Eun-Gyu Kim

dblp:74/924 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 32% Processor architecture and microarchitecture · 32% Distributed systems · 16%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
processing-in-memory
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
Memory systems › processing-in-memory
processor-in-memory architecture
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
Distributed systems
stream processing
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
Processor architecture and microarchitecture › data-parallel architecture
stream processor
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
Parallel and multicore computing › parallel algorithms › parallel algorithm design
tiled algorithm
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
Processor architecture and microarchitecture
tiled architecture
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003
High-performance computing
scientific computing systems
0.012003
A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels · ISCA 2003

Methods — techniques the papers use, named apart from their topics

simulation · 0.0performance measurement · 0.0
YearPublicationVenuePosition
2023 Making Data Engineering Declarative
Michael Armbrust, Ali Ghodsi 0002, Reynold Xin, Vuk Ercegovac, Sourav Chatterji, Eun-Gyu Kim, Paul Lappas, Yannis Papakonstantinou, Yingyi Bu, Yijia Cui, Rahul Govind, Aakash Japi, Kiavash Kianfar, Jon Mio, Mukul Murthy, Supun Nakandala, Yannis Sismanis, Justin Tang, Joseph Torres
CIDR6
2003 A Performance Analysis of PIM, Stream Processing, and Tiled Processing on Memory-Intensive Signal Processing Kernels
abstract
Trends in microprocessors of increasing die size and clock speed and decreasing feature sizes have fueled rapidly increasing performance. However, the limited improvements in DRAM latency and bandwidth and diminishing returns of increasing superscalar ILP and cache sizes have led to the proposal of new microprocessor architectures that implement processor-in- memory, stream processing, and tiled processing. Each architecture is typically evaluated separately and compared to a baseline architecture. In this paper, we evaluate the performance of processors that implement these architectures on a common set of signal processing kernels.The implementation results are compared with the measured performance of a conventional system based on the PowerPC with Altivec. The results show that these new processors show significant improvements over conventional systems and that each architecture has its own strengths and weaknesses.
Jinwoo Suh, Eun-Gyu Kim, Stephen P. Crago, Lakshmi Srinivasan, Matthew C. French
ISCA2