GPUs (04) (Xử lý song song và bộ xử lý đồ họa)
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Slide bài giảng về xử lý song song và bộ xử lý đồ họa (GPU), bao gồm so sánh GPU và CPU, lập trình GPU, và các ví dụ.
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Big Data Analytics Big Data Analytics A. Parallel Computing / 3. Graphical Processing Units (GPUs) 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 / 32 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 / 32 Big Data Analytics Outline 1. GPUs vs CPUs 2. Basics of GPU Programming 3. Example: Color to Grayscale 4. Example: Matrix Multiplication 5. Block Shared Memory Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. GPUs vs CPUs Outline 1. GPUs vs CPUs 2. Basics of GPU Programming 3. Example: Color to Grayscale 4. Example: Matrix Multiplication 5. Block Shared Memory Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. GPUs vs CPUs Massively Parallel Computation I mildly parallel computation: I tens of cores of a CPU I massively parallel computati
AI summary
- Document name
- GPUs (04) (Xử lý song song và bộ xử lý đồ họa)
- School / Course
- University of Hildesheim · Big Data
- Content
- Tài liệu này giới thiệu về việc sử dụng GPU cho tính toán song song trong phân tích dữ liệu lớn, so sánh với CPU, trình bày các khái niệm lập trình GPU cơ bản và đưa ra ví dụ minh họa.
- Table of contents
- Syllabus
- 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
- Outline
- 1. GPUs vs CPUs
- 2. Basics of GPU Programming
- 3. Example: Color to Grayscale
- 4. Example: Matrix Multiplication
- 5. Block Shared Memory
- Pages
- 46 pages
- Uploaded by
- Uni24h
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GPUs (04) (Xử lý song song và bộ xử lý đồ họa)
Generating preview...
Big Data Analytics Big Data Analytics A. Parallel Computing / 3. Graphical Processing Units (GPUs) 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 / 32 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 / 32 Big Data Analytics Outline 1. GPUs vs CPUs 2. Basics of GPU Programming 3. Example: Color to Grayscale 4. Example: Matrix Multiplication 5. Block Shared Memory Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. GPUs vs CPUs Outline 1. GPUs vs CPUs 2. Basics of GPU Programming 3. Example: Color to Grayscale 4. Example: Matrix Multiplication 5. Block Shared Memory Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 1 / 32 Big Data Analytics 1. GPUs vs CPUs Massively Parallel Computation I mildly parallel computation: I tens of cores of a CPU I massively parallel computati
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- Document name
- GPUs (04) (Xử lý song song và bộ xử lý đồ họa)
- School / Course
- University of Hildesheim · Big Data
- Content
- Tài liệu này giới thiệu về việc sử dụng GPU cho tính toán song song trong phân tích dữ liệu lớn, so sánh với CPU, trình bày các khái niệm lập trình GPU cơ bản và đưa ra ví dụ minh họa.
- Table of contents
- Syllabus
- 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
- Outline
- 1. GPUs vs CPUs
- 2. Basics of GPU Programming
- 3. Example: Color to Grayscale
- 4. Example: Matrix Multiplication
- 5. Block Shared Memory
- Pages
- 46 pages
- Uploaded by
- Uni24h
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