A hardware and software architecture for pervasive parallelism
Name
1220833661-MIT.pdf
Size
3.92 MB
Format
Adobe PDF
Checksum (MD5)
babb78a6ae9f1599ce941c041b305d06
Author(s)
Jeffrey, Mark Christopher.
Advisor(s)
Daniel Sanchez.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Parallelism is critical to achieve high performance in modern computer systems. Unfortunately, most programs scale poorly beyond a few cores, and those that scale well often require heroic implementation efforts. This is because current parallel architectures squander most of the parallelism available in applications and are too hard to program. This thesis presents Swarm, a new execution model, architecture, and system software that exploits far more parallelism than conventional multicores, yet is almost as easy to program as a sequential machine. Programmer-ordered tasks sit at the software-hardware interface. Swarm programs consist of tiny tasks, as small as tens of instructions each. Parallelism is dynamic: tasks can create new tasks at run time. Synchronization is implicit: the programmer specifies a total or partial order on tasks. This eliminates the correctness pitfalls of explicit synchronization (e.g., deadlock and data races). Swarm hardware uncovers parallelism by speculatively running tasks out of order, even thousands of tasks ahead of the earliest active task. Its speculation mechanisms build on decades of prior work, but Swarm is the first parallel architecture to scale to hundreds of cores due to its new programming model, distributed structures, and distributed protocols. Leaning on its support for task order, Swarm incorporates new techniques to reduce data movement, to speculate selectively for improved efficiency, and to compose parallelism across abstraction layers. Swarm achieves efficient near-linear scaling to hundreds of cores on otherwise hard-to-scale irregular applications. These span a broad set of domains, including graph analytics, discrete-event simulation, databases, machine learning, and genomics. Swarm even accelerates applications that are conventionally deemed sequential. It outperforms recent software-only parallel algorithms by one to two orders of magnitude, and sequential implementations by up to 600� at 256 cores.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 139-167).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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