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February,2024 | |
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14 Feb 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
14 Feb 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 - Feb 2024![]() |
15 Feb 11:00 am 12:00 pmEES1137: Lecture 12In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
15 Feb 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
21 Feb 12:00 pm 1:00 pmCompute Ontario Colloquium: Multi-Factor Authentication (Marco Saldarriaga, SciNet)Multi-factor authentication, MFA, two-factor authentication, or 2FA, along with similar terms, is an electronic authentication method in which a user is granted access to a device, website or application only after successfully presenting two or more pieces of evidence (or factors) to an authentication mechanism: MFA protects user data—which may include personal identification or financial assets from being accessed by an unauthorized third party that may have been able to discover, for example, a single password. We will explain the most common uses of MFA today, and how MFA is being implemented in our environment. Online | COCO - 21 Feb 2024![]() |
23 Feb 1:00 pm 4:00 pmIntro to Linux Command LineWorking with many of the HPC systems (like those at SciNet) involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course which will cover basic commands. It could be a great boon for your productivity.Format: Virtual Virtual | SCMP101 - Feb 2024![]() |
26 Feb 12:30 pm 2:00 pmGPU Programming 1/3An overview of GPUs and their use in supercomputers. This workshop will explain what GPUs are, and cover the basic ideas of GPU use in scientific computing. We will introduce several GPU programming frameworks, and demonstrate how to accelerate a solution of a science problem using a GPU. Python or C++ could be used for the assignment.Format: Virtual | HPC133 - Feb 2024 |
27 Feb 10:00 am 12:00 pmEES1137: Lecture 13In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
27 Feb 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
28 Feb 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
28 Feb 12:30 pm 2:00 pmGPU Programming 2/3An overview of GPUs and their use in supercomputers. This workshop will explain what GPUs are, and cover the basic ideas of GPU use in scientific computing. We will introduce several GPU programming frameworks, and demonstrate how to accelerate a solution of a science problem using a GPU. Python or C++ could be used for the assignment.Format: Virtual | HPC133 - Feb 2024 |
29 Feb 11:00 am 12:00 pmEES1137: Lecture 14In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
29 Feb 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
March,2024 | |
1 Mar 12:30 pm 2:00 pmGPU Programming 3/3An overview of GPUs and their use in supercomputers. This workshop will explain what GPUs are, and cover the basic ideas of GPU use in scientific computing. We will introduce several GPU programming frameworks, and demonstrate how to accelerate a solution of a science problem using a GPU. Python or C++ could be used for the assignment.Format: Virtual | HPC133 - Feb 2024 |
4 Mar 1:00 pm 2:30 pmIntro to OpenMPLearn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing.Format: Virtual Virtual | HPC113 - Mar 2024![]() |
5 Mar 10:00 am 12:00 pmEES1137: Lecture 15In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
5 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
6 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
7 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
7 Mar 11:00 am 12:00 pmEES1137: Lecture 16In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
11 Mar 9:00 am2D diffusion equation is dueThe assignment is to numerically solve the diffusion (heat) equationin two dimensions, using GPU acceleration, in either Python or C++. You can find serial, CPU-based solutions in both languages in the course’s source tarball. If you choose Python, you can follow the instructions on the appropriate slide in the handouts, titled Setting up the environment (Python). If you want graphics on Mist, please also install the matplotlib package in your Conda environment using conda install (no need on Graham as the package is provided by the scipy-stack module). You could modify the file diff2d.py such that the bulk of the calculation (within the time loop) is done using the GPU. You can follow the gravitational potential calculation example shown in class, Numba and/or CuPy can be used in the solution. Note that the sample solution in Python is equivalent to the “naïve” solution for the gravitational potential problem, therefore very bad. If you choose C++, we count on you being familiar with how to compile source code. You could modify the file diff2d.cpp such that the bulk of the calculation (within the time loop) is done using the GPU. On both Mist and Graham, load the following modules: cuda, gcc, pgplot (you may skip PGPLOT but then please remove the plotting calls from the main source file and do not compile diff2dplot.cpp). If you choose to work with HIP instead of CUDA, on Mist you can load the hip module in addition to the cuda module (which is still necessary since HIP uses the CUDA compiler under the hood when compiling for Nvidia GPUs). HIP is not currently available on Graham, but you can try to install it locally there. The sample C++ code uses rarray, you can install it locally or just pull the header. The usual suffix of CUDA files is .cu and the nvcc command is used to compile the source (instead of g++ for example). You can keep the .cpp suffix, but then have to pass --x=cu to nvcc (this is not recommended for files that contains a kernel launch with triple angle brackets, as that is not legal C++ syntax). The usual suffix of HIP files is just .cpp, and the hipcc command is used to compile. GPU kernels can be in the same file where the main function is, as we saw in the vector addition example, but in more complex applications the GPU code (including kernel launches) would generally be separated out to one or more compilation units that are linked to the rest of the code during the build process. The Mist login node has four GPUs that can be used for the assignment, on Graham one has to submit a job to the scheduler. Unlike for submitted jobs, there is no guarantee that a GPU on the Mist login node would be free when you launch your application, as the node is shared by everyone. You could use the nvidia-smi command to see the current usage of the GPUs. By default, the first device (number 0) is used, but this behaviour can be change by setting an environment variable as shown below. For example, if you want to use device number 1: CUDA_VISIBLE_DEVICES=1 python code.py There are three “bonus” tasks that you can try for your own amusement (2 & 3 are beyond the scope of this workshop): The smaller Δx, the more accurate and computationally heavy the solution. Plot the timing for your solution and of the serial CPU-based solution (and possibly improved CPU-based solutions) as a function of Δx. Decompose the domain and solve the problem with multiple GPUs on the same node. Use a distributed memory library to deploy your solution on multiple nodes. Hint: for a single node you could use multiprocessing in Python and thread or OpenMP in C++. For multiple nodes you could use mpi4py (Python) or MPI (C++). | HPC133 - Feb 2024 |
12 Mar 10:00 am 12:00 pmEES1137: Lecture 17In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
12 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
13 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
13 Mar 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 - Mar 2024![]() |
14 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
14 Mar 11:00 am 12:00 pmEES1137: Lecture 18In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
19 Mar 10:00 am 12:00 pmEES1137: Lecture 19In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
19 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
20 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
21 Mar 11:00 am 12:00 pmEES1137: Lecture 20In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
21 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
25 Mar 1:00 pm 4:00 pmParallel Debugging with DDTDebugging is an important step in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code and debugging of parallel (MPI and threaded) codes using DDT. Virtual | HPC245 - Mar 2024![]() |
26 Mar 10:00 am 12:00 pmEES1137: Lecture 21In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
26 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
27 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
28 Mar 11:00 am 12:00 pmEES1137: Lecture 22In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
28 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
April,2024 | |
2 Apr 10:00 am 12:00 pmEES1137: Lecture 23In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024![]() |
2 Apr 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
4 Apr 11:00 am 12:00 pmEES1137: Lecture 24In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024![]() |
4 Apr 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
10 Apr 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
10 Apr 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 - Apr 2024![]() |
15 Apr 1:00 pm 4:00 pmShell ScriptingLearn how to write bash scripts, use environment variables, how to control process, and much more. Requires some Linux basic command line experience.Note: this event has been moved from April 8th to April 15th.Format: Virtual Virtual | SCMP201 - Apr 2024![]() |
17 Apr 12:00 pm 1:00 pmCO Colloquium "How to Buy a Supercomputer for Scientific Computing"Buying a new supercomputer that both maximises total performance, given our budget, and whose architecture suits our users' workloads is a very difficult balancing act. There are a wide range of decisions to be made, such as: CPU architecture; node count; memory size/bandwidth; GPU count; interconnect type; storage size; filesystem type/bandwidth; cooling type and power budget to name but a few. In order to balance all of these constraints we need to come up with a scoring system to compare potential candidate supercomputers. In this talk we describe the Scalable System Improvement (SSI) metric and apply it to the system refresh of Niagara & Mist. Virtual | COCO - 17 Apr 2024![]() |
23 Apr 11:00 am 12:00 pmDAT112: Lecture 1Introduction to neural network programming, lecture 1 | DAT112 - Apr 2024 |
25 Apr 11:00 am 12:00 pmDAT112: Lecture 2Introduction to neural network programming, lecture 2 | DAT112 - Apr 2024 |
30 Apr 11:00 am 12:00 pmDAT112: Lecture 3Introduction to neural network programming, lecture 3 | DAT112 - Apr 2024 |
May,2024 | |
2 May 11:00 am 12:00 pmDAT112: Lecture 4Introduction to neural network programming, lecture 4 | DAT112 - Apr 2024 |