| Academic Year |
2024Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA211001 |
Subject Classification |
Specialized Education |
| Subject Name |
並列分散処理 |
Subject Name
（Katakana） |
ヘイレツブンサンショリ |
Subject Name in
English |
Parallel and Distributed Processing |
| Instructor |
KASAGI AKIHIKO,ITOU YASUAKI |
Instructor
(Katakana) |
カサギ アキヒコ,イトウ ヤスアキ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture and exercise |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
3nd-year (School of Informatics and Data Science) |
| Keywords |
UNIX, process parallel, thread parallel, SIMD, GPU, CUDA |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | Using the content learned in the basic subjects, deepen knowledge and understanding of the technical expertise. |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・D3. Knowledge of hardware and software and programming ability to process data efficiently.
Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Intelligence Science Program
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT. |
|---|
Class Objectives
/Class Outline |
The actual situation of recent multi-core processors and the necessity of parallel processing is introduced, and the processing model and operation method of parallel computing using CPU and GPU are explained respectively. To develop an operation and understanding of parallel processing programming using a large number of cores, and to acquire programming skills for parallel processing through practical exercises. |
| Class Schedule |
lesson1: Thread parallel processing
lesson2: Thread parallel programming using OpenMP
lesson3: Thread parallel programming exercise
lesson4: Thread parallel programming exercise
lesson5: Process parallel processing
lesson6: Process parallel programming using MPI
lesson7: Process parallel programming exercise
lesson8: Process parallel programming exercise
lesson9: Basis of GPU
lesson10: Basis of CUDA programming
lesson11: Basic CUDA programming exercise
lesson12: Basic CUDA programming exercise
lesson13: Parallel computing technique for GPUs
lesson14: Librarization for GPU programs
lesson15: CUDA programming exercise
Reports |
Text/Reference
Books,etc. |
Handout |
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Please review C language programing |
| Requirements |
|
| Grading Method |
Exercise and reports |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
Details will be posted on Momiji as soon as the schedule is confirmed. |
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |