Q100

Wu, Lisa; Lottarini, Andrea; Paine, Timothy K.; Kim, Martha A.; Ross, Kenneth A.

doi:10.1145/2654822.2541961

Cited by 19 publications

(4 citation statements)

References 22 publications

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“…Many recent works [26], [30], [32], [34]- [36], [40], [47], [49], [57], [59], [63]- [66] have tried to accelerate relational database queries with ASIC, FPGA, and GPU. Q100 [64] and Andrea et al [49] are typical database accelerator composed of ASIC tiles for each database primitive. Many prior works [30], [32], [35], [59], [63] have accelerated several relational operations using various hardware architectures on FPGA.…”

Section: Related Workmentioning

confidence: 99%

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Kim,

Han,

Park

et al. 2024

IEEE Access

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The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, the integration of ML in DBMSs introduces new challenges in traditional CPU-based systems because of its higher computational demands and bigger data bandwidth requirements. To address this, hardware acceleration has become even more important in database systems, and the computational storage device (CSD) placing an accelerator near storage is considered as an effective solution due to its high processing power with no extra data movement cost. In this paper, we propose Trinity, an end-to-end database system that enables in-database, in-storage platform that accelerates advanced analytics queries invoking trained ML models along with complex data operations. By designing a full stack from DBMS's internal software components to hardware accelerator, Trinity enables in-database ML pipelines on the CSD. On the software side, we extend the internals of conventional DBMSs to utilize the accelerator in the SmartSSD. Our extended analyzer evaluates the compatibility of the current query with our hardware accelerator and compresses compatible queries into a 24-byte numeric format for efficient hardware processing. Furthermore, the predictor is extended to integrate our performance cost models to always offload queries into the optimal hardware backend. The proposed SmartSSD cost model mathematically models our hardware, including host operations, data transfers, FPGA kernel execution time, and the CPU cost model uses polynomial regression ML models to predict complex CPU latency. On the hardware side, we introduce the in-database processing accelerator (i-DPA), a custom FPGA-based accelerator. i-DPA includes database page decoder to fully exploit the bandwidth benefit of near-storage processing. It also employs dynamic tuple binding to enhance the overall parallelism and hardware utilization. i-DPA's architecture having heterogeneous computing units with a reconfigurable on-chip interconnect also allows seamless data streaming, enabling task-level pipeline across different computing units. Finally, our evaluation shows that Trinity improves the end-to-end performance of analytics queries by 15.21× on average and up to 57.18× compared to the conventional CPU-based DBMS platform. We also show that the Trinity's performance can linearly scale up with multiple SmartSSDs, achieving nearly up to 200× speedup over the baseline with four SmartSSDs.

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Section: Related Workmentioning

confidence: 99%

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Kim,

Han,

Park

et al. 2024

IEEE Access

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“…Accelerating Data Analytics On-chip accelerator Q100 [47] exploits pipeline in query processing with heterogeneous processing cores to minimize the memory access. Mondrian and Polynesia [7], [12] integrate circuits in logic die of 3D stacked memory, which is much further from the DRAM cells and loses the internal bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Genome Sequence of Brevibacillus brevis HK544, an Antimicrobial Bacterium Isolated from Soil in Daejeon, South Korea

Kim

Han

et al. 2021

Microbiol Resour Announc

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The Brevibacillus brevis HK544 strain, which was isolated from soil, exhibited antimicrobial activity against plant pathogens such as Botrytis cinerea , Phytophthora infestans , and Erwinia amylovora . Here, we report the draft genome sequence of the B. brevis HK544 strain, which consists of one circular chromosome of 6,486,246 bp with a GC content of 47.3%.

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Section: Query Processing Hardware Designmentioning

confidence: 99%

“…There have been two main efforts to implement query processing on hardware, namely ASIC-based and FPGA-based solutions [141,62]. The work in [141] presents an ASIC-based method with heterogeneous compute tiles which manipulate rows and handle query processing operations in a coarse grain way. In order to manipulate streams of data according to the given query, the authors present spatial and temporal planning which enables/disables compute units along a predefined data path.…”

Section: Query Processing Hardware Designmentioning

confidence: 99%

Advanced analytics through FPGA based query processing and deep reinforcement learning

Malazgirt

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Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics. Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.

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Q100

Cited by 19 publications

References 22 publications

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Genome Sequence of Brevibacillus brevis HK544, an Antimicrobial Bacterium Isolated from Soil in Daejeon, South Korea

Advanced analytics through FPGA based query processing and deep reinforcement learning

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