Sequential data access for the rapid ingestion of large fact tables from storage is a pivotal yet resource-intensive operation in data warehouse systems, consuming substantial CPU cycles across various components of DBMSs and operating systems. Although bypassing these layers can eliminate access latency, concurrent access to the same table often results in redundant data fetching due to cache-bypassing data transfers. Thus, a new design for data access control is necessary to enhance rapid data ingestion in databases. To address this concern, we propose a novel DB-OS co-design that efficiently supports sequential data access at full device speed. Our approach, zicIO, liberates DBMSs from data access control by preparing required data just before DBMSs access it, while alleviating all known I/O latencies. The core of zicIO lies in its DB-OS co-design, which aims to (1) automate data access control and (2) relieve redundant data fetching through seamless collaboration between the DB and the OS. We implemented zicIO and integrated it with four databases to demonstrate its general applicability. The evaluation showed performance enhancements of up to 9.95x under TPC-H loads.

HTAP systems aim to unify operational and analytical workloads, yet real-time analytics remains constrained by the overhead of extract-transform-load (ETL) operations. Existing solutions often rely on dual-system architectures, incurring substantial resource costs and delays from data reformatting and relocation. We present TracerETL , a progressive ETL framework that enables real-time analytics in standalone DBMSs through instant tuple location discovery during transformation. At its core is Tracer , a counting-based tuple tracking mechanism that constructs a tuple trace vector using per-partition counters. This trace vector deterministically encodes each tuple's future relocation path with arrival order across transformation levels, enabling precise data access at any stage-without auxiliary indexes. We implement TracerETL in PostgreSQL and evaluate it against OLAP- and OLTP-optimized DBMSs. Experimental results show that PostgreSQL with TracerETL accelerates real-time HTAP queries by up to 127×, while efficiently handling progressive data conversion in a standalone DBMS.

High Efficiency Video Coding (HEVC) employs a coding unit (CU), prediction unit (PU), and transform unit (TU) based on the quadtree coding tree unit (CTU) structure to improve coding efficiency. However, the computational complexity increases greatly because the rate-distortion (RD) optimization process should be performed for all CUs, PUs, and TUs to obtain the optimal CTU partition. In this paper, a fast CU size decision algorithm is proposed to reduce the encoder complexity of HEVC. Based on the statistical analysis, three approaches with SKIP mode decision (SMD), CU skip estimation (CUSE), and early CU termination (ECUT) are considered. In SMD, it is determined that the remaining modes except for SKIP mode are preformed or not. CUSE and ECUT determine that larger CU sizes and smaller CU sizes are coded or not, respectively. Thresholds for SMD, CUSE, and ECUT are designed based on Bayes' rule with a complexity factor. Update process is performed to estimate the statistical parameters for SMD, CUSE, and ECUT considering the characteristic of RD cost. The experimental results demonstrate that the proposed CU size decision algorithm significantly reduces computational complexity by 69% on average with 2.99% Bjøntegaard difference bitrate (BDBR) increase for random access. The complexity reduction and BDBR increase for low delay are 68% and 2.46%, respectively. The experimental results also show that our proposed scheme performs well for various characteristic of sequences and outperforms the two previous state-of-the-art works.

High Efficiency Video Coding (HEVC) is developed for next-generation video coding, which achieves significant improvements in coding efficiency compared with H.264/Advanced Video Coding by adopting various tools including a quadtree-based block partitioning structure. However, this causes high encoding complexity for exhaustive rate-distortion (RD) cost computation of the extended prediction unit (PU) searching. In this paper, a fast PU skip and split termination algorithm is proposed. The proposed method consists of three algorithms: 1) early skip; 2) PU skip; and 3) PU split termination. The early skip algorithm allows immediate skipping of the RD cost computation for large PUs according to the neighboring PUs. Based on Bayes's rule, the PU skip algorithm allows skipping of the full RD cost computation, and the split termination algorithm terminates further PU splitting using the RD cost of rough mode decision (RMD). The decision parameter for the PU skip and the split termination is presented as the ratio of the RMD RD costs between the current PU and the spatially adjacent or upper depth PU. The simulation results show that the proposed algorithm achieves a saving of 53.52% encoding time while maintaining almost the same RD performances as the HEVC reference software.