Parallel computing (01) (Tính toán song song)
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- 81
- 形式
- サイズ
- 922 KB
- 年
- 2004
- Trường
- University of Hildesheim
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Slide bài giảng giới thiệu về tính toán song song, bao gồm các khái niệm cơ bản về threads, các API và đề cương khóa học Big Data Analytics.
- ドキュメント名
- Parallel computing (01) (Tính toán song song)
- 学校 / コース
- University of Hildesheim · Big Data
- 内容
- Tài liệu giới thiệu về tính toán song song, tập trung vào khái niệm tiến trình và luồng, cùng các API lập trình luồng phổ biến. Nó là phần mở đầu cho các chủ đề nâng cao hơn về tính toán phân tán và học máy.
- 目次
- 0. Introduction
- A. Parallel Computing
- A.1 Threads
- A.2 Message Passing Interface (MPI)
- A.3 Graphical Processing Units (GPUs)
- B. Distributed Storage
- B.1 Distributed File Systems
- B.2 Partioning of Relational Databases
- B.3 NoSQL Databases
- C. Distributed Computing Environments
- C.1 Map-Reduce
- C.2 Resilient Distributed Datasets (Spark)
- C.3 Computational Graphs (TensorFlow)
- D. Distributed Machine Learning Algorithms
- D.1 Distributed Stochastic Gradient Descent
- D.2 Distributed Matrix Factorization
- Questions and Answers
- 1. Threads Basics
- 2. Starting and Interrupting Threads
- 3. Synchronization I: Monitors
- 4. Synchronization II: Locks
- 5. Starting Threads II: Thread Pools and Dependency Graphs
- 6. Open MP
- 7. More Examples
- ページ数
- 81 ページ
- アップロード者
- Uni24h
説明
Trích nội dung tài liệu
Big Data Analytics Big Data Analytics A. Parallel Computing / A.1 Threads Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute for Computer Science University of Hildesheim, Germany Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics Syllabus Tue. 9.4. (1) 0. Introduction Tue. 16.4. Tue. 23.4. Tue. 30.4. (2) (3) (4) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) A.3 Graphical Processing Units (GPUs) Tue. 7.5. Tue. 14.5. Tue. 21.5. (5) (6) (7) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases B.3 NoSQL Databases Tue. 28.5. Tue. 4.6. Tue. 11.6. Tue. 18.6. (8) (9) (10) C. Distributed Computing Environments C.1 Map-Reduce Pentecoste Break — C.2 Resilient Distributed Datasets (Spark) C.3 Computational Graphs (TensorFlow) Tue. 25.6. Tue. 2.7. (11) (12) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent D.2 Distributed Matrix Factorization Tue. 9.7. (13) Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics Outline 1. Threads Basics 2. Starting and Interrupting Threads 3. Synchronization I: Monitors 4. Synchronization II: Locks 5. Starting Threads II: Thread Pools and Dependency Graphs 6. Open MP 7. More Examples Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics 1. Threads Basics Outline 1. Threads Basics 2. Starting and Interrupting Threads 3. Synchronization I: Monitors 4. Synchronization II: Locks 5. Starting Threads II: Thread Pools and Dependency Graphs 6. Open MP 7. More Examples Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics 1. Threads Basics Pro
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Parallel computing (01) (Tính toán song song)
プレビューを生成中...
Trích nội dung tài liệu
Big Data Analytics Big Data Analytics A. Parallel Computing / A.1 Threads Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute for Computer Science University of Hildesheim, Germany Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics Syllabus Tue. 9.4. (1) 0. Introduction Tue. 16.4. Tue. 23.4. Tue. 30.4. (2) (3) (4) A. Parallel Computing A.1 Threads A.2 Message Passing Interface (MPI) A.3 Graphical Processing Units (GPUs) Tue. 7.5. Tue. 14.5. Tue. 21.5. (5) (6) (7) B. Distributed Storage B.1 Distributed File Systems B.2 Partioning of Relational Databases B.3 NoSQL Databases Tue. 28.5. Tue. 4.6. Tue. 11.6. Tue. 18.6. (8) (9) (10) C. Distributed Computing Environments C.1 Map-Reduce Pentecoste Break — C.2 Resilient Distributed Datasets (Spark) C.3 Computational Graphs (TensorFlow) Tue. 25.6. Tue. 2.7. (11) (12) D. Distributed Machine Learning Algorithms D.1 Distributed Stochastic Gradient Descent D.2 Distributed Matrix Factorization Tue. 9.7. (13) Questions and Answers Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics Outline 1. Threads Basics 2. Starting and Interrupting Threads 3. Synchronization I: Monitors 4. Synchronization II: Locks 5. Starting Threads II: Thread Pools and Dependency Graphs 6. Open MP 7. More Examples Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics 1. Threads Basics Outline 1. Threads Basics 2. Starting and Interrupting Threads 3. Synchronization I: Monitors 4. Synchronization II: Locks 5. Starting Threads II: Thread Pools and Dependency Graphs 6. Open MP 7. More Examples Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 52 Big Data Analytics 1. Threads Basics Pro
- ドキュメント名
- Parallel computing (01) (Tính toán song song)
- 学校 / コース
- University of Hildesheim · Big Data
- 内容
- Tài liệu giới thiệu về tính toán song song, tập trung vào khái niệm tiến trình và luồng, cùng các API lập trình luồng phổ biến. Nó là phần mở đầu cho các chủ đề nâng cao hơn về tính toán phân tán và học máy.
- 目次
- 0. Introduction
- A. Parallel Computing
- A.1 Threads
- A.2 Message Passing Interface (MPI)
- A.3 Graphical Processing Units (GPUs)
- B. Distributed Storage
- B.1 Distributed File Systems
- B.2 Partioning of Relational Databases
- B.3 NoSQL Databases
- C. Distributed Computing Environments
- C.1 Map-Reduce
- C.2 Resilient Distributed Datasets (Spark)
- C.3 Computational Graphs (TensorFlow)
- D. Distributed Machine Learning Algorithms
- D.1 Distributed Stochastic Gradient Descent
- D.2 Distributed Matrix Factorization
- Questions and Answers
- 1. Threads Basics
- 2. Starting and Interrupting Threads
- 3. Synchronization I: Monitors
- 4. Synchronization II: Locks
- 5. Starting Threads II: Thread Pools and Dependency Graphs
- 6. Open MP
- 7. More Examples
- ページ数
- 81 ページ
- アップロード者
- Uni24h
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