Structured Parallel Programming: patterns for efficient computation : Michael McCool, Arch D Robison, James Reinders, Morgan Kaufmann-Elsevier 2012

In this book the authors, who are parallel computing experts and industry insiders, describe how to design and implement maintainable and efficient parallel algorithms using a pattern-based approach. They present both theory and practice, and give some specific examples using multiple programming models.Facultad de Informátic

Structured Parallel Programming: patterns for efficient computation : Michael McCool, Arch D Robison, James Reinders, Morgan Kaufmann-Elsevier 2012

In this book the authors, who are parallel computing experts and industry insiders, describe how to design and implement maintainable and efficient parallel algorithms using a pattern-based approach. They present both theory and practice, and give some specific examples using multiple programming models.Facultad de Informátic

Brook GLES Pi: democratising accelerator programming

Nowadays computing is heavily-based on accelerators, however, the cost of the hardware equipment prevents equal access to heterogeneous programming. In this work we present Brook GLES Pi, a port of the accelerator programming language Brook. Our solution, primarily focused on the educational platform Raspberry Pi, allows to teach, experiment and take advantage of heterogeneous programming on any low-cost embedded device featuring an OpenGL ES 2 GPU, democratising access to accelerator programming.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft

Finding parallel patterns through static analysis in C++ applications

Since The 'Free Lunch' Of Processor Performance Is Over, Parallelism Has Become The New Trend In Hardware And Architecture Design. However, Parallel Resources Deployed In Data Centers Are Underused In Many Cases, Given That Sequential Programming Is Still Deeply Rooted In Current Software Development. To Address This Problem, New Methodologies And Techniques For Parallel Programming Have Been Progressively Developed. For Instance, Parallel Frameworks, Offering Programming Patterns, Allow Expressing Concurrency In Applications To Better Exploit Parallel Hardware. Nevertheless, A Large Portion Of Production Software, From A Broad Range Of Scientific And Industrial Areas, Is Still Developed Sequentially. Considering That These Software Modules Contain Thousands, Or Even Millions, Of Lines Of Code, An Extremely Large Amount Of Effort Is Needed To Identify Parallel Regions. To Pave The Way In This Area, This Paper Presents Parallel Pattern Analyzer Tool, A Software Component That Aids The Discovery And Annotation Of Parallel Patterns In Source Codes. This Tool Simplifies The Transformation Of Sequential Source Code To Parallel. Specifically, We Provide Support For Identifying Map, Farm, And Pipeline Parallel Patterns And Evaluate The Quality Of The Detection For A Set Of Different C++ Applications.This work was partially supported by the EU Projects ICT 644235 “RePhrase: Refactoring Parallel Heterogeneous Resource-Aware Applications” and the FP7 609666 “Repara: Reengineering and Enabling Performance and Power of Application

A Fast Causal Profiler for Task Parallel Programs

This paper proposes TASKPROF, a profiler that identifies parallelism bottlenecks in task parallel programs. It leverages the structure of a task parallel execution to perform fine-grained attribution of work to various parts of the program. TASKPROF's use of hardware performance counters to perform fine-grained measurements minimizes perturbation. TASKPROF's profile execution runs in parallel using multi-cores. TASKPROF's causal profile enables users to estimate improvements in parallelism when a region of code is optimized even when concrete optimizations are not yet known. We have used TASKPROF to isolate parallelism bottlenecks in twenty three applications that use the Intel Threading Building Blocks library. We have designed parallelization techniques in five applications to in- crease parallelism by an order of magnitude using TASKPROF. Our user study indicates that developers are able to isolate performance bottlenecks with ease using TASKPROF.Comment: 11 page

Diseño de una infraestructura de computación de alto rendimiento para objetos paralelos en un lenguaje de alto nivel

Proyecto 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

Paving the way towards high-level parallel pattern interfaces for data stream processing

The emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerably reduced the development and parallelization efforts of this type of applications. An example of parallel pattern interface is GrPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GrPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports considerable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GrPPI pattern interface from the usability, flexibility and readability points of view.This work was partially supported by the EU project ICT 644235 “RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications” and the project TIN2013-41350-P “Scalable Data Management Techniques for High-End Computing Systems” from the Ministerio de Economía y Competitividad, Spai