Register training material
16 materials found

Keywords: AI  or Excel  or GPUs 


WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me-4d1ea222-4240-4b68-b9ae-7769ac664ee0 This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
Accelerating skills development in Data science and AI at scale

At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities...

Keywords: AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material

Accelerating skills development in Data science and AI at scale https://dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale-2d8a65fa-f96e-44ad-a026-cfae3f38d128 At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally. The talk will also cover our approach as outlined below •        Combined survey of gaps in skills and trainings for Data science and AI •        Provide seats to partners •        Share associate instructors/helpers/volunteers •        Develop combined training materials •        Publish a repository of open source trainings •        Train the trainer activities •        Establish a network of volunteers to deliver trainings at their local regions Industry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community. Finally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together. contact@ardc.edu.au AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
Monash University - University of Queensland training partnership in Data science and AI

We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning,...

Keywords: data skills, training partnerships, data science, AI, training material

Monash University - University of Queensland training partnership in Data science and AI https://dresa.org.au/materials/monash-university-university-of-queensland-training-partnership-in-data-science-and-ai-8082bf73-d20f-4214-ad8c-95123e25a36c We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning, visualisation, and computing tools, we have established a series of over 20 workshops over the year where either Monash or QCIF hosts the event for some 20-40 of their researchers and students, while some 5 places are offered to participants from the other institution. In the longer term we aim to share material developed at one institution and have trainers present it at the other. In this talk we will describe the many benefits we have found to this approach including access to a wider range of expertise in several rapidly developing fields, upskilling of trainers, faster identification of emerging training needs, and peer learning for trainers. contact@ardc.edu.au data skills, training partnerships, data science, AI, training material
OpenCL

Supercomputers make use of accelerators from a variety of different hardware vendors, using devices such as multi-core CPU’s, GPU’s and even FPGA’s. OpenCL is a way for your HPC application to make effective use of heterogeneous computing devices, and to avoid code refactoring for new HPC...

Keywords: OpenCL, supercomputing, CPUs, GPUs, FPGAs, HPC

OpenCL https://dresa.org.au/materials/opencl-3eabb316-794d-4f46-959a-725be3ae1bde Supercomputers make use of accelerators from a variety of different hardware vendors, using devices such as multi-core CPU’s, GPU’s and even FPGA’s. OpenCL is a way for your HPC application to make effective use of heterogeneous computing devices, and to avoid code refactoring for new HPC infrastructure. Topics covered in this course are : - Introduction to OpenCL - How to build and run applications on Setonix with OpenCL and MPI - Matrix multiplication with OpenCL – fully explained line by line - How to debug OpenCL applications and kernels - Measure performance with OpenCL Events and open source tools - Memory management - Coarse and fine-grained shared memory - Strategies for building optimised OpenCL kernels - Optimise IO performance with asynchronous operations training@pawsey.org.au OpenCL, supercomputing, CPUs, GPUs, FPGAs, HPC
PCon Preparing applications for El Capitan and beyond

As Lawrence Livermore National Laboratories (LLNL) prepares to stand up its next supercomputer, El Capitan, application teams prepare to pivot to another GPU architecture.

This talk presents how the LLNL application teams made the transition from distributed-memory, CPU-only architectures to...

Keywords: GPUs, supercomputing, HPC, PaCER

PCon Preparing applications for El Capitan and beyond https://dresa.org.au/materials/pcon-preparing-applications-for-el-capitan-and-beyond As Lawrence Livermore National Laboratories (LLNL) prepares to stand up its next supercomputer, El Capitan, application teams prepare to pivot to another GPU architecture. This talk presents how the LLNL application teams made the transition from distributed-memory, CPU-only architectures to GPUs. They share institutional best practices. They discuss new open-source software products as tools for porting and profiling applications and as avenues for collaboration across the computational science community. Join LLNL's Erik Draeger and Jane Herriman, who presented this talk at Pawsey's PaCER Conference in September 2023. training@pawsey.org.au Pawsey Supercomputing Research Centre GPUs, supercomputing, HPC, PaCER masters phd researcher ecr support professional ugrad
OpenCL

Supercomputers make use of accelerators from a variety of different hardware vendors, using devices such as multi-core CPU’s, GPU’s and even FPGA’s. OpenCL is a way for your HPC application to make effective use of heterogeneous computing devices, and to avoid code refactoring for new HPC...

Keywords: supercomputing, Pawsey Supercomputing Centre, CPUs, GPUs, OpenCL, FPGAs

Resource type: activity

OpenCL https://dresa.org.au/materials/opencl Supercomputers make use of accelerators from a variety of different hardware vendors, using devices such as multi-core CPU’s, GPU’s and even FPGA’s. OpenCL is a way for your HPC application to make effective use of heterogeneous computing devices, and to avoid code refactoring for new HPC infrastructure. training@pawsey.org.au Toby Potter supercomputing, Pawsey Supercomputing Centre, CPUs, GPUs, OpenCL, FPGAs masters ecr researcher support
AMD Profiling

The AMD profiling workshop covers the AMD suite of tools for development of HPC applications on AMD GPUs.

You will learn how to use the rocprof profiler and trace visualization tool that has long been available as part of the ROCm software suite.

You will also learn how to use the new...

Keywords: supercomputing, performance, GPUs, CPUs, AMD, HPC, ROCm

Resource type: activity

AMD Profiling https://dresa.org.au/materials/amd-profiling The AMD profiling workshop covers the AMD suite of tools for development of HPC applications on AMD GPUs. You will learn how to use the rocprof profiler and trace visualization tool that has long been available as part of the ROCm software suite. You will also learn how to use the new Omnitools - Omnitrace and Omniperf - that were introduced at the end of 2022. Omnitrace is a powerful tracing profiler for both CPU and GPU. It can collect data from a much wider range of sources and includes hardware counters and sampling approaches. Omniperf is a performance analysis tool that can help you pinpoint how your application is performing with a visual view of the memory hierarchy on the GPU as well as reporting the percentage of peak for many different measurements. training@pawsey.org.au supercomputing, performance, GPUs, CPUs, AMD, HPC, ROCm
HIP Workshop

The Heterogeneous Interface for Portability (HIP) provides a programming framework for harnessing the compute capabilities of multicore processors, such as the MI250X GPU’s on Setonix.

In this course we focus on the essentials of developing HIP applications with a focus on...

Keywords: HIP, supercomputing, Programming, GPUs, MPI, debugging

Resource type: full-course

HIP Workshop https://dresa.org.au/materials/hip-workshop The Heterogeneous Interface for Portability (HIP) provides a programming framework for harnessing the compute capabilities of multicore processors, such as the MI250X GPU’s on Setonix. In this course we focus on the essentials of developing HIP applications with a focus on supercomputing. Agenda - Introduction to HIP and high level features - How to build and run applications on Setonix with HIP and MPI - A complete line-by-line walkthrough of a HIP-enabled application - Tools and techniques for debugging and measuring the performance of HIP applications training@pawsey.org.au HIP, supercomputing, Programming, GPUs, MPI, debugging
WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
Beyond Basics: Conditionals and Visualisation in Excel

After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested...

Keywords: Data Analysis, Excel

Beyond Basics: Conditionals and Visualisation in Excel https://dresa.org.au/materials/beyond-basics-conditionals-and-visualisation-in-excel After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** training@intersect.org.au Data Analysis, Excel
Getting Started with Excel

We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors.

This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool,...

Keywords: Data Analysis, Excel

Getting Started with Excel https://dresa.org.au/materials/getting-started-with-excel We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data. #### You'll learn: - Introduction to Microsoft Excel user interface - Interpret data using sorting, filtering, and conditional formatting - Summarise data using functions - Analyse data using pivot tables - Manipulate and visualise data - Handy tips to speed up your work #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/excel001).** training@intersect.org.au Data Analysis, Excel
Excel for Researchers

Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise,...

Keywords: Data Analysis, Excel

Excel for Researchers https://dresa.org.au/materials/excel-for-researchers Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** training@intersect.org.au Data Analysis, Excel
Accelerating skills development in Data science and AI at scale

At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities...

Keywords: AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material

Accelerating skills development in Data science and AI at scale https://dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally. The talk will also cover our approach as outlined below •        Combined survey of gaps in skills and trainings for Data science and AI •        Provide seats to partners •        Share associate instructors/helpers/volunteers •        Develop combined training materials •        Publish a repository of open source trainings •        Train the trainer activities •        Establish a network of volunteers to deliver trainings at their local regions Industry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community. Finally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together. contact@ardc.edu.au AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
Monash University - University of Queensland training partnership in Data science and AI

We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning,...

Keywords: data skills, training partnerships, data science, AI, training material

Monash University - University of Queensland training partnership in Data science and AI https://dresa.org.au/materials/monash-university-university-of-queensland-training-partnership-in-data-science-and-ai We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning, visualisation, and computing tools, we have established a series of over 20 workshops over the year where either Monash or QCIF hosts the event for some 20-40 of their researchers and students, while some 5 places are offered to participants from the other institution. In the longer term we aim to share material developed at one institution and have trainers present it at the other. In this talk we will describe the many benefits we have found to this approach including access to a wider range of expertise in several rapidly developing fields, upskilling of trainers, faster identification of emerging training needs, and peer learning for trainers. contact@ardc.edu.au data skills, training partnerships, data science, AI, training material
Porting the multi-GPU SELF-Fluids code to HIPFort

In this presentation by Dr. Joseph Schoonover of Fluid Numerics LLC, Joe shares their experience with the porting process for SELF-Fluids from multi-GPU CUDA-Fortran to multi-GPU HIPFort.

The presentation covers the design principles and roadmap for SELF and the strategy to port from...

Keywords: AMD, GPUs, supercomputer, supercomputing

Resource type: presentation

Porting the multi-GPU SELF-Fluids code to HIPFort https://dresa.org.au/materials/porting-the-multi-gpu-self-fluids-code-to-hipfort In this presentation by Dr. Joseph Schoonover of Fluid Numerics LLC, Joe shares their experience with the porting process for SELF-Fluids from multi-GPU CUDA-Fortran to multi-GPU HIPFort. The presentation covers the design principles and roadmap for SELF and the strategy to port from Nvidia-only platforms to AMD & Nvidia GPUs. Also discussed are the hurdles encountered along the way and considerations for developing multi-GPU accelerated applications in Fortran. SELF is an object-oriented Fortran library that supports the implementation of Spectral Element Methods for solving partial differential equations. SELF-Fluids is an implementation of SELF that solves the compressible Navier Stokes equations on CPU only and GPU accelerated compute platforms using the Discontinuous Galerkin Spectral Element Method. The SELF API is designed based on the assumption that SEM developers and researchers need to be able to implement derivatives in 1-D and divergence, gradient, and curl in 2-D and 3-D on scalar, vector, and tensor functions using spectral collocation, continuous Galerkin, and discontinuous Galerkin spectral element methods. The presentation discussion is placed in context of the Exascale era, where we're faced with a zoo of available compute hardware. Because of this, SELF routines provide support for GPU acceleration through AMD’s HIP and support for multi-core, multi-node, and multi-GPU platforms with MPI. training@pawsey.org.au AMD, GPUs, supercomputer, supercomputing
Embracing new solutions for in-situ visualisation

This PPT was used by Jean Favre, senior visualisation software engineer at CSCS, the Swiss National Supercomputing Centre during his presentation at P'Con '21 (Pawsey's first PaCER Conference).

This material discusses the upcoming release of ParaView v5.10, a leading scientific visualisation...

Keywords: ParaView, GPUs, supercomputer, supercomputing, visualisation, data visualisation

Resource type: presentation

Embracing new solutions for in-situ visualisation https://dresa.org.au/materials/embracing-new-solutions-for-in-situ-visualisation This PPT was used by Jean Favre, senior visualisation software engineer at CSCS, the Swiss National Supercomputing Centre during his presentation at P'Con '21 (Pawsey's first PaCER Conference). This material discusses the upcoming release of ParaView v5.10, a leading scientific visualisation application. In this release ParaView consolidates its implementation of the Catalyst API, a specification developed for simulations and scientific data producers to analyse and visualise data in situ. The material reviews some of the terminology and issues of different in-situ visualisation scenarios, then reviews early Data Adaptors for tight-coupling of simulations and visualisation solutions. This is followed by an introduction of Conduit, an intuitive model for describing hierarchical scientific data. Both ParaView-Catalyst and Ascent use Conduit’s Mesh Blueprint, a set of conventions to describe computational simulation meshes. Finally, the materials present CSCS’ early experience in adopting ParaView-Catalyst and Ascent via two concrete examples of instrumentation of some proxy numerical applications. training@pawsey.org.au ParaView, GPUs, supercomputer, supercomputing, visualisation, data visualisation