调度和优化大数据计算框架基于CPU/GPU集群
发布时间：2024-01-29 20:42
本文将讨论大数据处理。大数据作为一种揭示数据背后诸如趋势、性质等信息的重要技术,已经引起了人们相当大程度的关注。最近,很多研究人员用不同方式提供了大数据处理的解决方案。MapReduce是其中一种最流行的类似数据处理框架。不管怎样,一些高端应用,尤其一些科学分析能同时具有大数据和云计算特点。因此,我们设计并实施了一个高效的大数据处理框架称为Lit,Lit能够最大限度发挥Hadoop和GPUs的力量。本文呈现了Lit的基本设计和结构。更重要的是,我们致力于最大限度地实现CPU和GPU的通信优化,并展示数据传送的策略。我方法的灵感一部分来自于科学计算界的代码最优化,并提出了指令合并。指令合并融合了两个GPU指令的代码体,目的是1)消除相关指令的无效操作;2)减少GPU指令和GPU存储之间的数据传输;3)减少GPU存储和CPU存储之间的数据传输;4)利用存储器参量的空间和时间位置。此外,我们还介绍了数据流优化方法以减少不必要的数据复制。最后,本文介绍了数据通信调度器,该方法能够最大限度地减少多余数据的传输。
【文章页数】：75 页
【学位级别】：硕士
【文章目录】：
摘要
ABSTRACT
CHAPTER 1: INTRODUCTION
1.1 Thesis background and significance
1.2 Accelerating compute boards: from ASICs to GPU computing
1.3 Computing with Graphic Processing Units
1.3.1 Fixed-function pipelines to fully programmable shaders
1.3.2 General Purpose GPUs
1.3.3 From GPGPU to GPU Computing
1.4 Programming Environments
1.4.1 Low-level Vendor Toolkits
1.4.2 Era of libraries
1.5 Future Trends
1.6 Thesis Objectives
1.7 Thesis organization
CHAPTER 2: LIT: DESIGN HIGH PERFORMANCE MASSIVE DATA COMPUTINGFRAMEWORK BASED ON CPU/GPU CLUSTE
2.1 LIT definition
2.2 Preliminaries and Related work
2.2.1 Data-intensive Computing with Hadoop Map Reduce
2.2.2 GPGPU
2.2.3 GPU based Map Reduce frameworks
2.3 System Design and implementation
2.3.1 Architecture Overview
2.3.2 Lit Workflow
2.3.3 Directives Design
CHAPTER 3: SCHEDULING AND OPTIMIZATION
3.1 Work?ow Optimization
3.2 Memory Copy Optimization
3.3 Instructions fusion Optimization
3.3.1 Instruction Fusion as an Optimization Method
3.3.2 The Benefits of Instruction Fusion
3.3.3 Automating instruction based data Fusion
3.3.4 Instructions Fusion
3.4 CPU/GPU data communication scheduling
3.4.1 Data communication scheduler
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Experimental Setup
4.2 Benchmark et Evaluation
4.3 Evaluation Data set
4.3.1 Measurements with MM, FFT&SCAN
4.3.2 Measurements With instructions Fusion
4.4 Discussion
CONCLUSION
REFERENCES
ACKNOWLEDGEMENT
本文编号：3888809
【文章页数】：75 页
【学位级别】：硕士
【文章目录】：
摘要
ABSTRACT
CHAPTER 1: INTRODUCTION
1.1 Thesis background and significance
1.2 Accelerating compute boards: from ASICs to GPU computing
1.3 Computing with Graphic Processing Units
1.3.1 Fixed-function pipelines to fully programmable shaders
1.3.2 General Purpose GPUs
1.3.3 From GPGPU to GPU Computing
1.4 Programming Environments
1.4.1 Low-level Vendor Toolkits
1.4.2 Era of libraries
1.5 Future Trends
1.6 Thesis Objectives
1.7 Thesis organization
CHAPTER 2: LIT: DESIGN HIGH PERFORMANCE MASSIVE DATA COMPUTINGFRAMEWORK BASED ON CPU/GPU CLUSTE
2.1 LIT definition
2.2 Preliminaries and Related work
2.2.1 Data-intensive Computing with Hadoop Map Reduce
2.2.2 GPGPU
2.2.3 GPU based Map Reduce frameworks
2.3 System Design and implementation
2.3.1 Architecture Overview
2.3.2 Lit Workflow
2.3.3 Directives Design
CHAPTER 3: SCHEDULING AND OPTIMIZATION
3.1 Work?ow Optimization
3.2 Memory Copy Optimization
3.3 Instructions fusion Optimization
3.3.1 Instruction Fusion as an Optimization Method
3.3.2 The Benefits of Instruction Fusion
3.3.3 Automating instruction based data Fusion
3.3.4 Instructions Fusion
3.4 CPU/GPU data communication scheduling
3.4.1 Data communication scheduler
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Experimental Setup
4.2 Benchmark et Evaluation
4.3 Evaluation Data set
4.3.1 Measurements with MM, FFT&SCAN
4.3.2 Measurements With instructions Fusion
4.4 Discussion
CONCLUSION
REFERENCES
ACKNOWLEDGEMENT
本文编号：3888809
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