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via Udemy |
Go to Course: https://www.udemy.com/course/learn-to-use-hpc-systems-and-supercomputers/
**Course Review and Recommendation: High Performance Computing (HPC) Systems on Coursera** **Overview:** This innovative course offered on Coursera provides an extensive introduction to High Performance Computing (HPC) systems, focusing on their software stack, architecture, and real-world applications. It is uniquely designed to equip learners with the skills necessary to leverage parallel and distributed computing resources, including supercomputers and HPC clusters, for solving complex problems efficiently. The course is ideal for students and professionals interested in Machine Learning, Deep Learning, Data Science, Big Data, and scientific computing. **Content and Structure:** The course covers a broad spectrum of topics starting from the history of supercomputing to advanced programming models. It includes: - An overview of supercomputers and HPC clusters, their components, and internal workings. - Practical instruction on job management systems such as PBS and Slurm, including commands and job scheduling techniques. - Deep dives into parallel programming models like OpenMP, MPI, CUDA, and HIP, with hands-on examples and beginner-friendly explanations. - Exploration of GPU programming on both NVIDIA and AMD GPUs, making complex concepts more accessible. - Insights into cloud-based HPC via AWS, demonstrating how to build and manage HPC clusters in the cloud environment. - Additional interactive live classes through the Scientific Programming School, enhancing learning interaction and hands-on practice. **Strengths:** - **Comprehensive Coverage:** The course consolidates a university semester’s worth of knowledge into an accessible format, making it suitable for those new to HPC as well as practitioners seeking a refresher. - **Practical Focus:** Emphasis on real-world commands, programming models, and cloud deployment provides learners with actionable skills. - **Diverse Content:** The inclusion of CUDA, HIP, OpenMP, MPI, and cloud HPC broadens your capabilities across different hardware and environments. - **Supportive Community:** Access to Q&A sessions and live classes fosters an engaging learning environment, allowing learners to clarify doubts and deepen understanding. **Areas for Improvement:** - Since the course packs a high-volume of information, beginners might find certain topics initially overwhelming without prior background in programming or Linux. - The complexity of topics like CUDA and HIP requires dedicated practice; additional project-based learning could enhance mastery. **Final Recommendation:** This course is highly recommended for anyone seeking a well-rounded introduction to HPC systems, especially those aiming to apply these skills in data science, machine learning, or scientific research. It is particularly valuable for learners who enjoy a mix of theoretical understanding and practical application, supported by interactive live classes. **In Summary:** - **Pros:** Extensive content, practical skills, covers multiple programming models, cloud integration. - **Cons:** Intensity of material for newcomers; requires dedication. - **Ideal for:** Researchers, students, and professionals in scientific computing, data science, and engineering fields. If you're interested in mastering high-performance computing tools and concepts to accelerate your research or projects, this course is an excellent investment in your technical development.
Welcome to the First-ever High Performance Computing (HPC) Systems course on the Udemy platform. The goal main of this course is to introduce you with the HPC systems and its software stack. This course has been specially designed to enable you to utilize parallel & distributed programming and computing resources to accelerate the solution of a complex problem with the help of HPC systems and Supercomputers. You can then use your knowledge in Machine learning, Deep learning, Data Sciences, Big data and so on.HPC clusters typically have a large number of computers (often called ‘nodes') and, in general, most of these nodes would be configured identically. Though from the out side the cluster may look like a single system, the internal workings to make this happen can be quite complex. This idea should not be confused with a more general client-server model of computing as the idea behind clusters is quite unique. Cluster computing utilize multiple machines to provide a more powerful computing environment perhaps through a single operating system.WHAT DO YOU LEARN?A Little bit of Supercomputing history, Supercomputing examples, Supercomputers vs. HPC clusters, HPC clusters computers, Benefits of using cluster computing.Components of a High Performance Systems (HPC) cluster, Properties of Login node(s), Compute node(s), Master node(s), Storage node(s), HPC networks and so on.Introduction to PBS, PBS basic commands, PBS `qsub`, PBS `qstat`, PBS `qdel` command, PBS `qalter`, PBS job states, PBS variables, PBS interactive jobs, PBS arrays, PBS MATLAB exampleIntroduction to Slurm, Slurm commands, A simple Slurm job, Slurm distrbuted MPI and GPU jobs, Slurm multi-threaded OpenMP jobs, Slurm interactive jobs, Slurm array jobs, Slurm job dependenciesOpenMP basics, Open MP - clauses, worksharing constructs, OpenMP- Hello world!, reduction and parallel `for-loop`, section parallelization, vector addition, MPI - hello world! send/ receive and `ping-pong` Parallel programming - GPU and CUDA: Finally, it gives you a concise beginner friendly guide to the GPUs - graphics processing units, GPU Programming - CUDA, CUDA - hello world and so on! We understand that CUDA is a difficult API, particularly the memory models. We have added some easy to understand CUDA lessons with examples to make your life easy and comfortable to grasp the basics fast!Parallel programming - AMD GPU and HIP (New! Aug 2023): Learn parallel programming on AMD GPU's with ROCm and HIP from basic concepts to advance implementations. We will start our discussion by looking at basic concepts including AMD GPU programming, execution model, and memory model. Then we will show you how to implement algorithms using ROCm and HIP.AWS HPC: With the recent advantage of the faster Cloud technologies, AWS provides the most elastic and scalable cloud infrastructure to run your HPC applications. With virtually unlimited capacity, engineers, researchers, and HPC system owners can innovate beyond the limitations of on-premises HPC infrastructure. We have added lectures to show and tell you on how to build a AWS HPC cluster and how to run codes -easily!Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of the Parallel and distributed computing and the High Performance Computing (HPC) systems software stack: Slurm, PBS Pro, OpenMP, MPI and CUDA! Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding. Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). Instructions to join are given in the additional contents section.DISCLAIMERWe created here a total of one university semester worth of knowledge (valued USD $2500-6000) into one single video course, and hence, it's a high-level overview. Don't forget to join our Q & A live community where you can get free help anytime from other students and the instructor. This awesome course is a component of the Learn Scientific Computing master course.