Course title
330160002
High Performance Computing

 OZAKI Katsuhisa
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
Mathematical Sciences Course DP-4・2 キャリアを見据えた高度な専門知識
現象の背後にある数理構造やデータのパターンを理論的に解析し、社会や自然科学、工学における複雑な課題に対して数理的視点から解決戦略を提案できる。
Purpose of class
The objective of this course is to understand the fundamental concepts and major computational techniques of High Performance Computing (HPC). Through topics such as parallel computing, memory hierarchy, and numerical algorithms, students learn the principles and methods required to execute large-scale computations efficiently. Furthermore, assuming practical computing environments, students acquire the basics of efficient program design and performance evaluation.
Course description
This course covers the fundamental concepts and practical computational techniques of High-Performance Computing (HPC). First, the basic principles of computer architecture, memory hierarchy, and parallel computing are introduced. Next, numerical algorithms and performance optimization techniques (such as vectorization, parallelization, and the use of computational libraries) are discussed in order to understand how to efficiently execute large-scale computations. In addition, through programming examples and numerical experiments, students learn methods for evaluating performance and improving computational efficiency. Through these topics, students acquire the foundations of scientific computing using high-
Goals and objectives
  1. Students will understand modern computing environments
  2. Students will understand algorithms and be able to implement them
  3. Students will be able to write efficient programs, including parallel implementations
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 exam1 exam2 exercise Total.
1. 20% 20% 6% 46%
2. 20% 7% 27%
3. 20% 7% 27%
Total. 40% 40% 20% -
Evaluation method and criteria
Students are evaluated based on a midterm examination, a final examination, and exercises. The examinations cover a wide range of fundamental topics. A score of 60% or higher is required to pass the course. Passing the course means that students understand the properties and characteristics of algorithms and can independently write basic programs.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Modern computing environments Review the C programming language 190minutes
2. Matrix storage and matrix multiplication Review linear algebra 190minutes
3. Sparse matrix storage and computation Review linear algebra 190minutes
4. Sorting algorithms: selection sort, bubble sort, bucket sort Review C programming 190minutes
5. Sorting algorithms: radix sort, quicksort Review C programming 190minutes
6. Divide-and-conquer algorithms Review linear algebra 190minutes
7. Midterm examination and review Review the material from Lectures 1–6 190minutes
8. OpenMP: introduction and sections construct Review the C programming language 190minutes
9. OpenMP: parallelization of for-loops Review the notes and materials from the previous lecture 190minutes
10. Parallel efficiency: weak scaling and strong scaling Review the notes and materials from the previous lecture; review recursive procedures 190minutes
11. Practical exercise: parallelization of matrix multiplication Review algorithms and computational complexity 190minutes
12. Numerical accuracy in numerical computation Review binary representation 190minutes
13. Programming in advanced computing environments Review MATLAB 190minutes
14. Summary and final examination Review all course materials 190minutes
Total. - - 2660minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
nothing
Prerequisites
Office hours and How to contact professors for questions
  • Lunch Time on Monday
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic interpersonal skills
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Mon Mar 23 04:07:44 JST 2026