scholarly journals The Fortran-P Translator: Towards Automatic Translation of Fortran 77 Programs for Massively Parallel Processors

1995 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Matthew O'keefe ◽  
Terence Parr ◽  
B. Kevin Edgar ◽  
Steve Anderson ◽  
Paul Woodward ◽  
...  
Keyword(s):  
High Performance ◽  
Parallel Machines ◽  
Parallel Processors ◽  
Massively Parallel ◽  
Automatic Translation ◽  
Efficient Code ◽  
Self Similar ◽  
User Friendly ◽  
Application Codes ◽  
Fortran 77

Massively parallel processors (MPPs) hold the promise of extremely high performance that, if realized, could be used to study problems of unprecedented size and complexity. One of the primary stumbling blocks to this promise has been the lack of tools to translate application codes to MPP form. In this article we show how applications codes written in a subset of Fortran 77, called Fortran-P, can be translated to achieve good performance on several massively parallel machines. This subset can express codes that are self-similar, where the algorithm applied to the global data domain is also applied to each subdomain. We have found many codes that match the Fortran-P programming style and have converted them using our tools. We believe a self-similar coding style will accomplish what a vectorizable style has accomplished for vector machines by allowing the construction of robust, user-friendly, automatic translation systems that increase programmer productivity and generate fast, efficient code for MPPs.

scholarly journals Design and Performance Analysis of a Massively Parallel Atmospheric General Circulation Model

2000 ◽  
Vol 8 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Daniel S. Schaffer ◽  
Max J. Suárez
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
High Performance ◽  
Degrees Of Freedom ◽  
Parallel Machines ◽  
Atmospheric General Circulation Model ◽  
Circulation Model ◽  
Massively Parallel ◽  
Atmospheric General Circulation ◽  
And Performance

In the 1990's, computer manufacturers are increasingly turning to the development of parallel processor machines to meet the high performance needs of their customers. Simultaneously, atmospheric scientists studying weather and climate phenomena ranging from hurricanes to El Niño to global warming require increasingly fine resolution models. Here, implementation of a parallel atmospheric general circulation model (GCM) which exploits the power of massively parallel machines is described. Using the horizontal data domain decomposition methodology, this FORTRAN 90 model is able to integrate a 0.6° longitude by 0.5° latitude problem at a rate of 19 Gigaflops on 512 processors of a Cray T3E 600; corresponding to 280 seconds of wall-clock time per simulated model day. At this resolution, the model has 64 times as many degrees of freedom and performs 400 times as many floating point operations per simulated day as the model it replaces.

The physical structure of concurrent problems and concurrent computers

1988 ◽  
Vol 326 (1591) ◽  
pp. 411-444 ◽  
Keyword(s):  
High Performance ◽  
Parallel Machines ◽  
Temporal Structure ◽  
Physical Structure ◽  
Massively Parallel ◽  
Single Instruction Multiple Data ◽  
Multiple Data ◽  
Network Methods ◽  
Particle Process ◽  
Physical Analogy

We introduce a physical analogy to describe problems and high-performance concurrent computers on which they are run. We show that the spatial characteristics of problems lead to their parallelism and review the lessons from use of the early hypercubes and a natural particle-process analogy. We generalize this picture to include the temporal structure of problems and show how this allows us to unify distributed, shared and hierarchical memories as well as SIMD (single instruction multiple data) architectures. We also show how neural network methods can be used to analyse a general formalism based on interacting strings and these lead to possible real-time schedulers and decomposers for massively parallel machines.

scholarly journals An Algebraic Machinery for Optimizing Data Motion for HPF

1997 ◽  
Vol 6 (3) ◽  
pp. 297-325
Author(s):  
Jan-Jan Wu ◽  
Marina C. Chen
Keyword(s):  
High Performance ◽  
Parallel Machines ◽  
Compiler Optimization ◽  
Optimization Technique ◽  
Massively Parallel ◽  
High Performance Fortran ◽  
Fortran 90

This paper describes a general compiler optimization technique that reduces communica tion over-head for FORTRAN-90 (and High Performance FORTRAN) implementations on massively parallel machines.

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan
Keyword(s):  
Antimicrobial Resistance ◽  
High Performance ◽  
Pathogen Detection ◽  
Bacterial Species ◽  
Workflow Management ◽  
Metagenomic Data ◽  
Antimicrobial Resistance Genes ◽  
Culture Independent ◽  
Automated Pipeline ◽  
User Friendly

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

scholarly journals Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yehe Liu ◽  
Andrew M. Rollins ◽  
Richard M. Levenson ◽  
Farzad Fereidouni ◽  
Michael W. Jenkins
Keyword(s):  
High Performance ◽  
Point Of Care ◽  
Low Cost ◽  
Consumer Electronics ◽  
Practical Implementation ◽  
The Novel ◽  
Point Of Care Diagnostics ◽  
Optical Module ◽  
Optical Configuration ◽  
User Friendly

AbstractSmartphone microscopes can be useful tools for a broad range of imaging applications. This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone. It enables high-quality multichannel fluorescence microscopy with submicron resolution over a 10× equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable, and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.

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