國立虎尾科技大學 |

Generating Thread-Level Parallelism in Nondeterministic Programs.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Generating Thread-Level Parallelism in Nondeterministic Programs./
作者:	Deiana, Enrico Armenio.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798381175103

Generating Thread-Level Parallelism in Nondeterministic Programs.
Deiana, Enrico Armenio.

Generating Thread-Level Parallelism in Nondeterministic Programs. - 1 online resource (139 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

Thesis (Ph.D.)--Northwestern University, 2023.

Includes bibliographical references

Chip Multiprocessors (CMP) are everywhere, from mobile systems to servers. Thread-Level Parallelism (TLP) is the characteristic of a program that makes use of the parallel cores of a CMP to improve performance. Programming language abstractions are a way to generate TLP, which allows CMP to reach their full potential.This dissertation focuses on two main contributions:• STATS, a parallelizing compiler designed to extract TLP from nondeterministic programs by leveraging a novel programming language abstraction.• CARMOT, a tool intended to aid programmers in effectively utilizing this innovative programming language abstraction.TLP in today's programs is limited by data dependences that must be satisfied as the program executes. Nondeterministic programs suffer from this same limitation, but the nondeterminism gives them an additional degree of freedom that deterministic programs do not have: the ability to satisfy some dependences with many different data, which results in different outputs even when they run with the same input. Some of these outputs can be generated more quickly in parallel than others can. STATS is the first compiler to generate a new source of parallelism that has never been explored before, and it does so by taking advantage of this extra degree of freedom in nondeterministic programs. This resulted in STATS being able to achieve significant performance improvements in nondeterministic programs. To use STATS, developers have to express this additional degree of freedom in the code explicitly. STATS enables developers to encode this knowledge by extending the C++ language with a new abstraction.While STATS allows developers to obtain significantly more performance, using the C++ abstraction we introduced can be challenging in large code-bases. To assist developers in using this abstraction, we created a new tool called CARMOT. We found that CARMOT can also support developers in using many other programming language abstractions beyond the STATS abstraction. Hence, we developed an approach that generalizes the STATS-specific needs to reach many other modern programming language abstractions such as those offered by OpenMP pragmas and C++ features such as smart pointers.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381175103Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

CompilerIndex Terms--Genre/Form:

554714
Electronic books.

Generating Thread-Level Parallelism in Nondeterministic Programs.
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