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AUTOTUNING, CODE GENERATION AND OPTIMIZING COMPILER
TECHNOLOGY FOR GPUS
by
Malik Muhammad Zaki Murtaza Khan
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2012
Copyright 2012 Malik Muhammad Zaki Murtaza Khan
Object Description
| Title | Autotuning, code generation and optimizing compiler technology for GPUs |
| Author | Khan, Malik Muhammad Zaki Murtaza |
| Author email | zakimurtaza@gmail.com;zakimurtaza@hotmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-03-22 |
| Date submitted | 2012-05-01 |
| Date approved | 2012-05-02 |
| Restricted until | 2012-05-02 |
| Date published | 2012-05-02 |
| Advisor (committee chair) |
Hall, Mary Nakano, Aiichiro |
| Advisor (committee member) | Prasanna, Viktor |
| Abstract | Graphics Processing Units (GPUs) have evolved to devices with teraflop-level performance potential. Application developers have a tedious task in developing GPU software by correctly identifying parallel computation and optimizing placement of data for the parallel processors in such architectures. Further, code optimized for one architecture may not perform well on different generations of even the same processor family. Many manually tuned GPU solutions would need a complete rewrite on a different architecture, hence more programmer time and effort. High-performance computing on GPUs can be facilitated by programming models and programming frameworks that attempt to reduce the amount of time and effort needed to develop GPU applications. ❧ This thesis describes a compiler framework for automatically generating and optimizing parallel code for GPUs (CUDA code for Nvidia GPUs), relieving programmers of the tedious work of parallelizing sequential code. The framework describes a script-based compiler for CUDA code generation combining: (1) a Transformation Strategy Generator (TSG), which automatically generates multiple scripts representing different optimization strategies; (2) an Autotuning System that automatically generates a set of code variants and selects the best among them through empirical evaluation. ❧ An underlying loop transformation and code generation framework takes TSG-generated scripts as input and generates CUDA code; thus enabling an end-to-end system to produce high performance solutions for scientific computations on a given GPU architecture. ❧ This flexible organization enables the system to explore a large optimization search space, simultaneously targeting different architecture features, but constrained by data dependences and guided by data reuse and the best heuristics from manual tuning. The system tailors the generated code to yield high-performance results for different GPU generations, data types and data sets. ❧ The key contributions of this thesis include: (1) the meta-optimizer, TSG,(2) a search and autotuning mechanism, (3) integration with a script-based compiler framework, resulting in an end-to-end automatic parallelization system. (4) performance portable code generation for the Nvidia GTX-280 and Nvidia Tesla C2050 Fermi architectures, and (5) performance gains of up to 1.84x over linear algebra kernels in the manually-tuned Nvidia CUBLAS library, and up to 2.03x for a set of scientific, multimedia and imaging kernels over a state-of-the-art GPU compiler. |
| Keyword | code optimization; compiler technology; autotuning; GPGPU; CUDA; GPU programming. |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Khan, Malik Muhammad Zaki Murtaza |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-KhanMalikM-710.pdf |
Description
| Title | Page 1 |
| Full text | AUTOTUNING, CODE GENERATION AND OPTIMIZING COMPILER TECHNOLOGY FOR GPUS by Malik Muhammad Zaki Murtaza Khan A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) May 2012 Copyright 2012 Malik Muhammad Zaki Murtaza Khan |
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