Graph Neural Networks (GNNs) are widely used in various applications and consist of two main operations: Propagation and Transformation. By permuting and combining these two operations, we can obtain different GNN architectures for different downstream tasks. Despite the effectiveness and popularity of GNNs, their training is time-consuming and memory-intensive, which limits their practical usage. Various methods have been proposed to tackle this issue, such as sampling and distributed training. In this work, we carefully revisit the two basic operations of GNNs and observe two types of redundancy in these operations: 1) propagation data redundancy, and 2) transformation computation redundancy. To address these problems, we propose CompressGNN, a framework that leverages hierarchical compression to accelerate GNNs by reducing data and computational redundancy. The framework comprises a propagation compression module and a transformation compression module. In the propagation compression module, we compress the graph structure to eliminate data redundancy. In the transformation compression module, we achieve computation compression by compressing similar node features at runtime. Extensive experiments demonstrate that our method achieves an average speedup of 2.32× across multiple datasets and models.

Given an edge-weighted graph and a set of potentially overlapping vertex groups, a group Steiner tree (GST) is a minimum weight tree that includes at least one vertex in each group. Finding GSTs serves as a classical approach to keyword search in relational databases. Existing studies use CPUs to find optimal GSTs in a serial way, and remain slow in some cases. No prior work has applied GPUs to meet this challenge. To fill this gap, first, we propose a parallel-friendly GST solution framework, by breaking the traditional bottom-up dynamic programming order. Second, since a direct execution of this framework on GPUs faces a severe workload imbalance problem, we develop a GST-customized load balancing approach. Specifically, we employ kernel fusion and global memory coalescing techniques to efficiently utilize different parallel granularities to match divergent tree construction workloads. Third, since existing pruning methods cannot be directly applied to a parallel scheme, we modify feasible pruning procedures to reduce the computation burden, and rigorously prove the solution correctness. Furthermore, inspired by recent applications, we present a novel dynamic programming algorithm for finding optimal diameter-bounded GSTs on GPUs. Experiments on various real datasets show that the proposed techniques achieve a speedup of 48-2390× over state-of-the-art methods, and can handle some large weighted graphs where existing solutions are too slow to be applied, and thus could greatly improve the user experience in related applications.

Approximate Nearest Neighbor Search (ANNS) is a critical problem in vector databases. Cluster-based index is utilized to narrow the search scope of ANNS, thereby accelerating the search process. Due to its scalability, it is widely employed in real-world vector search systems. However, existing cluster-based indexes often suffer from coarse granularity, requiring query vectors to compute distances with vectors of varying quality, thus increasing query complexity. Existing work aim to represent vectors with minimal cost, such as using product quantization (PQ) or linear transformations, to speed up ANNS. However, these approaches do not address the coarse granularity inherent in cluster-based index. In this paper, we present an efficient vector data query engine to enhance the granularity of cluster-based index by carefully subdividing clusters using diverse distance metrics. Building on this refined index, we introduce techniques that leverage triangle inequalities to develop highly optimized and distinct search strategies for clusters and vectors of varying qualities, thereby reducing the overhead of ANNS. Extensive experiments demonstrate that our method significantly outperforms existing in-memory cluster-based indexing algorithms, achieving up to an impressive 10× speedup and a pruning ratio exceeding 99.4%.

The development of hyperparameter optimization (HPO) algorithms is an important topic within both the machine learning and data management domains. While numerous strategies employing early stopping mechanisms have been proposed to bolster HPO efficiency, there remains a notable deficiency in understanding how the selection of early stopping metrics influences the reliability of early stopping decisions and, by extension, the broader HPO outcomes. This paper undertakes a systematic exploration of the impact of metric selection on the effectiveness of early stopping-based HPO. Specifically, we introduce a set of metrics that incorporate uncertainty and highlight their practical significance in enhancing the reliability of early stopping decisions. Our empirical experiments on HPO and NAS benchmarks show that using training loss as an early stopping metric in the early training stages improves HPO outcomes by up to 24.76% compared to the more widely accepted validation loss. Furthermore, integrating uncertainty into the metric yields an additional improvement of up to 4% under budget constraints, translating into meaningful resource savings and scalability benefits in large-scale HPO scenarios. These findings demonstrate the critical role of metric selection while shedding light on the potential implications of integrating uncertainty as a metric. This research provides empirical insights that serve as a compass for the selection and formulation of metrics, thereby contributing to a more profound comprehension of mechanisms underpinning early stopping-based HPO.

Time series data are generated on an unprecedented scale across various domains. Although traditional compression techniques reduce storage costs, they typically require full decompression before querying, leading to increased latency and higher resource consumption. Homomorphic compression (HC), which enables direct computation on the compressed data without decompression, shows the potential for both reduced storage and improved query performance. However, the unique complexities of time series data pose challenges that current HC methods have yet to adequately address. In this paper, we introduce HC theory in the time series domain, transformatively enabling HC to time series database queries. Building on our theory, we develop CompressIoTDB - a novel homomorphic compression framework integrated into Apache IoTDB. By leveraging our proposed CompColumn structure, our framework supports a wide range of query operators, including filtering, aggregation, and window-based functions, all while maintaining data in its compressed form. Furthermore, we incorporate system-level optimizations such as late decompression and dynamic auxiliary management to further boost query efficiency. Extensive experiments show that CompressIoTDB significantly enhances query processing for time series data, achieving an average throughput improvement of 53.4% and memory usage reduction of 20%.

Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC.

With the development of loT and edge computing, data analytics on edge has become popular, and text analytics directly on compression (TADOC) has been proven to be a promising technology for edge data analytics. At the same time, Field Programmable Gate Array (FPGA) also has broad application prospects in data analytics systems. Unfortunately, there is no work to date showing how to support TADOC using FPGAs. We propose FPGA-based text analytics directly on compression with HLS, namely F - TADOC, which is the first framework using HLS to provide FPGA-based text analytics directly on compressed data. It effectively supports efficient text analytics on FPGA without decompressing input data. F-TADOC addresses three major challenges. First, TADOC involves a large number of dependencies with unbalanced workload of rules, which causes extremely low pipeline efficiency on FPG As. To solve it, we use layer-wise approach to traverse the DAG composed of rules and allocate different pipeline processing strategies for rules of different sizes. Second, the data volume required can be large that beyond the on-chip memory capacity of FPGAs. We develop a memory pool supporting hash structure and on-chip caches on FPGA to deal with this challenge. Third, when traversing the DAG, there are massive indirect addressing with a large number of random accesses. This leads to redundant time overhead caused by the latency in accessing the High Bandwidth Memory (HBM) during the pipeline. We optimize the F - TADOC algorithm by using dataflow to expand the nested loop, thus eliminate indirect addressing. With four widely used datasets, experiments show that F - TADOC achieves 4.63 x and 1.49 x performance speedup over TADOC and G- TADOC.

>Sampling is one of the most widely employed approximations in big data processing. Among various challenges in sampling design, sampling for join is particularly intriguing yet complex. This perplexing problem starts with a classical case where the join of two Bernoulli samples shrinks its output size quadratically and exhibits a strong dependency on the input data, presenting a unique challenge that necessitates adaptive sampling to guarantee both the quantity and quality of the sampled data. The community has made strides in achieving this goal by constructing offline samples and integrating support from indexes or key frequencies. However, when dealing with stream data, due to the need for real-time processing and high-quality analysis, methods developed for processing static data become unavailable. Consequently, a fundamental question arises: Is it possible to achieve adaptive sampling in stream data without relying on offline techniques? To address this problem, we propose FreeSam, which couples hybrid sampling with intra-window join, a key stream join operator. Our focus lies on two widely used metrics: output size, ensuring quantity, and variance, ensuring quality. FreeSam enables adaptability in both the desired quantity and quality of data sampling by offering control on the two-dimensional space spanned by these metrics. Meanwhile, adjustable trade-offs between quality and performance make FreeSam practical for use. Our experiments show that, for every 1% increase in latency limitation, FreeSam can yield a 3.83% increase in the output size while maintaining the level of the estimator's variance. Additionally, we give FreeSam a multi-core implementation and ensure predictability of its latency through both an analytic model and a neural network model. The accuracy of these models is 88.05% and 96.75% respectively.

Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency access. However, developing a distributed database system that is well-suited for diverse application scenarios, particularly for large-scale applications, presents several challenges. These challenges include ensuring data consistency and achieving high levels of performance. This paper presents TDSQL, a distributed database system that prioritizes core design principles of distributed systems, including high availability, strong consistency, and scalability. In particular, TDSQL has achieved high performance through over a decade of practical experience and optimization in various modules, such as the kernel, synchronous replication, and transaction processing, in large-scale application scenarios. By conducting the TPC-C benchmark test, TDSQL demonstrated outstanding performance, achieving a throughput of 814 million tpmC across 1650 database nodes, with a jitter rate of less than 0.2%. This jitter rate is an order of magnitude lower than the standard required, showcasing the system's stability and reliability. During the 8-hour TPC-C standard stress test, TDSQL successfully completed over 860 billion transactions and processed 40 trillion order details, with zero forced rollbacks and zero data inconsistency.

Evolving graphs consisting of slices are large and constantly changing. For example, in Alipay, the graph generates hundreds of millions of new transaction records every day. Analyzing the graph within a temporary window is time-consuming due to the heavy merging of slices. Fortunately, we have discovered that most queries exhibit consistent patterns and possess monotonic properties. As a result, transitional results can be computed within slice generation for reuse. Accordingly, we develop MergeGraph enabling window-based monotonic graph analytics with reusable transitional results for pattern-consistent queries. MergeGraph has three advantages over previous works. First, it is the first system specifically tailored for window-based monotonic graph analytics with pattern-consistent queries. Second, it effectively utilizes transitional results from different slices concurrently. Third, MergeGraph boasts a high degree of expressiveness, supporting a broad spectrum of monotonic graph queries. Experimental results demonstrate that MergeGraph delivers significant performance benefits. In evaluating four typical graph applications, MergeGraph achieves an average speedup of 11.30× compared to state-of-the-art methods.

Databases employ out-of-line storage and compression strategies to manage extensive text data. However, the growth in both the size of individual data items and overall data volume has significantly increased the burden of decompression, adversely affecting query performance. To address this challenge, we develop HocoPG, an innovative system that incorporates homomorphic compression theory within RDBMS, enabling direct computation on compressed data to enhance query efficiency and system usability. HocoPG performs homomorphic evaluations across a suite of basic text operations, enabling the execution of intricate text queries by combining these operations flexibly. In this demonstration, we showcase the deployment and usage of HocoPG through a database terminal. Additionally, we introduce the HocoPG Admin, a tool that provides insights to DBAs and general users for choosing the optimal compression scheme based on their individual needs.

Recent studies have shown the promise of directly processing compressed graphs. However, its benefits have been limited by high peak-memory usage and unbearably long compression time. In this paper, we introduce Laconic, a novel rule-based graph processing solution that overcomes the challenges of restricted memory and impractical compression time faced by existing approaches. Laconic, for the first time, ensures minimal memory overhead during compression and significantly reduces graph sizes, thus reducing peak memory demand during computations. By employing an efficient parallel compression algorithm, Laconic achieves a remarkable reduction in compression time. In our experiments, we compare Laconic with state-of-the-art solutions. The results demonstrate that Laconic outperforms other methods, reducing peak memory consumption by an average of 70% during compression and 66% during computation. Additionally, Laconic reduces rule compression time by an average of 93% compared to traditional rule-based compression, achieving a 2.47× higher compression ratio, and providing a 2.12× performance speedup.

Directly performing operations on compressed data has been proven to be a big success facing Big Data problems in modern data management systems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete Big Data system must support both data query and data manipulation. To solve this problem, we develop CompressDB, which is a new storage engine that can support data processing for databases without decompression. CompressDB has the following advantages. First, CompressDB utilizes context-free grammar to compress data, and supports both data query and data manipulation. Second, for adaptability, we integrate CompressDB to file systems so that a wide range of databases can directly use CompressDB without any change. Third, we enable operation pushdown to storage so that we can perform data query and manipulation in storage systems without bringing large data to memory for high efficiency. We validate the efficacy of CompressDB supporting various kinds of database systems, including SQLite, MySQL, LevelDB, MongoDB, ClickHouse, and Neo4j. We evaluate our method using seven real-world datasets with various lengths, structures, and content in both single node and cluster environments. Experiments show that CompressDB achieves 40% throughput improvement and 44% latency reduction, along with 1.75 compression ratio on average.

Stream processing has been in widespread use, and one of the most common application scenarios is SQL query on streams. By 2021, the global deployment of IoT endpoints reached 12.3 billion, indicating a surge in data generation. However, the escalating demands for high throughput and low latency in stream processing systems have posed significant challenges due to the increasing data volume and evolving user requirements. We present a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, to significantly enhance the performance of existing stream processing solutions. CompressStreamDB utilizes nine diverse compression methods tailored for different stream data types and integrates a cost model to automatically select the most efficient compression schemes. CompressStreamDB provides high throughput with low latency in stream SQL processing by identifying and eliminating redundant data among streams. Our evaluation demonstrates that CompressStreamDB improves average performance by 3.84× and reduces average delay by 68.0% compared to the state-of-the-art stream processing solution for uncompressed streams, along with 68.7% space savings. Besides, our edge trials show an average throughput/price ratio of 9.95× and a throughput/power ratio of 7.32× compared to the cloud design.

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://github.com/ChenyangZhang-cs/EdgeNN.

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.

Modern graphs exert colossal time and space pressure on graph analytics applications. In 2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many compression algorithms have been developed to support direct processing on compressed graphs to address this challenge. However, previous graph compression algorithms do not focus on leveraging redundancy in repeated neighbor sequences, so they do not save the amount of computation for graph analytics. We develop CompressGraph, an efficient rule-based graph analytics engine that leverages data redundancy in graphs to achieve both performance boost and space reduction for common graph applications. CompressGraph has three advantages over previous works. First, the rule-based abstraction of CompressGraph supports the reuse of intermediate results during graph traversal, thus saving time. Second, CompressGraph has intense expressiveness to support a wide range of graph applications. Third, CompressGraph scales well under high parallelism because the context-free rules have few dependencies. Experiments show that CompressGraph provides significant performance and space benefits on both CPUs and GPUs. On evaluating six typical graph applications, CompressGraph can achieve 1.97× speedup on the CPU, while 3.95× speedup on the GPU, compared to the state-of-the-art CPU and GPU methods, respectively. Moreover, CompressGraph can save an average of 71.27% memory savings on CPU and 70.36 on GPU.

Lossless data compression is an effective way to handle the huge transmission and storage overhead of massive text data. Its utility is even more significant today when data volumes are skyrocketing. The concept of operating on compressed data infuses new blood into efficient text management by enabling mainly access-oriented text processing tasks to be done directly on compressed data without decompression. Facing limitations of the existing compressed text processing schemes such as limited types of operations supported, low efficiency, and high space occupation, we address these problems by proposing a homomorphic compression theory. It enables the generalization and characterization of algorithms with compression processing capabilities. On this basis, we develop HOCO, an efficient text data management engine that supports a variety of processing tasks on compressed text. We select three representative compression schemes and implement them combined with homomorphism in HOCO. HOCO supports the extension of homomorphic compression schemes through a modular and object-oriented design and has convenient interfaces for text processing tasks. We evaluate HOCO on six real-world datasets. The three schemes implemented in HOCO show trade-offs in terms of compression ratio, supported operation types, and efficiency. Experiments also show that HOCO can achieve higher throughput in random access and modification operations (averagely 9.18× than the state-of-the-art) and lower latency in text analytic tasks (averagely 7.16× than processing on uncompressed text) without compromising compression efficacy.

Compiler optimization plays an increasingly important role to boost the performance of machine learning models for data processing and management. With increasingly complex data, the dynamic tensor shape phenomenon emerges for ML models. However, existing ML compilers either can only handle static shape models or expose a series of performance problems for both operator fusion optimization and code generation in dynamic shape scenes. This paper tackles the main challenges of dynamic shape optimization: the fusion optimization without shape value, and code generation supporting arbitrary shapes. To tackle the fundamental challenge of the absence of shape values, it systematically abstracts and excavates the shape information and designs a cross-level symbolic shape representation. With the insight that what fusion optimization relies upon is tensor shape relationships between adjacent operators rather than exact shape values, it proposes the dynamic shape fusion approach based on shape information propagation. To generate code that adapts to arbitrary shapes efficiently, it proposes a compile-time and runtime combined code generation approach. Finally, it presents a complete optimization pipeline for dynamic shape models and implements an industrial-grade ML compiler, named BladeDISC. The extensive evaluation demonstrates that BladeDISC outperforms PyTorch, TorchScript, TVM, ONNX Runtime, XLA, Torch Inductor (dynamic shape), and TensorRT by up to 6.95×, 6.25×, 4.08×, 2.04×, 2.06×, 7.92×, and 4.16× (3.54×, 3.12×, 1.95×, 1.47×, 1.24×, 2.93×, and 1.46× on average) in terms of end-to-end inference speedup on the A10 and T4 GPU, respectively. BladeDISC's source code is publicly available at https://github.com/alibaba/BladeDISC.

Deep learning on edge devices is becoming increasingly important, especially with the explosion of IoT devices. For example, the total number of devices connected to IoT reaches 29 billion in 2022. Convolutional neural networks (CNNs), as common deep learning representatives, are among the most popular neural networks in knowledge and data engineering. However, CNN employs a high degree of computing. In comparison to the training phase, the inference process is more frequently done on low-power computing equipments, such as edge devices. The limited computing resource and high computation pressure limit the effective use of CNN algorithms at the edge. Fortunately, a minimal filtering algorithm called Winograd can reduce convolution calculations by minimizing multiplication operations. We find that Winograd convolution can be accelerated further bydeep reusetechnique, which reuses the similar data and computation processes. In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. DREW handles three difficulties. First, it can detect the similarities from the complex minimal filtering patterns by clustering. Second, it reduces the online clustering cost in a reasonable range. Third, it provides an adjustable method in clustering granularity balancing the performance and accuracy. We perform evaluation on Raspberry PI and NVIDIA Jetson AGX Xavier edge devices, and experiments show that on five popular networks, 1) DREW further accelerates the Winograd convolution by an average of 8.27× speedup. Even for the highly parallel Winograd implementation, DREW still can provide 2.21× speedup. 2) When DREW is applied to end-to-end Winograd CNN inferences, DREW achieves 5.94× the average performance speedup with no ($< $0.4%) accuracy loss. 3) Energy consumption is an important factor for inference in practice. DREW reduces the number of convolution operations to 10% of the original operations, thus achieving up to 60% energy-efficiency benefits than the original Winograd inference.

In modern data management systems, directly performing operations on compressed data has been proven to be a big success facing big data problems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete big data system must support both data query and data manipulation.

Deep learning has been used in various domains, including Web services. Convolutional neural networks (CNNs), which are deep learning representatives, are among the most popular neural networks in Web systems. However, CNN employs a high degree of computing. In comparison to the training phase, the inference process is more frequently done on low-power computing equipments. The limited computing resource and high computation pressure limit the effective use of CNN algorithms in industry. Fortunately, a minimal filtering algorithm called Winograd can reduce convolution calculations by minimizing multiplication operations. We find that Winograd convolution can be sped up further by deep reuse technique, which reuses the similar data and computation processes. In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. DREW handles three difficulties. First, it can detect the similarities from the complex minimal filtering patterns by clustering. Second, it reduces the online clustering cost in a reasonable range. Third, it provides an adjustable method in clustering granularity balancing the performance and accuracy. Experiments show that 1) DREW further accelerates the Winograd convolution by an average of 2.06 × speedup; 2) when DREW is applied to end-to-end Winograd CNN inference, it achieves 1.71 × the average performance speedup with no (<0.4%) accuracy loss; 3) DREW reduces the number of convolution operations to 11% of the original operations on average.

Abstract With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.

Butterfly counting is an important and costly operation for large bipartite graphs. GPUs are popular parallel heterogeneous devices and can bring significant performance improvement for data science applications. Unfortunately, no work enables efficient butterfly counting on GPU currently. To fill this gap, we propose a GPU-based butterfly counting, called G-BFC. G-BFC addresses three main technical challenges. First, butterfly counting involves massive serial operations, which leads to severe synchronization overheads and performance degradation. We unlock the serial region and utilize the shared memory on GPU to efficiently handle it. Second, butterfly counting on GPU faces the workload imbalance problem. We develop a novel adaptive strategy to balance the workload among threads for efficiency. Third, butterfly counting in parallel suffers from the traversal of the huge amount of two-hop paths, also called wedges, in bipartite graphs. We develop a novel preprocessing strategy, which can effectively reduce the number of wedges to be traversed. Experiments show that G-BFC brings significant performance benefits. On eleven real datasets, G-BFC achieves 19.8X performance speedup over the state-of-the-art solution.

There are plenty of parking spaces in big cities, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, weekdays, and events. PewLSTM includes a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events. PewLSTM is extremely useful for drivers and parking lot owners to improve customer experience. For example, the probability of parking space that will be available soon can be provided even if the parking lot is full. Based on 910,477 real parking records in 904 days from 13 parking lots, PewLSTM yields 93.84% parking prediction accuracy, which is about 30% higher than the state-of-the-art parking behavior prediction method. Additionally, we have analyzed parking behaviors in events like holidays and COVID-19. PewLSTM can handle parking behavior prediction in events and reaches 90.68 percent accuracy.

Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data. G-TADOC solves three major challenges. First, TADOC involves a large amount of dependencies, which makes it difficult to exploit massive parallelism on a GPU. We develop a novel fine-grained thread-level workload scheduling strategy for GPU threads, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads writing to the same result buffer leads to inconsistency while directly using locks and atomic operations lead to large synchronization overheads. We develop a memory pool with thread-safe data structures on GPUs to handle such difficulties. Third, maintaining the sequence information among words is essential for lossless compression. We design a sequence-support strategy, which maintains high GPU parallelism while ensuring sequence information. Our experimental evaluations show that G-TADOC provides 31.1× average speedup compared to state-of-the-art TADOC.

The integrated architecture that features both CPU and GPU on the same die is an emerging and promising architecture for fine-grained CPU-GPU collaboration. However, the integration also brings forward several programming and system optimization challenges, especially for irregular applications such as graph processing. The complex interplay between heterogeneity and irregularity leads to very low processor utilization of running irregular applications on integrated architectures. Furthermore, fine-grained co-processing on the CPU and GPU is still an open problem. Particularly, in this paper, we show that the previous workload partitioning for CPU-GPU co-processing is far from ideal in terms of resource utilization and performance. To solve this problem, we propose a system software called FinePar, which considers architectural differences of the CPU and GPU and leverages fine-grained collaboration enabled by integrated architectures. Through irregularity-aware performance modeling and online auto-tuning, FinePar partitions irregular workloads and achieves both device-level and thread-level load balance. We evaluate FinePar with eight irregular applications in graphs and sparse matrices on two integrated architectures and compare it with state-of-the-art partitioning approaches. Results show that FinePar demonstrates better resource utilization and achieves an average of 1.6X speedup over the optimal coarse-grained partitioning method.

Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.

Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice support for concurrent state access that eases the burden of developing stateful stream applications. Recently, some have proposed managing concurrent state access during stream processing by modeling state accesses as transactions. However, these are realized with locks involving serious contention overhead. The coarse-grained processing paradigm adopted in these proposals magnify contention issues and does not exploit modern multicore architectures to their full potential. This paper introduces TStream, a novel DSPS supporting efficient concurrent state access on multicore processors. Transactional semantics is employed like previous work, but scalability is greatly improved due to two novel designs: 1) dual-mode scheduling, which exposes more parallelism opportunities, 2) dynamic restructuring execution, which aggressively exploits the parallelism opportunities from dual-mode scheduling without centralized lock contentions. To validate our proposal, we evaluate TStream with a benchmark of four applications on a modern multicore machine. Experimental results show that 1) TStream achieves up to 4.8 times higher throughput with similar processing latency compared to the state-of-the-art and 2) unlike prior solutions, TStream is highly tolerant of varying application workloads such as key skewness and multi-partition state accesses.

Today's rapidly growing document volumes pose pressing challenges to modern document analytics, in both space usage and processing time. In this work, we propose the concept of compression-based direct processing to alleviate issues in both dimensions. The main idea is to enable direct document analytics on compressed data. We present how the concept can be materialized on Sequitur, a compression algorithm that produces hierarchical grammar-like representations. We discuss the major challenges in applying the idea to various document analytics tasks, and reveal a set of guidelines and also assistant software modules for developers to effectively apply compression-based direct processing . Experiments show that our proposed techniques save 90.8% storage space and 77.5% memory usage, while speeding up data processing significantly, i.e., by 1.6X on sequential systems, and 2.2X on distributed clusters, on average.

Abstract The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user‐intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low‐level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low‐level and high‐level features, which is extremely useful for assigning the low‐level features' weights in similarity assessment.