Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid ...

159 Pages·2015·2.43 MB·English

by  Kaibo Wang

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU
Hybrid Computing Environments

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University

By
Kaibo Wang
Graduate Program in Computer Science and Engineering

The Ohio State University
2015

Dissertation Committee:
Xiaodong Zhang, Advisor
P. Sadayappan
Christopher Stewart
Copyright by
Kaibo Wang
2015
Abstract

Massively data-parallel processors, Graphics Processing Units (GPUs) in particular,
have recently entered the main stream of general-purpose computing as powerful
hardware accelerators to a large scope of applications including databases, medical
informatics, and big data analytics. However, despite their performance benefit and cost
effectiveness, the utilization of GPUs in production systems still remains limited. A
major reason behind this situation is the slow development of supportive GPU software
ecosystem. More specially, (1) CPU-optimized algorithms for some critical computation
problems have irregular memory access patterns with intensive control flows, which
cannot be easily ported to GPUs to take full advantage of its fine-grained, massively data-
parallel architecture; (2) commodity computing environments are inherently concurrent
and require coordinated resource sharing to maximize throughput, while existing systems
are still mainly designed for dedicated usage of GPU resources.
In this Ph.D. dissertation, we develop efficient software solutions to support the
adoption of massively data-parallel processors in general-purpose commodity computing
systems. Our research mainly focuses on the following areas. First, to make a strong case
for GPUs as indispensable accelerators, we apply GPUs to significantly improve the
performance of spatial data cross-comparison in digital pathology analysis. Instead of
trying to port existing CPU-based algorithms to GPUs, we design a new algorithm and
fully optimize it to utilize GPU’s hardware architecture for high performance. Second, we
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propose operating system support for automatic device memory management to improve
the usability and performance of GPUs in shared general-purpose computing
environments. Several effective optimization techniques are employed to ensure the
efficient usage of GPU device memory space and to achieve high throughput. Finally, we
develop resource management facilities in GPU database systems to support concurrent
analytical query processing. By allowing multiple queries to execute simultaneously, the
resource utilization of GPUs can be greatly improved. It also enables GPU databases to
be utilized in important application areas where multiple user queries need to make
continuous progresses simultaneously.

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Dedication

To my family and dearest friends.

iv
Acknowledgments

This journey could not have been so memorable without the companion of many
wonderful people. My advisor opened the door and led me step by step to the essential
goal of doing impactful research. Xiaoning Ding and Rubao Li were great mentors whose
deep knowledge and consistent support smoothened the roughest roads. Yuan Yuan, Kai
Zhang, and Yin Huai walked shoulder to shoulder with me and lent the strongest arms
whenever help was needed. My girlfriend was always supportive and never complained
about my academic life that was often too busy to give her enough attention.
Thank you all!

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Vita

2006................................................................B.S. Computer Science and Engineering,
Northwestern Polytechnical University, Xi’an, China
2009................................................................M.S. Computer Science and Engineering,
Northwestern Polytechnical University, Xi’an, China
2010 to present ...............................................Ph.D. Computer Science and Engineering,
The Ohio State University

Publications

1. Kai Zhang, Kaibo Wang, Yuan Yuan, Rubao Lee, Lei Guo, Xiaodong Zhang. Mega-
KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores.
Proc. of VLDB Endow., 8(11):1226-1237, 2015.
2. Kaibo Wang, Kai Zhang, Yuan Yuan, Siyuan Ma, Rubao Lee, Xiaoning Ding,
Xiaodong Zhang. Concurrent Analytical Query Processing with GPUs. Proc. VLDB
Endow., 7(11):1011-1022, 2014.
3. Kaibo Wang, Xiaoning Ding, Rubao Lee, Shinpei Kato, Xiaodong Zhang. GDM:
Device Memory Management for GPGPU Computing. In Proceedings of the 2014 ACM
International Conference on Measurement and Modeling of Computer Systems
(SIGMETRICS 2014), pages 533-545, 2014.
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4. Kaibo Wang, Yin Huai, Rubao Lee, Fusheng Wang, Xiaodong Zhang, Joel H. Saltz.
Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems. In
Proc. VLDB Endow., 5(11):1543-1554, 2012.
5. Xiaoning Ding, Kaibo Wang, Phillip B. Gibbons, Xiaodong Zhang. BWS: Balanced
Work Stealing for Time-Sharing Multicores. In Proceedings of the 7th European
Conference on Computer Systems (EuroSys 2012), pages 365-378, 2012.
6. Xiaoning Ding, Kaibo Wang, Xiaodong Zhang. SRM-Buffer: An OS Buffer
Management Technique to Prevent Last Level Caches from Thrashing in Multicores. In
Proceedings of the 6th European Conference on Computer Systems (EuroSys 2011),
pages 243-256, 2011.
7. Xiaoning Ding, Kaibo Wang, Xiaodong Zhang. ULCC: A User-Level Facility for
Optimizing Shared Cache Performance on Multicores. In Proceedings of the 16th ACM
Symposium on Principles and Practice of Parallel Programming (PPoPP 2011), pages
103-112, 2011.

Fields of Study

Major Field: Computer Science and Engineering

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Table of Contents

Abstract ............................................................................................................................... ii
Dedication .......................................................................................................................... iv
Acknowledgments............................................................................................................... v
Vita ..................................................................................................................................... vi
Publications ........................................................................................................................ vi
Fields of Study .................................................................................................................. vii
Table of Contents ............................................................................................................. viii
List of Tables ................................................................................................................... xiii
List of Figures .................................................................................................................. xiv
Chapter 1 Introduction ........................................................................................................ 1
Chapter 2 Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid
Systems ............................................................................................................................... 6
2.1 Introduction ............................................................................................................... 6
2.2 Problem Identification ............................................................................................... 9
2.2.1 Background: Spatial Cross-Comparison ............................................................ 9
2.2.2 Existing Solutions with SDBMSs..................................................................... 11
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2.2.3 Performance Profiling of SDBMS Solution ..................................................... 13
2.3 The PixelBox Algorithm ......................................................................................... 15
3.1 Pixelization of Polygon Pairs .............................................................................. 15
2.3.2 Reduction of Computing Intensity ................................................................... 18
2.3.3 Optimized Algorithm Implementation ............................................................. 22
2.3.4 Related Discussions .......................................................................................... 26
2.4 System Framework .................................................................................................. 27
2.4.1 The Pipelined Structure .................................................................................... 28
2.4.2 Dynamic Task Migration .................................................................................. 30
2.5 Experiments ............................................................................................................. 31
2.5.1 Experiment Methodology ................................................................................. 31
2.5.2 Performance of the PixelBox Algorithm .......................................................... 33
2.5.3 Effectiveness of Optimization Techniques ....................................................... 36
2.5.4 Parameter Sensitivity of PixelBox .................................................................... 37
2.5.5 Performance of the Pipelined Framework ........................................................ 39
2.5.6 Effectiveness of Dynamic Task Migration ....................................................... 40
2.5.7 Performance Evaluation with All Data Sets ..................................................... 42
2.6 Related Work........................................................................................................... 43
2.7 Conclusions ............................................................................................................. 45
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