parallel computing education training

**HPC Education and Training: Pathways to Parallel Computing — textbooks, courses, and workshops for skill development** High-performance computing education spans textbooks, online courses, workshops, and internship programs, providing structured pathways from fundamentals to advanced specialization. **Foundational Textbooks** Programming Massively Parallel Processors (Kirk & Hwu, MIT Press 2013/2022 edition) covers GPU architecture, CUDA programming, parallel patterns (reduction, scan, sort), and optimization. Structured progressively: architectural fundamentals, kernel optimization techniques, case studies. Computer Organization and Design (Patterson & Hennessy) provides CPU architecture prerequisites. Parallel Programming in OpenMP (Chapman, Kousouris, Baresi) covers OpenMP fundamentals; similar texts exist for MPI. **Online Courses and Certifications** NVIDIA DLI (Deep Learning Institute) offers instructor-led and self-paced courses: Fundamentals of Accelerated Computing with CUDA C/C++, Scaling GPU-Accelerated Applications with NVIDIA NCCL, Scaling Multi-Node Deep Learning with NVIDIA Collective Communications Library. Udacity Intro to Parallel Programming (free, NVIDIA-sponsored) covers CUDA fundamentals via video lectures and coding projects. Coursera specializations (Parallel Programming in Java, Data Science with Scala) enable broader skill building. **HPC Carpentry and Workshops** HPC Carpentry provides community-led workshops covering HPC clusters, Linux, shell scripting, job scheduling, MPI, OpenMP, CUDA basics. Venues include universities, national labs, supercomputing conferences. Supercomputing Conference (SC—annual) hosts tutorials covering cutting-edge topics: GPU programming, performance optimization, new HPC frameworks, distributed training. SC student volunteers gain mentorship and networking. **XSEDE/ACCESS and SULI Programs** XSEDE (eXtreme Science and Engineering Discovery Environment, now ACCESS) provides HPC resources and training nationwide. SULI (Science Undergraduate Laboratory Internship) places US undergraduates at DOE labs (ORNL, LLNL, LANL, BNL, SLAC, ANL) for 10-week paid internships, providing hands-on HPC experience. NERSC (National Energy Research Scientific Computing Center) offers visiting scholar programs. **Community Resources** MPITUTORIAL.COM provides free MPI tutorial with example code. Official CUDA Programming Guide and ROCm documentation offer detailed references. GitHub repositories (CUDA samples, OpenMP examples) enable self-learning. Research communities (IEEE TCPP Curriculum Initiative, ACM SIGHPC) develop curriculum guidelines.