東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Automated Generation of Domain Specific Kernels.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated Generation of Domain Specific Kernels./
Author:	Cowan, Meghan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	88 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: A.
Contained By:	Dissertations Abstracts International83-02A.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544887
ISBN:	9798535503783

Automated Generation of Domain Specific Kernels.
Cowan, Meghan.

Automated Generation of Domain Specific Kernels. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 88 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: A.

Thesis (Ph.D.)--University of Washington, 2021.

This item must not be sold to any third party vendors.

Seamless gains in performance from technology scaling is coming to an end, but many applications rely on hardware and their compilation stacks to continue improving performance and efficiency. In order to keep up with application compute demands, emerging hardware is becoming more diverse, specialized, and complex. New hardware and accelerators expose programming models that have great potential performance, but are often more restrictive and difficult to program. Often times, even traditional compilers struggle to generate efficient programs for these new programming models, leading to a proliferation of domain specific libraries comprised of hand-optimized kernels that are meticulously tuned to take advantage of the target hardware and avoid any bottlenecks.This thesis argues that we can automatically generate and optimize programs by building domain specific tools that search for efficient code. I show how we can apply two search-based methods, program synthesis and autotuning, to take advantage of application specific optimizations such as efficiently utilizing vector parallelism present in many programming models.In this dissertation, I demonstrate how we can optimize programs in machine learning and Homomorphic Encryption (HE) using search-based methods. First, I show how we can extend existing machine learning compilers to efficiently deploy unconventional neural networks, such as ultra quantized networks, by augmenting core operators with synthesized vector code and intelligently tuned memory access behavior. Then, I present Porcupine, a synthesizng compiler for vectorized HE programs. Porcupine synthesizes vectorized code from a plaintext scalar reference program, and performs instruction selection and scheduling for HE's unique performance model. Together, these two systems show how we can use search to both tune and discover optimizations in programs.

ISBN: 9798535503783Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Automated generation

Automated Generation of Domain Specific Kernels.
LDR:03010nmm a2200361 4500 001 2348397
005 20220908125735.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798535503783
035 $a (MiAaPQ)AAI28544887
035 $a AAI28544887
040 $a MiAaPQ $c MiAaPQ
100 1 $a Cowan, Meghan. $3 3687740
245 1 0 $a Automated Generation of Domain Specific Kernels.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 88 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: A.
500 $a Advisor: Ceze, Luis.
502 $a Thesis (Ph.D.)--University of Washington, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Seamless gains in performance from technology scaling is coming to an end, but many applications rely on hardware and their compilation stacks to continue improving performance and efficiency. In order to keep up with application compute demands, emerging hardware is becoming more diverse, specialized, and complex. New hardware and accelerators expose programming models that have great potential performance, but are often more restrictive and difficult to program. Often times, even traditional compilers struggle to generate efficient programs for these new programming models, leading to a proliferation of domain specific libraries comprised of hand-optimized kernels that are meticulously tuned to take advantage of the target hardware and avoid any bottlenecks.This thesis argues that we can automatically generate and optimize programs by building domain specific tools that search for efficient code. I show how we can apply two search-based methods, program synthesis and autotuning, to take advantage of application specific optimizations such as efficiently utilizing vector parallelism present in many programming models.In this dissertation, I demonstrate how we can optimize programs in machine learning and Homomorphic Encryption (HE) using search-based methods. First, I show how we can extend existing machine learning compilers to efficiently deploy unconventional neural networks, such as ultra quantized networks, by augmenting core operators with synthesized vector code and intelligently tuned memory access behavior. Then, I present Porcupine, a synthesizng compiler for vectorized HE programs. Porcupine synthesizes vectorized code from a plaintext scalar reference program, and performs instruction selection and scheduling for HE's unique performance model. Together, these two systems show how we can use search to both tune and discover optimizations in programs.
590 $a School code: 0250.
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
650 4 $a Dissertations & theses. $3 3560115
650 4 $a Design. $3 518875
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Neural networks. $3 677449
653 $a Automated generation
653 $a Domain specific kernels
653 $a Homomorphic Encryption
653 $a Computer hardware
690 $a 0984
690 $a 0464
690 $a 0389
710 2 $a University of Washington. $b Computer Science and Engineering. $3 2097608
773 0 $t Dissertations Abstracts International $g 83-02A.
790 $a 0250
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544887