자료유형  

 학위논문

Control Number  

0017164878

International Standard Book Number  

9798346381693

Dewey Decimal Classification Number  

005

Main Entry-Personal Name  

Zhang, Nathan.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

128 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-05, Section: A.

General Note  

Advisor: Olukotun, Oyekunle.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약In recent years, machine learning has driven massively increased computational demand at all scales - ranging from kiloflop-scale per-packet networking analytics to petaflop-scale large language models (LLMs). Spatial accelerators such as field programmable gate arrays (FPGAs) and other reconfigurable dataflow architectures (RDAs) have emerged as promising candidates, offering significant performance-per-watt improvements over instruction-based architectures such as CPUs and GPUs. However, their widespread adoption is hampered by limitations in programming models and tools. This dissertation tackles two key challenges: (1) constructing efficiently composable libraries for these accelerators, and (2) enabling the parallel simulation of dataflow systems, which are distributed processing units connected by communication channels.Previous work in programming spatial accelerators made writing individual applications significantly easier by lifting the level of abstraction. However, writing single applications is insufficient; reusable libraries are essential to compose larger programs. We first characterize the hierarchical pipeline promotion problem (HPPP), a performance trap which prevents the creation of high performance libraries when implemented with traditional abstractions. We then present streaming tensor interfaces (STIs), a software design pattern which bypasses the HPPP by constructing decoupled streaming pipelines. Using STIs, we construct an efficient library for performing inference, achieving a throughput of 10 cycles-per-inference without batching.We then turn our focus to the simulation of dataflow systems, which sits at the heart of research - from functional testing to performance estimation. Unfortunately, existing parallel software simulation schemes are ill-suited to such systems; optimistic schemes place a large burden on the end-user to reason about undoing speculation errors, while current conservative schemes scale poorly in the face of low-latency high-bandwidth communication. To address these limitations, we introduce the Dataflow Abstract Machine (DAM), a parallel simulator framework which breaks from tradition in both user interface and execution. DAM replaces event-driven modeling with communicating sequential processes, bulk-synchronous time with asynchronous distributed time, and global synchronization with a peer-to-peer protocol. DAM requires 57% less code compared to an existing cycle-based simulation, and outperforms a state-of-the-art simulation framework by at least 2x. Compared to ad-hoc research simulators, DAM achieves speedups of up to five orders of magnitude.Ultimately, both of these advancements are enabled by new abstractions. In this thesis, we will argue that abstractions are key, as abstractions constrain implementations, and implementations dictate performance.

Subject Added Entry-Topical Term  

Programming languages.

Subject Added Entry-Topical Term  

Systems design.

Subject Added Entry-Topical Term  

Libraries.

Subject Added Entry-Topical Term  

Software engineering.

Subject Added Entry-Topical Term  

Linear algebra.

Subject Added Entry-Topical Term  

Semantics.

Subject Added Entry-Topical Term  

Engineers.

Subject Added Entry-Topical Term  

Computer engineering.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Design.

Subject Added Entry-Topical Term  

Logic.

Subject Added Entry-Topical Term  

Systems science.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-05A.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:655952