독재영 교수

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple

Data storage devices are getting "smarter." Smart Flash storage devices (a.k.a. "Smart SSD") are on the horizon and will package CPU processing and DRAM storage inside a Smart SSD, and make that available to run user programs inside a Smart SSD. The focus of this paper is on exploring the opportunities and challenges associated with exploiting this functionality of Smart SSDs for relational analytic query processing. We have implemented an initial prototype of Microsoft SQL Server running on a S

Flash solid-state drives (SSDs) are changing the I/O landscape, which has largely been dominated by traditional hard disk drives (HDDs) for the last 50 years. In this paper we propose and systematically explore designs for using an SSD to improve the performance of a DBMS buffer manager. We propose three alternatives that differ mainly in the way that they deal with the dirty pages evicted from the buffer pool. We implemented these alternatives, as well another recently proposed algorithm for th

The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and energy-efficient computational storage developed for realizing the full potential of in-storage

Programmable software-defined solid-state drives can move computing functions closer to storage.

<inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale cloud services, as it easily scales up the database while offering fast search. On the other hand, a computational storage device (CSD) that combines programmable logic and storage modules on a single board becomes popular to address the data bandwid

Flash solid state drives (SSDs) provide an attractive alternative to traditional magnetic hard disk drives (HDDs) for DBMS applications. Naturally there is substantial interest in redesigning critical database internals, such as join algorithms, for flash SSDs. However, we must carefully consider the lessons that we have learnt from over three decades of designing and tuning algorithms for magnetic HDD-based systems, so that we continue to reuse techniques that worked for magnetic HDDs and also

Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices, while providing the low access latency and byte-addressablity of traditional DRAM memory. In this paper, we provide extensive performance evaluations on a recently released NVM device, Intel Optane DC Persistent Memory (PMem), under different configurations with several micro-benchmark tools. Further, we evaluate OLTP and OLAP database workloads with Microsoft SQL Ser

Referring image segmentation aims to localize the object in an image referred by a natural language expression. Most previous studies learn referring image segmentation with a large-scale dataset containing segmentation labels, but they are costly. We present a weakly supervised learning method for referring image segmentation that only uses readily available image-text pairs. We first train a visual-linguistic model for image-text matching and extract a visual saliency map through Grad-CAM to i

While clinical notes are essential to the field of healthcare, they pose several challenges for clinicians since it is difficult to write down medical information, review prior notes, and extract the desired information at the same time while examining a patient. Thus, we designed a system that can automatically generate clinical notes from dialogues between patients and clinicians and provide specific information upon clinicians’ query using a Large Language Model (LLM) both in real-time. To ex

A promising use of flash SSDs in a DBMS is to extend the main memory buffer pool by caching selected pages that have been evicted from the buffer pool. Such a use has been shown to produce significant gains in the steady state performance of the DBMS. One strategy for using the SSD buffer pool is to throw away the data in the SSD when the system is restarted (either when recovering from a crash or restarting after a shutdown), and consequently a long “ramp-up” period to regain peak performance i

Data storage devices are getting smarter. Smart flash storage devices (a.k.a. Smart SSDs) are on the horizon and package a small programmable computer inside the device. Thus, users can run code closer to the data right inside the SSD, on the “other” side of the I/O bus. The focus of this paper is on exploring the opportunities and challenges associated with exploiting this functionality of Smart SSDs for relational analytic query processing. We have implemented an initial prototype of Microsoft

Computational Storage Devices (CSDs), which are storage devices including general-purpose, special-purpose, and/or re-configurable processing units, are now becoming commercially available from different vendors. CSDs are capable of running software that usually runs on the host CPU – but on the storage device, where the data reside. Thus, a server with one or more CSDs may improve the overall performance and energy consumption of software dealing with a large amount of data. With the aim of fos

Exploiting a storage hierarchy is critical to cost-effective data management. One can achieve great performance when working solely on main memory data. But this comes at a high cost. Systems that use secondary storage as the "home" for data have much lower storage costs as they can not only make the data durable but reduce its storage cost as well. Performance then becomes the challenge, reflected in an increased execution cost. Log structured stores, e.g. Deuteronomy, improve I/O cost/performa