With the deepening awareness of the supercomputing Internet, more and more students realize that high-performance computing is one of the best possible employment directions in the future.
Because high-performance computing is more low-level and less likely to be replaced, it is widely favored by college students and working programmers. So is it feasible to learn high-performance computing by yourself? Can HPC be self-taught? As the first domestic company focusing on high-performance computing training and talent recommendation, Ape Code has something to say:
Let me talk about the conclusion first-for friends with strong learning ability and conditions to practice, of course.
One of the benefits of self-study is that you can learn according to your own learning style and pace, and improve your independent learning ability and problem-solving ability.
However, due to the lack of high-performance computing materials in China, most public courses are simple and unsystematic, and high-performance computing is very focused on practice and project experience. If you are self-study based on interest, there is no problem at all, but if you are based on For employment and changing careers, it is necessary to measure the input-output ratio and whether the cost of detours is cost-effective.
Here are some further suggestions to help you get better results in teaching yourself HPC:
Set clear learning goals: Identify specific areas and skills you want to learn, and set short-term and long-term learning goals. This helps you stay motivated and focused as you study, and measure your progress.
Find high-quality learning resources: Take advantage of various resources on the Internet, including textbooks, instructional videos, online courses, and academic papers. Choose those resources that are authoritative and appropriate to your level of study. Open source software, simulators, and lab environments are also available to help you practice and experiment.
Make a study plan and timetable: Make a reasonable study plan, arrange daily or weekly study time, and stick to it. Combine your study time with other daily activities, making sure you have enough time for deep learning and practice.
Organize study notes and summaries: Document important concepts, techniques, and solutions as you learn. Organizing the study notes and summarizing helps to deepen the understanding and memory of the knowledge learned, and facilitates subsequent review and reference.
Seek Feedback and Guidance: Whenever possible, seek opportunities to ask questions of professionals and seek feedback and guidance. Gain inspiration and help from the experiences and opinions of others by attending seminars, participating in online discussions, or connecting with other autodidacts.
Hands-on and project experience: Apply what you've learned by solving real-world problems, participating in open source projects, or building personal projects. Practice is an important way to consolidate knowledge, and it also helps you demonstrate your abilities in job hunting or practical work.
Continuous learning and industry dynamics: High performance computing is an ever-evolving and changing field. Keep an eye on the latest technology, research results and application fields in the industry. Attend industry conferences, subscribe to relevant journals and blogs, and more to keep yourself learning and evolving.
Teaching yourself high-performance computing requires persistence, patience, and continuous learning. Here are some key points to help you succeed in teaching yourself high-performance computing:
Build a solid foundational knowledge: High performance computing involves many fundamental concepts and techniques, such as parallel computing, distributed systems, algorithm optimization, etc. Make sure you have a solid understanding of these fundamentals, which you can learn in depth by reading classic textbooks, taking online courses, or watching academic lectures.
Learn parallel programming and optimization techniques: The core of high performance computing is parallel computing and performance optimization. Understanding and mastering parallel programming models (such as MPI and OpenMP) and related programming techniques and tools can help you effectively utilize parallel computing resources and improve computing performance.
Practical and project experience: Consolidate what you have learned through practice, participate in real projects or open source projects, and exercise your skills. You will gain valuable experience and skills by solving the challenges and problems you will encounter in practice.
Take advantage of open resources and learning communities: There are many open resources and learning communities on the Internet, providing a wealth of learning materials, tutorials and communication platforms. Use these resources to network with other learners and professionals to share experiences and resources.
Keep up with the latest technology and research developments: Techniques and methods in the field of high performance computing are constantly developing and evolving. Keep an eye on the industry and pay attention to the latest technology trends, research results and application fields. Attend relevant seminars, conferences and workshops to communicate with professionals and learn about the latest developments in the industry.
Insist on continuous learning and self-improvement: High-performance computing is a rapidly developing field that requires constant updating and learning of new knowledge and technologies. Maintain enthusiasm for learning and an attitude of continuous learning, regularly review and update your knowledge system.
It takes time and effort to learn high-performance computing by yourself, but with the right learning method, persistent learning attitude and timely practical experience, you can gradually master the core concepts and skills of high-performance computing and succeed in this field.
Of course, if you find it difficult during the self-study process.
Here, Yuancode Technology (https://www.ydma.com) has prepared a high-performance computing training course for you, covering CPU, GPU and supercomputing operation and maintenance. Among them, the ape code - high performance computing from novice to master series courses (parallel optimization of CPU and GPU) is especially recommended
This is a high-performance computing special course jointly developed by experts from top domestic scientific research institutes and engineers from well-known enterprises. Wei et al. You will have the opportunity to learn what you have learned through engineering practical projects, and cooperate and learn closely with top domestic experts and engineers.
