Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Gyuyoung Park

dblp:206/7343 · DBLP profile ↗
← Back
5ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0002-4916-6576ORCID · corroborated

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

Systems, architecture and hardware · 5 · 2 first-author · 1 since 2021Software engineering, systems software and programming languages · 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
2 papers
Memory systems · 72% Hardware accelerators and domain-specific architectures · 16% Energy-efficient computing · 6%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
non-volatile memory
1.022022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022
DRAM-Less: Hardware Acceleration of Data Processing with New Memory · HPCA 2020
Operating systems
persistence
0.612022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022
Operating systems › persistence
whole-system persistence
0.612022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022
Memory systems › non-volatile memory
persistent memory
0.612022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022
Hardware accelerators and domain-specific architectures › domain-specific accelerator
data processing accelerator
0.412020
DRAM-Less: Hardware Acceleration of Data Processing with New Memory · HPCA 2020
Memory systems › non-volatile memory
phase change memory
0.412020
DRAM-Less: Hardware Acceleration of Data Processing with New Memory · HPCA 2020
Hardware reliability and fault tolerance
power failure resilience
0.212022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022
Energy-efficient computing
power management
0.212022
LightPC: hardware and software co-design for energy-efficient full system persistence · ISCA 2022

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

open-channel PMEM · 1.1hardware-software co-design · 1.1PRAM integration · 0.4PCIe accelerator emulation · 0.4
YearPublicationVenuePosition
2022 LightPC: hardware and software co-design for energy-efficient full system persistence
abstract
We propose LightPC, a lightweight persistence-centric platform to make the system robust against power loss. LightPC consists of hardware and software subsystems, each being referred to as open-channel PMEM (OC-PMEM) and persistence-centric OS (PecOS). OC-PMEM removes physical and logical boundaries in drawing a line between volatile and nonvolatile data structures by unshackling new memory media from conventional PMEM complex. PecOS provides a single execution persistence cut to quickly convert the execution states to persistent information in cases of a power failure, which can eliminate persistent control overhead. We prototype LightPC's computing complex and OC-PMEM using our custom system board. PecOS is implemented based on Linux 4.19 and Berkeley bootloader on the hardware prototype. Our evaluation results show that OC-PMEM can make user-level performance comparable with a DRAM-only non-persistent system, while consuming 73% lower power and 69% less energy. LightPC also shortens the execution time of diverse HPC, SPEC, and In-memory DB workloads, compared to traditional persistent systems by 4.3X, on average.
Sangwon Lee 0014, Miryeong Kwon, Gyuyoung Park, Myoungsoo Jung
ISCA3
2020 TensorPRAM: Designing a Scalable Heterogeneous Deep Learning Accelerator with Byte-addressable PRAMs
Sangwon Lee 0014, Gyuyoung Park, Myoungsoo Jung
HotStorage2
2020 DRAM-Less: Hardware Acceleration of Data Processing with New Memory
abstract
General purpose hardware accelerators have become major data processing resources in many computing domains. However, the processing capability of hardware accelerations is often limited by costly software interventions and memory copies to support compulsory data movement between different processors and solid-state drives (SSDs). This in turn also wastes a significant amount of energy in modern accelerated systems. In this work, we propose, DRAM-less, a hardware automation approach that precisely integrates many state-of-the-art phase change memory (PRAM) modules into its data processing network to dramatically reduce unnecessary data copies with a minimum of software modifications. We implement a new memory controller that plugs a real 3x nm multi-partition PRAM to 28nm technology FPGA logic cells and interoperate its design into a real PCIe accelerator emulation platform. The evaluation results reveal that our DRAM-less achieves, on average, 47% better performance than advanced acceleration approaches that use a peer-to-peer DMA.
Jie Zhang 0048, Gyuyoung Park, David Donofrio, John Shalf, Myoungsoo Jung
HPCA2
2020 Automatic-SSD: Full Hardware Automation over New Memory for High Performance and Energy Efficient PCIe Storage Cards
abstract
We propose Automatic-SSD that converts all storage management logic into hardware, which enable energy efficient, high performance fast memory based block storage. To achieve low operating power, Automatic-SSD directly reads or writes host-side data to underlying backend storage media without internal DRAM caches. To realize such DRAM-less approach with better performance and make it more energy efficient, Automatic-SSD also removes the internal processor(s) and firmware execution therein by fully automating the backend request management and data transfers over all pipelined hardware modules. We prototype Automatic-SSD on a middle-end FPGA custom board, employing massive numbers of phase change memories as representative of new memory technologies. Our evaluation results show that, compared to a conventional firmware-based approach, Automatic-SSD shows up 28.8× and 25.4× better bandwidth and latency behaviors, respectively, while consuming only 5% of the total energy, on overage.
Gyuyoung Park, Myoungsoo Jung
ICCAD1
2018 BIBIM: A Prototype Multi-Partition Aware Heterogeneous New Memory
Gyuyoung Park, Miryeong Kwon, Pratyush Mahapatra, Michael M. Swift, Myoungsoo Jung
HotStorage1