Peachy Parallel Assignments (EduHPC 2018)

Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes implementing a subset of OpenMP using pthreads, creating an animated fractal, image processing using histogram equalization, simulating a storm of high-energy particles, and solving the wave equation in a variety of settings. All of these come with sample assignment sheets and the necessary starter code.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Facilitar la inclusión de ejercicios prácticos de programación paralela en cursos de Computación Paralela o de alto rendimiento (HPC)Comunicación en congreso: Descripción de ejercicios prácticos con acceso a material ya desarrollado y probado

Hardware acceleration of reaction-diffusion systems:a guide to optimisation of pattern formation algorithms using OpenACC

Reaction Diffusion Systems (RDS) have widespread applications in computational ecology, biology, computer graphics and the visual arts. For the former applications a major barrier to the development of effective simulation models is their computational complexity - it takes a great deal of processing power to simulate enough replicates such that reliable conclusions can be drawn. Optimizing the computation is thus highly desirable in order to obtain more results with less resources. Existing optimizations of RDS tend to be low-level and GPGPU based. Here we apply the higher-level OpenACC framework to two case studies: a simple RDS to learn the ‘workings’ of OpenACC and a more realistic and complex example. Our results show that simple parallelization directives and minimal data transfer can produce a useful performance improvement. The relative simplicity of porting OpenACC code between heterogeneous hardware is a key benefit to the scientific computing community in terms of speed-up and portability

Automatic Parallelisation of Web Applications

Small web applications have a tendency to get bigger. Yet despite the current popularity of web applications, little has been done to help programmers to leverage the performance and scalability benefits that can result from the introduction of parallelism into a program. Accordingly, we present a technique for the automatic parallelisation of whole web applications, including persistent data storage mechanisms. We detail our prototype implementation of this technique, Ceth and finally, we establish the soundness of the process by which we extract coarse-grained parallelism from programs

A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options

We present a parallel algorithm for solving backward stochastic differential equations (BSDEs in short) which are very useful theoretic tools to deal with many financial problems ranging from option pricing option to risk management. Our algorithm based on Gobet and Labart (2010) exploits the link between BSDEs and non linear partial differential equations (PDEs in short) and hence enables to solve high dimensional non linear PDEs. In this work, we apply it to the pricing and hedging of American options in high dimensional local volatility models, which remains very computationally demanding. We have tested our algorithm up to dimension 10 on a cluster of 512 CPUs and we obtained linear speedups which proves the scalability of our implementationComment: 25 page

A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options

We present a parallel algorithm for solving backward stochastic differential equations (BSDEs in short) which are very useful theoretic tools to deal with many financial problems ranging from option pricing option to risk management. Our algorithm based on Gobet and Labart (2010) exploits the link between BSDEs and non linear partial differential equations (PDEs in short) and hence enables to solve high dimensional non linear PDEs. In this work, we apply it to the pricing and hedging of American options in high dimensional local volatility models, which remains very computationally demanding. We have tested our algorithm up to dimension 10 on a cluster of 512 CPUs and we obtained linear speedups which proves the scalability of our implementationbackward stochastic differential equations, parallel computing, Monte- Carlo methods, non linear PDE, American options, local volatility model.

parMERASA Multi-Core Execution of Parallelised Hard Real-Time Applications Supporting Analysability

International audienceEngineers who design hard real-time embedded systems express a need for several times the performance available today while keeping safety as major criterion. A breakthrough in performance is expected by parallelizing hard real-time applications and running them on an embedded multi-core processor, which enables combining the requirements for high-performance with timing-predictable execution. parMERASA will provide a timing analyzable system of parallel hard real-time applications running on a scalable multicore processor. parMERASA goes one step beyond mixed criticality demands: It targets future complex control algorithms by parallelizing hard real-time programs to run on predictable multi-/many-core processors. We aim to achieve a breakthrough in techniques for parallelization of industrial hard real-time programs, provide hard real-time support in system software, WCET analysis and verification tools for multi-cores, and techniques for predictable multi-core designs with up to 64 cores