Hardware Accelerated Semantic Declarative Memory Systems through CUDA and MapReduce
| Autor: | Tarek M. Taha, Tanvir Atahary, Scott Douglass, Mark Edmonds |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Speedup business.industry Computer science Process (computing) 02 engineering and technology Semantic network CUDA Constant (computer programming) Computational Theory and Mathematics Hardware and Architecture Signal Processing Scalability 0202 electrical engineering electronic engineering information engineering business Declarative memory Associative property Computer hardware |
| Zdroj: | IEEE Transactions on Parallel and Distributed Systems. 30:601-614 |
| ISSN: | 2161-9883 1045-9219 |
| DOI: | 10.1109/tpds.2018.2866848 |
| Popis: | Declarative memory enables cognitive agents to effectively store and retrieve factual memory in real-time. Increasing the capacity of a real-time agent's declarative memory increases an agent's ability to interact intelligently with its environment but requires a scalable retrieval system. This work represents an extension of the Accelerated Declarative Memory (ADM) system, referred to as Hardware Accelerated Declarative Memory (HADM), to execute retrievals on a GPU. HADM also presents improvements over ADM's CPU execution and considers critical behavior for indefinitely running declarative memories. The negative effects of a constant maximum associative strength are considered, and mitigating solutions are proposed. HADM utilizes a GPU to process the entire semantic network in parallel during retrievals, yielding significantly faster declarative retrievals. The resulting GPU-accelerated retrievals show an average speedup of approximately 70 times over the previous Service Oriented Architecture Declarative Memory (soaDM) implementation and an average speedup of approximately 5 times over ADM. HADM is the first GPU-accelerated declarative memory system in existence. |
| Databáze: | OpenAIRE |
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