scholarly journals Flexible Language Constructs for Large Parallel Programs

1994 ◽  
Vol 3 (3) ◽  
pp. 169-186 ◽  
Author(s):  
Matt Rosing ◽  
Robert Schnabel
Keyword(s):  
Large Data ◽  
Sequential Programs ◽  
Global Synchronization ◽  
Implicit Communication ◽  
Data Parallel ◽  
Communication Models ◽  
Multiple Data ◽  
Language Constructs ◽  
Efficient Expression ◽  
Selection Of

The goal of the research described in this article is to develop flexible language constructs for writing large data parallel numerical programs for distributed memory (multiple instruction multiple data [MIMD]) multiprocessors. Previously, several models have been developed to support synchronization and communication. Models for global synchronization include single instruction multiple data (SIMD), single program multiple data (SPMD), and sequential programs annotated with data distribution statements. The two primary models for communication include implicit communication based on shared memory and explicit communication based on messages. None of these models by themselves seem sufficient to permit the natural and efficient expression of the variety of algorithms that occur in large scientific computations. In this article, we give an overview of a new language that combines many of these programming models in a clean manner. This is done in a modular fashion such that different models can be combined to support large programs. Within a module, the selection of a model depends on the algorithm and its efficiency requirements. In this article, we give an overview of the language and discuss some of the critical implementation details.

scholarly journals Static Mapping with Dynamic Switching of Multiple Data-Parallel Applications on Embedded Many-Core SoCs

2014 ◽  
Vol E97.D (11) ◽  
pp. 2827-2834 ◽  
Author(s):  
Ittetsu TANIGUCHI ◽  
Junya KAIDA ◽  
Takuji HIEDA ◽  
Yuko HARA-AZUMI ◽  
Hiroyuki TOMIYAMA
Keyword(s):  
Parallel Applications ◽  
Data Parallel ◽  
Dynamic Switching ◽  
Multiple Data ◽  
Many Core

scholarly journals Interpreting the Data: Parallel Analysis with Sawzall

2005 ◽  
Vol 13 (4) ◽  
pp. 277-298 ◽  
Author(s):  
Rob Pike ◽  
Sean Dorward ◽  
Robert Griesemer ◽  
Sean Quinlan
Keyword(s):  
Programming Language ◽  
Regular Structure ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Parallel ◽  
Web Document ◽  
Distributed Computations ◽  
Procedural Programming ◽  
Two Phases

Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories. These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database. On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on. We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new procedural programming language, emits data to an aggregation phase. Both phases are distributed over hundreds or even thousands of computers. The results are then collated and saved to a file. The design – including the separation into two phases, the form of the programming language, and the properties of the aggregators – exploits the parallelism inherent in having data and computation distributed across many machines.

scholarly journals Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alberto A. Toffano ◽  
Giacomo Chiarot ◽  
Stefano Zamuner ◽  
Margherita Marchi ◽  
Erika Salvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Theory ◽  
Large Data ◽  
Data Availability ◽  
Computational Pipeline ◽  
Excitable Cells ◽  
Genes Encoding ◽  
Complementary Techniques ◽  
Applications Of Machine Learning ◽  
Selection Of

Abstract Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.

scholarly journals Evaluation of the Efficiency of Basic Sanitation Integrated Management in Brazilian Municipalities

2020 ◽  
Vol 17 (24) ◽  
pp. 9244
Author(s):  
Alvaro Cavalcanti ◽  
Arthur Teixeira ◽  
Karen Pontes
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Integrated Management ◽  
Financial Resources ◽  
Data Envelopment ◽  
Multiple Data ◽  
Basic Sanitation ◽  
Potential Efficiency ◽  
Support Decision Making ◽  
Selection Of

This study aims to evaluate the level of technical efficiency of companies that perform the integrated management of basic sanitation in Brazilian municipalities. A Multiple Data Envelopment Analysis (M-DEA) model was applied to estimate the performance of water supply and sewage services in 1628 municipalities covering more than 56% of the Brazilian population, identifying the factors that most influence the efficiency of the sector in the years 2008 and 2016. The M-DEA methodology is an extension of Data Envelopment Analysis (DEA) with multiple DEA executions considering all combinations of inputs and outputs to calculate efficiency scores. The methodology reduces possible biases in the selection of resources and products of the model, ability to support decision-making in favor of improvements in the sector′s efficiency based on national regulatory framework. The analyses show that the companies analyzed can increase their operating results and attendance coverage by more than 60%, given the current levels of infrastructure, human and financial resources in the sector. Based on the simulation of potential efficiency gains in Brazilian basic sanitation companies, the estimates show that the coverage of the population with access to sanitary sewage would go from the current 59.9% to 76.5%. The evidence found provides indications to subsidize sanitation management in the country at the micro-analytical level, enabling a better competitive position in the sector for the integrated management of basic sanitation and its universalization in Brazil.

scholarly journals Highlights from HAWC

2019 ◽  
Vol 208 ◽  
pp. 14001
Author(s):  
H. León Vargas
Keyword(s):  
Energy Range ◽  
High Altitude ◽  
High Precision ◽  
Gamma Rays ◽  
Large Data ◽  
Cosmic Ray ◽  
Air Showers ◽  
Data Set ◽  
Sierra Negra Volcano ◽  
Selection Of

The HAWC (High Altitude Water Cherenkov) observatory, located on the slopes of the Sierra Negra volcano in the state of Puebla, Mexico, was designed with the goal of detecting gamma-rays in the Teraelectron- volt energy range. However, most of the air showers that are detected with the observatory, with a rate of ≈ 27 kHz, are of hadronic origin. This makes that, after three years of operations, HAWC has accumulated a very large data set that allows to perform cosmic-ray analysis of high precision. The details of the observatory operation, as well as a selection of recent results in cosmic-ray physics are discussed in this work.

scholarly journals Static Mapping of Multiple Data-Parallel Applications on Embedded Many-Core SoCs

2013 ◽  
Vol E96.D (10) ◽  
pp. 2268-2271
Author(s):  
Junya KAIDA ◽  
Yuko HARA-AZUMI ◽  
Takuji HIEDA ◽  
Ittetsu TANIGUCHI ◽  
Hiroyuki TOMIYAMA ◽  
...  
Keyword(s):  
Parallel Applications ◽  
Data Parallel ◽  
Multiple Data ◽  
Many Core

Fault Fictions: Systematic biases in the conceptualization of fault zone architecture

2020 ◽  
Author(s):  
Zoe Shipton ◽  
Jen Roberts ◽  
Emma Comrie ◽  
Yannick Kremer ◽  
Lunn Rebecca ◽  
...  
Keyword(s):  
Mental Models ◽  
Internal Representation ◽  
Geological Uncertainty ◽  
Geological Investigation ◽  
Multiple Data ◽  
Geological Models ◽  
Fault Zone Architecture ◽  
Systematic Biases ◽  
Field Geology ◽  
Selection Of

<p>Mental models are a human’s internal representation of the real world and have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This presentation draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline.</p>

scholarly journals A Linear Algebra Framework for Static High Performance Fortran Code Distribution

1997 ◽  
Vol 6 (1) ◽  
pp. 3-27 ◽  
Author(s):  
Corinne Ancourt ◽  
Fabien Coelho ◽  
FranÇois Irigoin ◽  
Ronan Keryell
Keyword(s):  
Linear Algebra ◽  
High Performance ◽  
Address Space ◽  
Data Parallel ◽  
High Performance Fortran ◽  
Multiple Data ◽  
Fortran Code ◽  
Code Distribution ◽  
Overlap Analysis ◽  
Data Parallel Programming

High Performance Fortran (HPF) was developed to support data parallel programming for single-instruction multiple-data (SIMD) and multiple-instruction multiple-data (MIMD) machines with distributed memory. The programmer is provided a familiar uniform logical address space and specifies the data distribution by directives. The compiler then exploits these directives to allocate arrays in the local memories, to assign computations to elementary processors, and to migrate data between processors when required. We show here that linear algebra is a powerful framework to encode HPF directives and to synthesize distributed code with space-efficient array allocation, tight loop bounds, and vectorized communications forINDEPENDENTloops. The generated code includes traditional optimizations such as guard elimination, message vectorization and aggregation, and overlap analysis. The systematic use of an affine framework makes it possible to prove the compilation scheme correct.

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