.png){kind=icon}
返回
Computing En-Route for Near-Data Processing
.png){kind=icon}
PRE
AI
DOI：10.1109/TC.2021.3063378.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The data explosion and faster data analysis demand have spawned emerging applications that operate over myriads of data and exhibit large memory footprints with low data reuse rate. Such characteristics lead to enormous data movements across the memory hierarchy and pose significant pressure on modern communication fabrics and memory subsystems. To mitigate the worsening gap between high processor computation density and deficient memory bandwidth, memory networks, and near-data processing techniques are proposed to keep improving system performance and energy efficiency. In this article, we propose Active-Routing, an in-network near-data processing architecture for data-flow execution, which enables computation en-route by exploiting patterns of aggregation over intermediate results. The proposed architecture leverages the massive memory cube- and vault-level parallelism as well as network concurrency to optimize the aggregation operations along a dynamically built Active-Routing Tree. It also introduces page granular computation offloading to amortize the offloading overhead and improve the throughput. Compared to the state-of-the-art processing-in-memory architecture, the evaluations show that the baseline Active-Routing can achieve up to 7x speedup with an average of 60 percent performance improvement, and reduce the energy-delay product by 80 percent across various benchmarks. Further optimizations with vault-level parallelism and page granular offloading can achieve an extra order of magnitude improvement.
Keyword:
Bandwidth
Parallel processing
Kernel
Optimization
Random access memory
Memory management
Fabrics
Memory network
data-flow
in-network computing
near-data processing
processing-in-memory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
5.3K
被引数:
9.8K
机构
引用论文
.png){kind=icon}
查看更多内容