Hardware acceleration of graph analysis applications
Abstract/Contents
- Abstract
- Graphs are powerful data representations favored in many computational domains. Analytics and knowledge extraction on these data structures has become an area of great interest, particularly with the increasing prevalence of large data sets now becoming commonplace in data centers. Due to the scale of these operations, energy efficiency on frequent tasks such as graph analysis will be very important as the domain and the data sizes continue to grow. Dedicated hardware accelerators are one high-performing yet energy-efficient approach to this problem. Unfortunately, they are notoriously labor-intensive to design and verify while meeting stringent time-to-market goals. This thesis presents GraphOps, a modular hardware library for quickly and easily constructing energy-efficient accelerators for graph analytics algorithms. GraphOps provide a hardware designer with a set of composable graph-specific building blocks broad enough to target a wide array of graph analytics algorithms. The system is built upon a streaming execution platform and targets FPGAs, allowing a vendor to use the same hardware to accelerate different types of analytics computation. Stubborn hardware implementation details such as flow control, input buffering, rate throttling, and host/interrupt interaction are automatically handled and built into the design of the GraphOps, greatly reducing design time. As an enabling contribution, this thesis presents a novel locality-optimized graph data structure that improves the efficiency of memory access to the graph.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Oguntebi, Kunle Oluwatayo, Jr |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Olukotun, Oyekunle Ayinde |
| Thesis advisor | Olukotun, Oyekunle Ayinde |
| Thesis advisor | Kozyrakis, Christoforos, 1974- |
| Thesis advisor | Mitra, Subhasish |
| Advisor | Kozyrakis, Christoforos, 1974- |
| Advisor | Mitra, Subhasish |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kunle Oluwatayo Oguntebi, Jr. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Kunle Oluwatayo Oguntebi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...