1,720,975 research outputs found
Transmogrifying performance analysis: data analytics on GPU application codes
Get PDFProyecto de Graduación (Maestría en Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2022.High Performance Computing (HPC) is now reaching exactable capabilities. Modern supercomputers are catalyzing scientific research and have become central tools in topics like big data analysis and machine/deep learning. However, the road to extreme-scale computing is not without its challenges. Energy efficiency as well as power and cooling are some of the hardware concerns in this quest. On the other hand aspects like application scaling, the cost of scientific code development including new programming models and portability issues are some examples of challenges in the software spectrum. This project is focused on one particular challenge that is also crucial in achieving next generation compute capabilities: application performance analysis and optimization. Many of the leading HPC systems are powered by heterogeneous compute nodes, which integrate Graphic Processing Units (GPUs) as hardware accelerators. Adapting modern applications to leverage such systems effectively is of great importance. The performance evaluation process is key in enabling algorithms to scale on these modern massively parallel clusters. Although modern tools allow for the analysis of parallel applications, they usually limit the user to proprietary data formats and data visualization interfaces, effectively restricting the kinds of analysis that can be done. In this project, we implemented a data transformation and manipulation workflow that enables the creation of context-aware hierarchical performance data for GPU applications profiled with NVIDIA’s NSight Tools. This information can then be loaded into a tool like Hatchet, a Python-based library, to enable programmatic performance analysis. Through a series of case studies, we showcase how this newly implemented workflow in hand with a data analytics approach can help users identify bottlenecks and implement custom and reproducible analysis of GPU-accelerated performance data
Parallelization of Plasma Physics Simulations on Massively Parallel Architectures
Get PDFProyecto de Graduación (Maestría en Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2017.Clean energy sources have increased its importance in the last few years. Because of that,
the seek for more sustainable sources has been increased too. This effect made to turn the
eyes of the scientific community into plasma physics, specially to the controlled fusion. This
plasma physics developments have to rely on computer simulation processes before start the
implementation of the respective fusion devices. The simulation process has to be done in order
to detect any kind of issues on the theoretical model of the device, saving time and money. To
achieve this, those computer simulation processes have to finish in a timely manner. If not, the
simulation defeats its purpose. However, in recent years, computer systems have passed from
an increment speed approach to a increment parallelism approach. That change represents a
short stop for these applications. Because of these reasons, on this dissertation we took one
plasma physics application for simulation and sped it up by implementing vectorization, shared,
and distributed memory programming in a hybrid model. We ran several experiments regarding
the performance improvement and the scaling of the new implementation of the application
on sumpercomputers using a recent architecture, Intel Xeon Phi - Knights Landing - manycore
processor. The claim of this thesis is that a plasma physics application can be parallelized
achieving around 0.8 of performance under the right configuration and the right architecture
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Diseño de una infraestructura de computación de alto rendimiento para objetos paralelos en un lenguaje de alto nivel
Get PDFProyecto de investigación. Código del proyecto: 540213700005La computación paralela ha alcanzado una posición predominante en la última década gracias
a la abundancia de arquitecturas computacionales de múltiples núcleos. Explotar el poder
computacional disponible en los sistemas modernos ofrece una enorme posibilidad de avanzar
el estado del arte en la ciencia y la ingeniería. El modelo de programación de objetos paralelos
ofrece muchas ventajas con respecto a otros modelos en computación paralela. Sin embargo,
este modelo no ha sido explorado en el contexto de lenguajes de alto nivel. Este proyecto se
enfocó en explorar las posibilidades de diseño de un sistema de computación de alto
rendimiento para objetos paralelos en un lenguaje de alto nivel. Para lograr ese objetivo se hizo
una recolección exhaustiva de herramientas en el lenguaje Python para computación de alto
rendimiento. Esa colección demostró la oportunidad que existe al combinar los dos dominios:
objetos paralelos y un lenguaje de alto nivel. Además, el proyecto creó un panorama de las
posibilidades de diseño de tal combinación
Scalable message-logging techniques for effective fault tolerance in HPC applications
Get PDFAn important set of challenges emerge as the High Performance Computing (HPC)
community aims to reach extreme scale. Resilience and energy consumption are two
of those challenges. Extreme-scale machines are expected to have a high failure
frequency. This is an inevitable consequence of the mismatch between two trends.
The number of components assembled in supercomputers grows exponentially.
However, the improvement on the reliability of each individual component is much
slower. At the same time, the vast number of components in a single machine will
consume a non-trivial amount of energy. To keep a supercomputer within
operational margins, HPC systems have to be both reliable and energy-aware. For
an application to be able to run and make progress in spite of constant
interruptions, it has to incorporate some fashion of fault tolerance.
Rollback-recovery techniques provide a framework to overcome crashes in the
system by periodically saving the state of the application and rolling back to
checkpoints in case of failures. Two well-known rollback-recovery techniques are
checkpoint/restart and message-logging. The former is easier to implement and
has become the de facto standard to make applications fault tolerant. It
has, however, a high performance and energy cost during recovery.
Message-logging, on the other hand, makes it possible to recover faster from a
failure and to consume less energy. The downside of message-logging is the
overhead it exhibits in the failure-free scenario. Memory and performance
overheads may offset its advantages. This thesis focuses on techniques to
alleviate the downsides of message-logging. It presents a mechanism based on
high-level programming language constructs to decrease the performance overhead
of message-logging. It also introduces two strategies to reduce the memory
overhead created by the message log. Additionally, it addresses important
architectural constraints of modern supercomputers. Based on large-scale
experimental results and projections from an analytical model, we conclude
message-logging is a promising strategy to provide fault tolerance at a low
energy cost for extreme-scale machines.Item withdrawn by Mark Zulauf ([email protected]) on 2013-07-07T15:55:21Z
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University of Illinois Theses & Dissertations (ID: 1)
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A fault-tolerance protocol for parallel applications with communication imbalance
Get PDFArticuloThe predicted failure rates of future supercomputers
loom the groundbreaking research large machines are
expected to foster. Therefore, resilient extreme-scale applications
are an absolute necessity to effectively use the new generation
of supercomputers. Rollback-recovery techniques have been
traditionally used in HPC to provide resilience. Among those
techniques, message logging provides the appealing features of
saving energy, accelerating recovery, and having low performance
penalty. Its increased memory consumption is, however, an
important downside. This paper introduces memory-constrained
message logging (MCML), a general framework for decreasing the
memory footprint of message-logging protocols. In particular, we
demonstrate the effectiveness of MCML in maintaining message
logging feasible for applications with substantial communication
imbalance. This type of applications appear in many scientific
fields. We present experimental results with several parallel codes
running on up to 4,096 cores. Using those results and an analytical
model, we predict MCML can reduce execution time up to 25%
and energy consumption up to 15%, at extreme scale
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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