1,629 research outputs found
Advanced analytics through FPGA based query processing and deep reinforcement learning
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 dÃa, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analÃticos 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 estadÃsticas y fÃsica. Esta tesis propone dos métodos para abordar los desafÃos 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-crÃtico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en lÃnea 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 especÃfica muy mÃnima del problema, TauRieL puede superar a una heurÃstica codiciosa de Knapsack Problem.Postprint (published version
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within
low latency Function as a Service (FaaS) prediction pipelines: including image
recognition, object detection, natural language processing, speech synthesis,
and personalized recommendation pipelines. Cloud computing, as the de-facto
backbone of modern computing infrastructure for both enterprise and consumer
applications, has to be able to handle user-defined pipelines of diverse DNN
inference workloads while maintaining isolation and latency guarantees, and
minimizing resource waste. The current solution for guaranteeing isolation
within FaaS is suboptimal -- suffering from "cold start" latency. A major cause
of such inefficiency is the need to move large amount of model data within and
across servers. We propose TrIMS as a novel solution to address these issues.
Our proposed solution consists of a persistent model store across the GPU, CPU,
local storage, and cloud storage hierarchy, an efficient resource management
layer that provides isolation, and a succinct set of application APIs and
container technologies for easy and transparent integration with FaaS, Deep
Learning (DL) frameworks, and user code. We demonstrate our solution by
interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x
speedup in latency for image classification models and up to 210x speedup for
large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201
The Value of Business Incubators and Accelerators from the Entrepreneurs Perspective
For several decades, the business creation rate has declined in the United States. Scholars and practitioners debate the reasons for the descent, but most agree that it is undesirable. Entrepreneurial sponsors (e.g., government, investors) seek ways to foster strong business startup ecosystems to attract and sustain new companies. Entrepreneurs gravitate toward these concentrated ecosystems to efficiently access resources to improve their odds of startup and survival. Business incubators and accelerators (BIAs) have become prominent in entrepreneurial ecosystems. By consolidating startup-related services, the BIAs offer programs that help entrepreneurs access scarce resources and build capabilities to enable new business growth.
Although BIAs are increasingly popular, there is debate as to their efficacy. The focus of this empirical research is to explore whether entrepreneurs, as the first-hand BIA users, value the BIA programs as a useful tool for progressing their businesses. And if they do find value, then understand what is driving this worth. The study leverages resource-based theory as well as task effort cost theory using quantitative and qualitative methods for analysis.
The research findings indicate that entrepreneurs find BIA programs very valuable for improving their business outcomes. The entrepreneurs express that the program experience is worthwhile regardless of whether their businesses ultimately survive. Moreover, the entrepreneurs strongly recommend the usage of BIAs to fellow entrepreneurs. Many factors contributed to this value, but knowledge resources (e.g., mentors, network) tops the list. Incubator users indicate a reduction in expenses to be most impactful on value, while accelerator users find access to capital funding to be most impactful.
The research contributes to the academic body of knowledge concerning entrepreneurial processes and the application of resource-based theory. It contributes to the literary conversation by providing a supportive position regarding BIA efficacy and bringing forth a variance model to understand contributing factors as well as highlighting differences between incubators and accelerators. Moreover, the study educates entrepreneurs about the potential experience and outcomes from BIA usage. It informs BIA administrators and sponsors about potential ways to provide greater value to their users. Overall, the study’s contributions aim to foster business dynamism
Increasing Consonance and Resonance in Agile Teaching Methodologies
In a cooperative environment technicalexcellence and high quality students’ artifacts is whatteachers strive to achieve while educating computerscience students and facing the challenges of this newcentury. When agile techniques and accelerators andinjected in the process in a cooperative environment theconsonance and resonance in groups increases. Thisspeeds up the learning process and the quality of thematerial produced by the students improves. Twoobservational studies at Kent State University at Starkand Ohio University are described in this paper. Thestudies observe the usefulness of using agile teachingtechniques and analyze the quality of deliverablesproduced. A post questionnaire gathered students’feedback. The observation shows that cooperativelearning produces better results than individuallearning however consonance and resonance must bereached before the speed is achieved
A Conceptual Architecture for a Quantum-HPC Middleware
Quantum computing promises potential for science and industry by solving
certain computationally complex problems faster than classical computers.
Quantum computing systems evolved from monolithic systems towards modular
architectures comprising multiple quantum processing units (QPUs) coupled to
classical computing nodes (HPC). With the increasing scale, middleware systems
that facilitate the efficient coupling of quantum-classical computing are
becoming critical. Through an in-depth analysis of quantum applications,
integration patterns and systems, we identified a gap in understanding
Quantum-HPC middleware systems. We present a conceptual middleware to
facilitate reasoning about quantum-classical integration and serve as the basis
for a future middleware system. An essential contribution of this paper lies in
leveraging well-established high-performance computing abstractions for
managing workloads, tasks, and resources to integrate quantum computing into
HPC systems seamlessly.Comment: 12 pages, 3 figure
- …