Learning high-performance computing requires a certain foundation in computer science and mathematics. The following is a detailed learning route, from getting started to proficient in high-performance computing steps:
Phase 1: Basic Knowledge Learning
Computer Science Fundamentals: Learn about computer architecture, operating systems, data structures, and algorithms. Learn programming languages such as C/C++, Python, etc.
Mathematical foundations: Learn the mathematical foundations of linear algebra, calculus, and probability theory, which are important for understanding algorithms and models in high-performance computing.
It is generally enough to have a high level of mathematics and a foundation in computer science for undergraduates.
Phase 2: Parallel Computing and Distributed Systems
Parallel Computing Fundamentals: Learn the concepts of parallel computing, parallel algorithms, and parallel programming models, such as shared memory and distributed memory parallel models.
Distributed system: understand the principle, architecture and communication model of distributed computing, and master common distributed computing frameworks such as Apache Hadoop and Apache Spark.
Phase 3: High Performance Computing Architecture and Programming Model
High-performance computing architecture: learn high-performance computing hardware architectures such as multi-core processors, GPUs, and FPGAs, and understand their characteristics and advantages.
Parallel programming model: In-depth study of parallel programming models, such as OpenMP, CUDA, and MPI, etc., to achieve parallel computing and utilize high-performance hardware to accelerate computing.
Phase 4: High Performance Computing Application and Optimization
High-performance computing applications: Study domain-specific high-performance computing applications, such as computational fluid dynamics, molecular dynamics, and astrophysics, and understand their algorithms and models.
Performance optimization techniques: Learn performance analysis and tuning techniques, including algorithm optimization, memory optimization, parallelization and vectorization, etc., to improve computing performance.
Phase Five: Parallel File System and Cluster Management
Parallel file system: Understand the design and implementation principles of parallel file systems, such as Luster and GPFS, and learn how to effectively manage large-scale data in high-performance computing.
Cluster management: Master cluster management tools and technologies, such as Slurm and PBS, to manage and schedule high-performance computing cluster resources.
Stage Six: Frontier Technology and Application
GPU Accelerated Computing: Learn how to use GPU for general computing and deep learning acceleration, understand CUDA programming and deep learning frameworks such as TensorFlow and PyTorch, etc.
Quantum computing: understand the basic principles and algorithms of quantum computing, and learn how to use quantum computers for high-performance computing
Phase Six (continued):
Quantum computing (continued): An in-depth study of the core theories and algorithms of quantum computing, such as qubit manipulation, quantum error correction, and quantum search. Learn quantum programming languages and quantum computing libraries such as Qiskit and Cirq, etc.
Phase 7: Performance optimization and debugging tools
Performance analysis tools: Learn to use performance analysis tools, such as Intel VTune, GNU gprof and HPCToolkit, etc., to perform performance analysis and bottleneck location on high-performance computing programs.
Debugging tools: Familiar with debugging tools, such as GDB and TotalView, etc., to solve errors and exceptions in parallel computing programs.
Stage Eight: Practical and Project Experience
Practical projects: Participate in practical projects related to high-performance computing, such as scientific computing, large-scale data processing, or machine learning model training. Through practical project experience, deepen the understanding and application ability of high-performance computing.
Open source community participation: Actively participate in the open source community in the field of high performance computing, contribute code and experience, communicate and learn with professionals.
Stage Nine: Continuous Learning and Tracking Development
Track development: Keep track and learn in the field of high-performance computing, pay attention to the latest technology and research progress, and understand new hardware architecture and programming models.
Continue learning: Attend relevant trainings, seminars and conferences, read academic papers and technical documents in the field of high-performance computing, and continuously improve your knowledge and skills.
It is important to note that high performance computing is a broad and evolving field and the study path may be tailored to suit individual interests and needs. The above learning route is for reference only, and it is recommended to adjust and expand according to personal circumstances.
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