Automatic Parallelization of Array-oriented Programs for a Multi-core Machine

Ching, Wai-Mee; Zheng, Da

doi:10.1007/s10766-012-0197-6

Cited by 9 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraint programming [74,[145][146][147] Cube computation Dynamic programming [114] Domain specific languages [153] Stochastic programming [122] [69] [148] Scientific Programming Scheduling techniques Workflow [95] Computation model…”

Section: Gradient Descent Searchmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

View full text Add to dashboard Cite

The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

show abstract

Section: Gradient Descent Searchmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Ching et. al [3] parallelize APL code by compiling it to C. Accelerator [26] compiles data-parallel operations on the fly to execute programs on a GPU.…”

Section: Related Workmentioning

confidence: 99%

FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs

Zheng,

Mhembere,

Vogelstein

et al. 2016

Preprint

Self Cite

View full text Add to dashboard Cite

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets. The general approach for speeding up an implementation in R is to implement the algorithms in C or FOR-TRAN and provide an R wrapper. FlashR takes a different approach: it executes R code in parallel and scales the code beyond memory capacity by utilizing solid-state drives (SSDs) automatically. It provides a small number of generalized operations (GenOps) upon which we reimplement a large number of matrix functions in the R base package. As such, FlashR parallelizes and scales existing R code with little/no modification. To reduce data movement between CPU and SSDs, FlashR evaluates matrix operations lazily, fuses operations at runtime, and uses cache-aware, two-level matrix partitioning. We evaluate FlashR on a variety of machine learning and statistics algorithms on inputs of up to four billion data points. FlashR out-of-core tracks closely the performance of FlashR in-memory. The R code for machine learning algorithms executed in FlashR outperforms the in-memory execution of H2O and Spark MLlib by a factor of 2 − 10 and outperforms Revolution R Open by more than an order of magnitude.

show abstract

Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language

Tan

Falcou

Etiemble

et al. 2015

Int J Parallel Prog

View full text Add to dashboard Cite

Providing high level tools for parallel programming while sustaining a high level of performance has been a challenge that techniques like Domain Specific Embedded Languages try to solve. In previous works, we investigated the design of such a DSEL -NT 2 -providing a Matlab -like syntax for parallel numerical computations inside a C++ library. In this paper, we show how NT 2 has been redesigned for shared memory systems in an extensible and portable way. The new NT 2 design relies on a tiered Parallel Skeleton system built using asynchronous task management and automatic compile-time taskification of user level code. We describe how this system can operate various shared memory runtimes and evaluate the design by using several benchmarks implementing linear algebra algorithms.

show abstract

Automatic Parallelization of Array-oriented Programs for a Multi-core Machine

Cited by 9 publications

References 16 publications

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs

Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language

Contact Info

Product

Resources

About