3,034 research outputs found
Dataflow Programming and Acceleration of Computationally-Intensive Algorithms
The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping
DRLCap: Runtime GPU Frequency Capping with Deep Reinforcement Learning
Power and energy consumption is the limiting factor of modern computing systems. As the GPU becomes a mainstream computing device, power management for GPUs becomes increasingly important. Current works focus on GPU kernel-level power management, with challenges in portability due to architecture-specific considerations. We present DRLCap , a general runtime power management framework intended to support power management across various GPU architectures. It periodically monitors system-level information to dynamically detect program phase changes and model the workload and GPU system behavior. This elimination from kernel-specific constraints enhances adaptability and responsiveness. The framework leverages dynamic GPU frequency capping, which is the most widely used power knob, to control the power consumption. DRLCap employs deep reinforcement learning (DRL) to adapt to the changing of program phases by automatically adjusting its power policy through online learning, aiming to reduce the GPU power consumption without significantly compromising the application performance. We evaluate DRLCap on three NVIDIA and one AMD GPU architectures. Experimental results show that DRLCap improves prior GPU power optimization strategies by a large margin. On average, it reduces the GPU energy consumption by 22% with less than 3% performance slowdown on NVIDIA GPUs. This translates to a 20% improvement in the energy efficiency measured by the energy-delay product (EDP) over the NVIDIA default GPU power management strategy. For the AMD GPU architecture, DRLCap saves energy consumption by 10%, on average, with a 4% percentage loss, and improves energy efficiency by 8%
Software Defined Radio, a perspective from education
The evolution of communication systems has brought about a paradigm shift, particularly in radiocommunications, where software has increasingly taken precedence over hardware. This transition has not only reduced implementation costs but has also significantly enhanced the flexibility of equipment architecture. A prime example of this trend is the emergence and consolidation of software-defined radio (SDR) technology in recent decades. This study provides a comprehensive contextualization of SDR technology, offering insights into its current state in terms of development tools and market equipment. Additionally, two learning scenarios are presented that employ different teaching methodologies. In one of these scenarios, communication theory is exclusively approached from a theoretical perspective. In the second scenario, knowledge acquisition is encouraged through the implementation of low-cost laboratories that incorporate SDR technology. The study indicates that implementing SDR technology boosts student motivation and learning, with 73.13% believing it enhances engineering education and 96% showing increased motivation. Those using SDR in practical laboratories perform better on knowledge tests, but statistical analysis shows that the difference is not statistically significant
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs
Producción CientíficaIterative stencil computations are widely used in numerical simulations. They
present a high degree of parallelism, high locality and mostly-coalesced memory
access patterns. Therefore, GPUs are good candidates to speed up their computa-
tion. However, the development of stencil programs that can work with huge grids in
distributed systems with multiple GPUs is not straightforward, since it requires solv-
ing problems related to the partition of the grid across nodes and devices, and the
synchronization and data movement across remote GPUs. In this work, we present
EPSILOD, a high-productivity parallel programming skeleton for iterative stencil
computations on distributed multi-GPUs, of the same or different vendors that sup-
ports any type of n-dimensional geometric stencils of any order. It uses an abstract
specification of the stencil pattern (neighbors and weights) to internally derive the
data partition, synchronizations and communications. Computation is split to better
overlap with communications. This paper describes the underlying architecture of
EPSILOD, its main components, and presents an experimental evaluation to show
the benefits of our approach, including a comparison with another state-of-the-art
solution. The experimental results show that EPSILOD is faster and shows good
strong and weak scalability for platforms with both homogeneous and heterogene-
ous types of GPUJunta de Castilla y León, Ministerio de Economía, Industria y Competitividad, y Fondo Europeo de Desarrollo Regional (FEDER): Proyecto PCAS (TIN2017-88614-R) y Proyecto PROPHET-2 (VA226P20).Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y “European Union NextGenerationEU/PRTR” : (MCIN/ AEI/10.13039/501100011033) - grant TED2021-130367B-I00CTE-POWER and Minotauro and the technical support provided by Barcelona Supercomputing Center (RES-IM-2021-2-0005, RES-IM-2021-3-0024, RES- IM-2022-1-0014).Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL
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