Upcoming SciNet Events

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June,2020
Tue 9th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 9th Jun
12:15 pm
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In lieu of its annual Ontario Summer School, SciNet in collaboration with CAMH will be offering weekly virtual summer training on High Performance Computing from June through to August. Topics will include parallel programming, Linux shell, large scale batch processing, biomedical computations, and performance Python and R. The schedule will be announced on the site in the near future. Location: SciNet Online
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Tue 9th Jun
12:30 pm
2:00 pm
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An introduction to basic concepts of high-performance computing. It is intended to be a high-level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics 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. Location: SciNet Online
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Wed 10th Jun
12:30 pm
2:00 pm
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An introduction to basic concepts of high-performance computing. It is intended to be a high-level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics 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. Location: SciNet Online
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Thu 11th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 11th Jun
12:30 pm
2:00 pm
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An introduction to basic concepts of high-performance computing. It is intended to be a high-level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics 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. Location: SciNet Online
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Tue 16th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 16th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
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Wed 17th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
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Thu 18th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 18th Jun
12:30 pm
2:00 pm
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Working with HPC systems involves using the Linux command line.  This short session will cover basic Linux commands to get you initiated with the command line. Location: SciNet Online
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Tue 23rd Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 23rd Jun
12:30 pm
2:00 pm
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Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing. Location: SciNet Online
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Wed 24th Jun
12:30 pm
2:00 pm
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Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing. Location: SciNet Online
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Thu 25th Jun
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 25th Jun
12:30 pm
2:00 pm
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Learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing. Location: SciNet Online
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July,2020
Tue 7th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 7th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
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Wed 8th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
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Thu 9th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 9th Jul
12:30 pm
2:00 pm
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Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems.  Location: SciNet Online
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Tue 14th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 14th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
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Wed 15th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
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Thu 16th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 16th Jul
12:30 pm
2:00 pm
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Introduction to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. Location: SciNet Online
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Tue 21st Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 21st Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
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Wed 22nd Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
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Thu 23rd Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 23rd Jul
12:30 pm
2:00 pm
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Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. Finally, we will cover how to perform functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: SciNet Online
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Tue 28th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Tue 28th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
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Wed 29th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
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Thu 30th Jul
11:00 am
12:00 pm
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Part of Neural Network Programming, Location: SciNet Online
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Thu 30th Jul
12:30 pm
2:00 pm
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Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online
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August,2020
Tue 4th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
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Wed 5th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
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Thu 6th Aug
12:30 pm
2:00 pm
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Learn how to model the neural network in the brain using Python. Location: SciNet Online
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Tue 11th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
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Wed 12th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
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Thu 13th Aug
12:30 pm
2:00 pm
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Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online
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Tue 18th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. Location: SciNet Online
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Wed 19th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. Location: SciNet Online
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Thu 20th Aug
12:30 pm
2:00 pm
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Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. Location: SciNet Online
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Tue 25th Aug
12:30 pm
2:00 pm
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Debugging and profiling are important steps 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 with gdb and the debugging of parallel (MPI and threaded) codes. Location: SciNet Online
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Wed 26th Aug
12:30 pm
2:00 pm
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Debugging and profiling are important steps 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 with gdb and the debugging of parallel (MPI and threaded) codes. Location: SciNet Online
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Thu 27th Aug
12:30 pm
2:00 pm
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Debugging and profiling are important steps 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 with gdb and the debugging of parallel (MPI and threaded) codes. Location: SciNet Online
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July,2021
Sun 11th Jul
1:00 pm
to Fri 23rd Jul
6:00 pm
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This summer school will familiarize the best students in computational sciences with major state-of-the-art aspects of HPC for a variety of scientific disciplines, catalyze the formation of networks, provide advanced mentoring, facilitate international exchange and open up further career options. Leading Canadian, European, Japanese and American computational scientists and HPC technologists will offer instruction in parallel sessions on a variety of topics as: HPC challenges in major scientific disciplines, HPC programming proficiencies, Performance analysis and profiling, Software engineering, Numerical libraries, Big data analysis and analytics, Machine learning, Scientific visualization, and Canadian, European, Japanese and US HPC infrastructure. This events was originally planned to take place in July 2020 but has been moved to July 2021 instead.
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