Journal of Computer Science and Technology最新文献

Data partitioning techniques are pivotal for optimal data placement across storage devices, thereby enhancing resource utilization and overall system throughput. However, the design of effective partition schemes faces multiple challenges, including considerations of the cluster environment, storage device characteristics, optimization objectives, and the balance between partition quality and computational efficiency. Furthermore, dynamic environments necessitate robust partition detection mechanisms. This paper presents a comprehensive survey structured around partition deployment environments, outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed. We discuss partitioning features pertaining to database schema, table data, workload, and runtime metrics. We then delve into the partition generation process, segmenting it into initialization and optimization stages. A comparative analysis of partition generation and update algorithms is provided, emphasizing their suitability for different scenarios and optimization objectives. Additionally, we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and solutions. This survey aims to foster the implementation, deployment, and updating of high-quality partitions for specific system scenarios.

Given a query patch from a novel class, one-shot object detection aims to detect all instances of this class in a target image through the semantic similarity comparison. However, due to the extremely limited guidance in the novel class as well as the unseen appearance difference between the query and target instances, it is difficult to appropriately exploit their semantic similarity and generalize well. To mitigate this problem, we present a universal Cross-Attention Transformer (CAT) module for accurate and efficient semantic similarity comparison in one-shot object detection. The proposed CAT utilizes the transformer mechanism to comprehensively capture bi-directional correspondence between any paired pixels from the query and the target image, which empowers us to sufficiently exploit their semantic characteristics for accurate similarity comparison. In addition, the proposed CAT enables feature dimensionality compression for inference speedup without performance loss. Extensive experiments on three object detection datasets MS-COCO, PASCAL VOC and FSOD under the one-shot setting demonstrate the effectiveness and efficiency of our model, e.g., it surpasses CoAE, a major baseline in this task, by 1.0% in average precision (AP) on MS-COCO and runs nearly 2.5 times faster.

Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors. In this paper, we propose a random subspace sampling method, RSS, by sampling missing items from the corresponding feature histogram distributions in random subspaces, which is effective and efficient at different levels of missing data. Unlike most established approaches, RSS does not train on fixed imputed datasets. Instead, we design a dynamic training strategy where the filled values change dynamically by resampling during training. Moreover, thanks to the sampling strategy, we design an ensemble testing strategy where we combine the results of multiple runs of a single model, which is more efficient and resource-saving than previous ensemble methods. Finally, we combine these two strategies with the random subspace method, which makes our estimations more robust and accurate. The effectiveness of the proposed RSS method is well validated by experimental studies.

The goal of qubit mapping is to map a logical circuit to a physical device by introducing additional gates as few as possible in an acceptable amount of time. We present an effective approach called Tabu Search Based Adjustment (TSA) algorithm to construct the mappings. It consists of two key steps: one is making use of a combined subgraph isomorphism and completion to initialize some candidate mappings, and the other is dynamically modifying the mappings by TSA. Our experiments show that, compared with state-of-the-art methods, TSA can generate mappings with a smaller number of additional gates and have better scalability for large-scale circuits.

Graph processing has been widely used in many scenarios, from scientific computing to artificial intelligence. Graph processing exhibits irregular computational parallelism and random memory accesses, unlike traditional workloads. Therefore, running graph processing workloads on conventional architectures (e.g., CPUs and GPUs) often shows a significantly low compute-memory ratio with few performance benefits, which can be, in many cases, even slower than a specialized single-thread graph algorithm. While domain-specific hardware designs are essential for graph processing, it is still challenging to transform the hardware capability to performance boost without coupled software codesigns. This article presents a graph processing ecosystem from hardware to software. We start by introducing a series of hardware accelerators as the foundation of this ecosystem. Subsequently, the codesigned parallel graph systems and their distributed techniques are presented to support graph applications. Finally, we introduce our efforts on novel graph applications and hardware architectures. Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.

Direct volume rendering (DVR) is a technique that emphasizes structures of interest (SOIs) within a volume visually, while simultaneously depicting adjacent regional information, e.g., the spatial location of a structure concerning its neighbors. In DVR, transfer function (TF) plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them. TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters, which is time-consuming and inefficient. Attempts at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain experts. In these approaches, a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the SOIs. Although these approaches potentially reduce the workload to generate the TFs, they, however, require manual TF navigation of the knowledge database, as well as the likely fine tuning of the selected TF to suit the input. In this work, we propose a TF design approach, CBR-TF, where we introduce a new content-based retrieval (CBR) method to automatically navigate the knowledge database. Instead of pre-designed TFs, our knowledge database contains volumes with SOI labels. Given an input volume, our CBR-TF approach retrieves relevant volumes (with SOI labels) from the knowledge database; the retrieved labels are then used to generate and optimize TFs of the input. This approach largely reduces manual TF navigation and fine tuning. For our CBR-TF approach, we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile. For the regional spatial semantics, we adopt a convolutional neural network to obtain high-level image feature representations. For the intensity profile, we extend the dynamic time warping technique to address subtle alignment differences between similar profiles (SOIs). Finally, we propose a two-stage CBR scheme to enable the use of these two different feature representations in a complementary manner, thereby improving SOI retrieval performance. We demonstrate the capabilities of our CBR-TF approach with comparison with a conventional approach in visualization, where an intensity profile matching algorithm is used, and also with potential use-cases in medical volume visualization.

This paper presents the design of a Coherence-Free Processor (CFP) that enables a scalable multiprocessor by eliminating cache coherence operations in both hardware and software. The CFP uses a coherence-free cache (CFC) that can improve the cost-effectiveness and performance-effectiveness of the existing multiprocessors for commonly used workloads. The CFC is feasible because not all program data that reside in a multiprocessor cache need to be accessed by other processors, and private caches at level 1 (L1) and level 2 (L2) facilitate this method of sharing. Reentrant programs are specifically designed to protect their data from modification by other tasks. Program data that are modified but not shared with other tasks do not require a coherence protocol. Adding processors reduces the multitasking queue, reducing elapsed time. Simultaneous execution replaces concurrent execution.

Infrastructure-as-a-Service (IaaS) cloud platforms offer resources with diverse buying options. Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant discount. However, users have to carefully weigh the low cost of spot instances against their poor availability. Spot instances will be revoked when the revocation event occurs. Thus, an important problem that an IaaS user faces now is how to use spot instances in a cost-effective and low-risk way. Based on the replication-based fault tolerance mechanism, we propose an online termination algorithm that optimizes the cost of using spot instances while ensuring operational stability. We prove that in most cases, the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal offline algorithm that knows the exact future a priori. Through a large number of experiments, we verify that our algorithm in most cases has a competitive ratio of no more than 2, and in other cases it can also reach the guaranteed competitive ratio.

In recent years, live streaming has become a popular application, which uses TCP as its primary transport protocol. Quick UDP Internet Connections (QUIC) protocol opens up new opportunities for live streaming. However, how to leverage QUIC to transmit live videos has not been studied yet. This paper first investigates the achievable quality of experience (QoE) of streaming live videos over TCP, QUIC, and their multipath extensions Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC). We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission reliability. However, network fluctuations may cause heterogeneous paths, high path loss, and bandwidth degradation, resulting in significant QoE deterioration. Motivated by the above observations, we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP, a multipath adaptive packet scheduling scheme over QUIC. Specifically, a linear upper confidence bound (LinUCB)-based online learning algorithm, along with four novel scheduling mechanisms, i.e., Dispatch, Duplicate, Discard, and Decompensate, is proposed to conquer the above problems. 4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission schemes. Experimental results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.