | HPC101 - Sep 2023 |
28 Sep 9:00 am 10:30 amMSC1090 lecture 6The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
29 Sep 12:30 pm 2:00 pmIntro to Supercomputing, session 3/3An introduction to basic concepts in High-Performance Computing (HPC). This is intended to be a high-level primer for those largely new to HPC. Topic will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism and a high-level overview of parallel programming models.Format: Virtual | HPC101 - Sep 2023 |
| October,2023 | |
3 Oct 9:00 am 10:30 amMSC1090 lecture 7The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
3 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
5 Oct 9:00 am 10:30 amMSC1090 lecture 8The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
5 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
10 Oct 9:00 am 10:30 amMSC1090 lecture 9The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
10 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
11 Oct 12:00 pm 1:00 pmCO Colloquium: High-Performance Computing in RIn the world where data has become extremely important, scientists require tools to process large volumes of data efficiently. R became increasingly popular in recent years for data processing, statistical analysis, and data science. In this session we will discuss tools that measure the performance of an R code, so that we can understand the nature of performance issues. We will also describe techniques that will improve the computational speed of R code. Basic knowledge of programming in R will be assumed. On-line | COCO - 6 Dec 2023 |
11 Oct 12:00 pm 1:00 pmCO Colloquium: High-Performance Computing in RIn the world where data has become extremely important, scientists require tools to process large volumes of data efficiently. R became increasingly popular in recent years for data processing, statistical analysis, and data science. In this session we will discuss tools that measure the performance of an R code, so that we can understand the nature of performance issues. We will also describe techniques that will improve the computational speed of R code. Basic knowledge of programming in R will be assumed. On-line | COCO - 25 Oct 2023 |
11 Oct 12:00 pm 1:00 pmCO Colloquium: High-Performance Computing in RIn the world where data has become extremely important, scientists require tools to process large volumes of data efficiently. R became increasingly popular in recent years for data processing, statistical analysis, and data science. In this session we will discuss tools that measure the performance of an R code, so that we can understand the nature of performance issues. We will also describe techniques that will improve the computational speed of R code. Basic knowledge of programming in R will be assumed. On-line | COCO - 11 Oct 2023 |
11 Oct 12:00 pm 1:00 pmCO Colloquium: High-Performance Computing in RIn the world where data has become extremely important, scientists require tools to process large volumes of data efficiently. R became increasingly popular in recent years for data processing, statistical analysis, and data science. In this session we will discuss tools that measure the performance of an R code, so that we can understand the nature of performance issues. We will also describe techniques that will improve the computational speed of R code. Basic knowledge of programming in R will be assumed. On-line | COCO - 8 Nov 2023 |
11 Oct 12:00 pm 1:00 pmCO Colloquium: High-Performance Computing in RIn the world where data has become extremely important, scientists require tools to process large volumes of data efficiently. R became increasingly popular in recent years for data processing, statistical analysis, and data science. In this session we will discuss tools that measure the performance of an R code, so that we can understand the nature of performance issues. We will also describe techniques that will improve the computational speed of R code. Basic knowledge of programming in R will be assumed. On-line | COCO - 22 Nov 2023 |
11 Oct 1:00 pm 2:30 pmIntro to NiagaraIn about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers.Format: Virtual VIrtual | HPC105 - Oct 2023 |
12 Oct 9:00 am 10:30 amMSC1090 lecture 10The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
12 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
16 Oct 1:00 pm 4:00 pmIntro to ApptainerContainer computing is gradually changing the way researchers are developing, sharing, and running software applications. Apptainer (formerly called Singularity) is gaining popularity in HPC for its performance, ease of use, portability, and security. In this course, we will explore: what is a container, why use a container, and how to use and create one.Format: Virtual | SCMP161 - Oct 2023 |
17 Oct 9:00 am 10:30 amMSC1090 lecture 11The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
17 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
19 Oct 9:00 am 10:30 amMSC1090 lecture 12The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
19 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
24 Oct 9:00 am 10:30 amMSC1090 lecture 13The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085. Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program. SS1085 | MSC1090 - Fall 2023 |
24 Oct 1:00 pm 2:00 pmIntro to Programming SessionNew to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.Format: In-person. Sessions will be recorded. SciNet Teaching Room | SCMP142 - Oct 2023 |
25 Oct 12:00 pm 1:00 pmCO Colloquium: SWIFT: A Modern Highly Parallel Gravity and Smoothed Particle Hydrodynamics SolverNumerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this talk, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with ≈300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift. Virtual | COCO - 8 Nov 2023 |
25 Oct 12:00 pm 1:00 pmCO Colloquium: SWIFT: A Modern Highly Parallel Gravity and Smoothed Particle Hydrodynamics SolverNumerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this talk, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with ≈300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift. Virtual | COCO - 25 Oct 2023 |

