In this paper we consider productivity challenges for parallel programmers and explore ways that parallel language design might help improve end-user productivity. We offer a candidate list of desirable qualities for a parallel programming language, and describe how these qualities are addressed in the design of the Chapel language. In doing so, we provide an overview of Chapel's features and how they help address parallel productivity. We also survey current techniques for parallel programming and describe ways in which we consider them to fall short of our idealized productive programming model.

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Parallel Programmability and the Chapel Language

This paper presents a many-core visual computing architecture code named Larrabee, a new software rendering pipeline, a manycore programming model, and performance analysis for several applications. Larrabee uses multiple in-order x86 CPU cores that are augmented by a wide vector processor unit, as well as some fixed function logic blocks. This provides dramatically higher performance per watt and per unit of area than out-of-order CPUs on highly parallel workloads. It also greatly increases the flexibility and programmability of the architecture as compared to standard GPUs. A coherent on-die 2nd level cache allows efficient inter-processor communication and high-bandwidth local data access by CPU cores. Task scheduling is performed entirely with software in Larrabee, rather than in fixed function logic. The customizable software graphics rendering pipeline for this architecture uses binning in order to reduce required memory bandwidth, minimize lock contention, and increase opportunities for parallelism relative to standard GPUs. The Larrabee native programming model supports a variety of highly parallel applications that use irregular data structures. Performance analysis on those applications demonstrates Larrabee's potential for a broad range of parallel computation.

/pdf/larrabee-a-many-core-x86-architecture-for-visual-computing-58j94b6w2n.pdf

Larrabee: a many-core x86 architecture for visual computing

The perfectly matched layer (PML) absorbing boundary condition has proven to be very efficient from a numerical point of view for the elastic wave equation to absorb both body waves with nongrazing incidence and surface waves. However, at grazing incidence the classical discrete PML method suffers from large spurious reflections that make it less efficient for instance in the case of very thin mesh slices, in the case of sources located close to the edge of the mesh, and/or in the case of receivers located at very large offset. We demonstrate how to improve the PML at grazing incidence for the differential seismic wave equation based on an unsplit convolution technique. The improved PML has a cost that is similar in terms of memory storage to that of the classical PML. We illustrate the efficiency of this improved convolutional PML based on numerical benchmarks using a finite-difference method on a thin mesh slice for an isotropic material and show that results are significantly improved compared with the classical PML technique. We also show that, as the classical PML, the convolutional technique is intrinsically unstable in the case of some anisotropic materials.

An unsplit convolutional perfectly matched layer improved at grazing incidence for the seismic wave equation

The constantly increasing demand for more computing power can seem impossible to keep up with. However, multicore processors capable of performing computations in parallel allow computers to tackle ever larger problems in a wide variety of applications. This book provides a comprehensive introduction to parallel computing, discussing theoretical issues such as the fundamentals of concurrent processes, models of parallel and distributed computing, and metrics for evaluating and comparing parallel algorithms, as well as practical issues, including methods of designing and implementing shared- and distributed-memory programs, and standards for parallel program implementation, in particular MPI and OpenMP interfaces. Each chapter presents the basics in one place followed by advanced topics, allowing novices and experienced practitioners to quickly find what they need. A glossary and more than 80 exercises with selected solutions aid comprehension. The book is recommended as a text for advanced undergraduate or graduate students and as a reference for practitioners.

Introduction to Parallel Computing

Characteristics-based marker-in-cell method with conservative finite-differences schemes for modeling geological flows with strongly variable transport properties

Aimed at the working researcher or scientific C/C++ or Fortran programmer, this text introduces the competent research programmer to a new vocabulary of idioms and techniques for parallelizing software using OpenMP.

http://www.dfmf.uned.es/~osotolongo/pub/mpi/Parallel%20programming%20in%20OpenMP.pdf

Jeff McDonald

Papers

Parallel Programming in OpenMP

Getting Started with OpenMP