Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation
- Title
- Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation
- Author(s)
- Sanghoon Lee
- DGIST Authors
- Sanghoon Lee ; Yeseong Kim ; Sungjin Lee
- Advisor
- 김예성
- Co-Advisor(s)
- Sungjin Lee
- Issued Date
- 2023
- Awarded Date
- 2023-02-01
- Type
- Thesis
- Description
- Clustering Algorithms, Near Data Processing, Disaggregated Memory, CXL, Genetic Algorithms
- Table Of Contents
-
Ⅰ. INTRODUCTION 1
II. RELATED WORK 6
III. OVERVIEW OF SIDEKICK 8
3.1 Architectural Overview: Memory Disaggregation with CXL 10
3.2 Core Software: Automated Migration Technique 12
IV. AUTOMATED PROGRAM CONTEXT EXTRACTION 13
4.1 Program Context Extraction 13
4.2 Managing computation and memory allocation 15
4.3 Implementation 16
V. MIGRATION POLICY DECISION 17
5.1 Initialization of Genetic Algorithm 18
5.2 Identifying the best migration policy using genetic algorithm 19
VI. EXPERIMENTAL RESULTS 21
6.1 Experimental Setup 21
6.2 Migration Decision Evaluation 26
6.2.1 Effectiveness and Scalability 26
6.2.2 Migration Quality Evaluation 27
6.3 Memory Activity Evaluation 29
6.4 In-Depth Analysis of Sidekick Optimization Procedure 30
6.5 Impact of Bootstrapping 31
VII. CONCLUSION 33
- URI
-
http://hdl.handle.net/20.500.11750/45760
http://dgist.dcollection.net/common/orgView/200000656260
- Degree
- Master
- Publisher
- DGIST
- Related Researcher
-
-
Kim, Yeseong
- Research Interests Embedded Systems for Edge Intelligence; Brain-Inspired HD Computing for AI; In-Memory Computing
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- Files in This Item:
-
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- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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