A hierarchic task-based programming model for distributed heterogeneous computing

Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Clouds introducing in its computing nodes, processors with different core architectures, accelerators (i.e. GPUs, FPGAs), as well as different memories and storage devices in order to achieve better performance with lower energy consumption. As a consequence of this heterogeneity, programming applications for these distributed heterogeneous platforms becomes a complex task. Additionally to the complexity of developing an application for distributed platforms, developers must also deal now with the complexity of the different computing devices inside the node. In this article, we present a programming model that aims to facilitate the development and execution of applications in current and future distributed heterogeneous parallel architectures. This programming model is based on the hierarchical composition of the COMP Superscalar and Omp Superscalar programming models that allow developers to implement infrastructure-agnostic applications. The underlying runtime enables applications to adapt to the infrastructure without the need of maintaining different versions of the code. Our programming model proposal has been evaluated on real platforms, in terms of heterogeneous resource usage, performance and adaptation.This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272.Peer ReviewedPostprint (author's final draft

Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models

[EN] Directive-based programming models, such as OpenMP, OpenACC, and OmpSs, enable users to accelerate applications by using coprocessors with little effort. These devices offer significant computing power, but their use can introduce two problems: an increase in the total cost of ownership and their underutilization because not all codes match their architecture. Remote accelerator virtualization frameworks address those problems. In particular, rCUDA provides transparent access to any graphic processor unit installed in a cluster, reducing the number of accelerators and increasing their utilization ratio. Joining these two technologies, directive-based programming models and rCUDA, is thus highly appealing. In this work, we study the integration of OmpSs and OpenACC with rCUDA, describing and analyzing several applications over three different hardware configurations that include two InfiniBand interconnections and three NVIDIA accelerators. Our evaluation reveals favorable performance results, showing low overhead and similar scaling factors when using remote accelerators instead of local devices.The researchers from the Universitat Jaume I de Castello were supported by Universitat Jaume I research project (P11B2013-21), project TIN2014-53495-R, a Generalitat Valenciana grant and FEDER. The researcher from the Barcelona Supercomputing Center (BSC-CNS) Lausanne was supported by the European Commission (HiPEAC-3 Network of Excellence, FP7-ICT 287759), Intel-BSC Exascale Lab collaboration, IBM/BSC Exascale Initiative collaboration agreement, Computacion de Altas Prestaciones VI (TIN2012-34557) and the Generalitat de Catalunya (2014-SGR-1051). This work was partially supported by the U.S. Dept. of Energy, Office of Science, Office of Advanced Scientific Computing Research (SC-21), under contract DE-AC02-06CH11357. The initial version of rCUDA was jointly developed by Universitat Politecnica de Valencia (UPV) and Universitat Jaume I de Castellon (UJI) until year 2010. This initial development was later split into two branches. Part of the UPV version was used in this paper. The development of the UPV branch was supported by Generalitat Valenciana under Grants PROMETEO 2008/060 and Prometeo II 2013/009. We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.Castelló-Gimeno, A.; Peña Monferrer, AJ.; Mayo Gual, R.; Planas, J.; Quintana Ortí, ES.; Balaji, P. (2018). Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models. The Journal of Supercomputing. 74(11):5628-5642. https://doi.org/10.1007/s11227-016-1791-yS562856427411Strohmaier E, Dongarra J, Simon H, Meuer M (2015) TOP500 supercomputing sites. http://www.top500.org/lists/2015/11 . Accessed Nov 2015NVIDIA (2015) CUDA API reference, version 7.5Shreiner D, Sellers G, Kessenich JM, Licea-Kane BM (2013) OpenGL programming guide: the official guide to learning OpenGL. Addison-Wesley Professional, BostonMark WR, Glanville RS, Akeley K, Kilgard MJ (2003) Cg: a system for programming graphics hardware in a C-like language. ACM Trans Graph (TOG) 22(3):896–907Munshi A (2014)The OpenCL specification 2.0. 0.5em minus 0.4em Khronos OpenCL working groupOpenACC directives for accelerators (2015). http://www.openacc-standard.org . Accessed Dec 2015OmpSs project home page. http://pm.bsc.es/ompss . Accessed Dec 2015OpenMP application program interface 4.0 (2013). OpenMP Architecture BoardPeña AJ (2013) Virtualization of accelerators in high performance clusters. Ph.D. dissertation, Universitat Jaume I, CastellónKawai A, Yasuoka K, Yoshikawa K, Narumi T (2012) Distributed-shared CUDA: virtualization of large-scale GPU systems for programmability and reliability. In: International conference on future computational technologies and applicationsShi L, Chen H, Sun J, Li K (2012) vCUDA: GPU-accelerated high-performance computing in virtual machines. IEEE Trans Comput 61(6):804–816Xiao S, Balaji P, Zhu Q, Thakur R, Coghlan S, Lin H, Wen G, Hong J, Feng W (2012) VOCL: an optimized environment for transparent virtualization of graphics processing units. In: Innovative parallel computing. IEEE, New YorkKim J, Seo S, Lee J, Nah J, Jo G, Lee J (2012) SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters. In: International conference on supercomputingDuran A, Ayguadé E, Badia RM, Labarta J, Martinell L, Martorell X, Planas J (2011) OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Process Lett 21(02):173–193Castelló A, Duato J, Mayo R, Peña AJ, Quintana-Ortí ES, Roca V, Silla F (2014) On the use of remote GPUs and low-power processors for the acceleration of scientific applications. In: The fourth international conference on smart grids, green communications and IT energy-aware technologies, pp 57–62Iserte S, Castelló A, Mayo R, Quintana-Ortí ES, Reaño C, Prades J, Silla F, Duato J (2014) SLURM support for remote GPU virtualization: implementation and performance study. In: International symposium on computer architecture and high performance computing (SBAC-PAD)Peña AJ, Reaño C, Silla F, Mayo R, Quintana-Ortí ES, Duato J (2014) A complete and efficient CUDA-sharing solution for HPC clusters. Parallel Comput 40(10):574–588Kegel P, Steuwer M, Gorlatch S (2012) dOpenCL: towards a uniform programming approach for distributed heterogeneous multi-/many-core systems. In: International parallel and distributed processing symposium workshops (IPDPSW)Castelló A, Peña AJ, Mayo R, Balaji P, Quintana-Ortí ES (2015) Exploring the suitability of remote GPGPU virtualization for the OpenACC programming model using rCUDA. In: IEEE international conference on cluster computingCastelló A, Mayo R, Planas J, Quintana-Ortí ES (2015) Exploiting task-parallelism on GPU clusters via OmpSs and rCUDA virtualization. In: IEEE international workshop on reengineering for parallelism in heterogeneous parallel platformsHP Corp., Intel Corp., Microsoft Corp., Phoenix Tech. Ltd., Toshiba Corp. (2011) Advanced configuration and power interface specification, revision 5.0Reaño C, Silla F, Castelló A, Peña AJ, Mayo R, Quintana-Ortí ES, Duato J (2014) Improving the user experience of the rCUDA remote GPU virtualization framework. Concurr Comput 27(14):3746–3770PGI compilers and tools (2015) http://www.pgroup.com/ . Accessed Dec 2015Johnson N (2013) EPCC OpenACC benchmark suite. https://www.epcc.ed.ac.uk/ . Accessed Dec 2015Herdman J, Gaudin W, McIntosh-Smith S, Boulton M, Beckingsale D, Mallinson A, Jarvis SA (2012) Accelerating hydrocodes with OpenACC, OpenCL and CUDA. In: SC companion: high performance computing, networking, storage and analysi

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

Self-adaptive OmpSs tasks in heterogeneous environments

As new heterogeneous systems and hardware accelerators appear, high performance computers can reach a higher level of computational power. Nevertheless, this does not come for free: the more heterogeneity the system presents, the more complex becomes the programming task in terms of resource management. OmpSs is a task-based programming model and framework focused on the runtime exploitation of parallelism from annotated sequential applications. This paper presents a set of extensions to this framework: we show how the application programmer can expose different specialized versions of tasks (i.e. pieces of specific code targeted and optimized for a particular architecture) and how the system can choose between these versions at runtime to obtain the best performance achievable for the given application. From the results obtained in a multi-GPU system, we prove that our proposal gives flexibility to application's source code and can potentially increase application's performance.This work has been supported by the European Commission through the ENCORE project (FP7-248647), the TERAFLUX project (FP7-249013), the TEXT project (FP7-261580), the HiPEAC-3 Network of Excellence (FP7-ICT 287759), the Intel-BSC Exascale Lab collaboration project, the support of the Spanish Ministry of Education (CSD2007- 00050 and FPU program), the projects of Computación de Altas Prestaciones V and VI (TIN2007-60625, TIN2012-34557) and the Generalitat de Catalunya (2009-SGR-980).Peer ReviewedPostprint (author’s final draft

Selection of Task Implementations in the Nanos++ Runtime

New heterogeneous systems and hardware accelerators can give higher levels of computational power to high performance computers. However, this does not come for free, since the more heterogeneity the system presents, the more complex becomes the programming task in terms of resource utilization. OmpSs is a task-based programming model and framework focused on the automatic parallelization of sequential applications. We present a set of extensions to this framework: we show how the application programmer can expose different specialized versions of tasks (i.e. pieces of specific code targeted and optimized for a particular architecture) and how the framework will choose between these versions at runtime to obtain the best performance achievable for the given application. From our results, obtained in a multi-GPU system, we can prove that our project gives flexibility to application's source code and can potentially increase application’s performance

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version

Porting and tuning of the Mont-Blanc benchmarks to the multicore ARM 64bit architecture

This project is about porting and tuning the Mont-Blanc benchmarks to the multicore ARM 64 bits architecture. The Mont-Blanc benchmarks are part of the Mont-Blanc European project and they have been developed internally in the BSC (Barcelona Supercomputing Center). The project will explore the possibilities that an ARM architecture can offer running in a HPC (High Performance Computing) setup, this includes to learn how to tune and adapt a parallelized computer program and analyze its execution behavior. As part of the project, we will analyze the performance of each benchmark using instrumentation tools such like Extrae and Paraver. Each benchmark will be adapted, tuned and executed mainly in the three new Mont-Blanc mini-clusters, Thunder (ARMv8 custom), Merlin (ARMv8 custom) and Jetson TX (ARMv8 cortex-a57) using the OmpSs programming model. The evolution of the performance obtained will be shown followed by a brief analysis of the results after each optimization.Aquest projecte es basa en adaptar i afinar els Mont-Blanc benchmarks a l’arquitectura multinucli ARM 64 bits. Els Mont-Blanc benchmarks formen part del projecte Europeu Mont-Blanc i han estat desenvolupats internament en el BSC (Barcelona Supercomputing Center). Aquest projecte explorarà el potencial d’usar l’arquitectura ARM en un entorn HPC (High Performance Computing), això inclou aprendre a adaptar i afinar un programa paral·lel, i analitzar el seu comportament durant l’execució. Com a part del projecte, s’analitzarà el rendiment de cada benchmark usant eines d’instrumentació com Extrae o Paraver. Cada benchmark serà adaptat, afinat i executat en els tres nous miniclústers de Mont-Blanc, Thunder (ARMv8 personalitzat), Merlin (ARMv8 personalitzat) i Jetson TX (ARMv8 cortex-a57) usant el model de programació OmpSs. Es mostrarà l’evolució del rendiment, seguit d’una breu explicació dels resultats després de cada optimització.Este proyecto se basa en adaptar y afinar los Mont-blanc benchmarks a la arquitectura multi-núcleo ARM 64 bits. Los Mont-Blanc benchmarks forman parte del proyecto Europeo Mont-Blanc y han sido desarrollados internamente en el BSC (Barcelona Supercomputing Center). Este proyecto explorará el potencial de usar la arquitectura ARM en un entorno HPC (High Performance Computing), esto incluye aprender a adaptar y afinar un programa paralelo, y analizar su comportamiento durante la ejecución. Como parte del proyecto, se analizará el rendimiento de cada benchmark usando herramientas de instrumentación como Extrae o Paraver. Cada benchmark será adaptado, afinado y ejecutado en los tres nuevos mini-clústeres de Mont-Blanc, Thunder (ARMv8 personalizado), Merlin (ARMv8 personalizado) y Jetson TX (ARMv8 cortex-a57) usando el modelo de programación OmpSs. Se mostrará la evolución del rendimiento obtenido, y una breve explicación de los resultados después de cada optimización

Run-time support for multi-level disjoint memory address spaces

High Performance Computing (HPC) systems have become widely used tools in many industry areas and research fields. Research to produce more powerful and efficient systems has grown in par with their popularity. As a consequence, the complexity of modern HPC architectures has increased in order to provide systems with the highest levels of performance. This increased complexity has also affected the way HPC systems are programmed. HPC users have to deal with new devices, languages and tools, and this is can be a significant access barrier to people that do not have a deep knowledge in computer science. On par with the evolution of HPC systems, programming models have also evolved to ease the task of developing applications for these machines. Two well-known examples are OpenMP and MPI. The former can be used in shared memory systems and is praised for offering an easy methodology of software development. The latter is more popular because it targets distributed environments but it is considered burdensome to use. Besides these two, many programming models have emerged to propose new methodologies or to handle new hardware devices. One of these models is OmpSs. OmpSs is a programming model for modern HPC systems that is based on OpenMP and StarSs. Developed by the Programming Models group at the Barcelona Supercomputing Center, it targets the latest generation of HPC systems while benefiting from the ease of use of OpenMP. OmpSs offers asynchronous parallelism with the concept of tasks with data dependencies. These tasks allow the specification of sections of code that can be executed in parallel while the dependencies specify the restrictions about the order in which the tasks can be executed. With this, OmpSs programs can adapt to a many different system configurations while fundamentally still being sequential programs with annotations. This thesis explores the benefits of providing OmpSs the capability to target architectures with complex memory hierarchies. An example of such systems can be the new generation of clusters that use accelerators to power their computing capabilities. The memory hierarchy of these machines is composed of a first level of distributed memory formed by the memory of each individual node, and a second level formed by the private memory of each accelerator devices. Our first contribution shows the implementation of the support of cluster of multi-cores for the OmpSs programming model. We also present two optimizations to boost the performance of applications running on top of cluster systems: a specific task scheduling policy and the addition of slave-to-slave transfers. We evaluate our implementation using a set of benchmarks coded in OmpSs and we also compare them against the same applications implemented using MPI, the most widely used programming model for these systems. We extend our initial implementation in our second contribution, which provides OmpSs with support for clusters of GPUs. We show that OmpSs programs targeting these complex systems are capable of achieving a good performance when compared against MPI+CUDA implementations. The third contribution of this thesis presents an implementation and evaluation of the performance and programmability impact of supporting non-contiguous memory regions. Offering this feature allows applications with complex data accesses to be easily annotated with OmpSs. This is important to widen the spectrum of applications that can be handled by the programming model.Els sistemes de computació d'altes prestacions (CAP) han esdevingut eines importants en diferents sectors industrials i camps de recerca. La recerca per produir sistemes més potents i eficients ha crescut proporcionalment a aquesta popularitat. Com a conseqüència, la complexitat d'aquest tipus de sistemes s'ha incrementat per tal de dotar-los d'altes prestacions. Aquest increment en la complexitat també ha afectat la manera de programar aquest tipus de sistemes. Els usuaris de sistemes CAP han de treballar amb nous dispositius, llenguatges i eines, i això pot convertir-se en una barrera d'entrada significativa per aquelles persones que no tinguin uns alts coneixements informàtics. Seguin l'evolució dels sistemes CAP, els models de programació també han evolucionat per tal de facilitar la tasca de desenvolupar aplicacions per aquests sistemes. Dos exemples ben coneguts son OpenMP i MPI. El primer es pot utilitzar en sistemes de memòria compartida i es reconegut per oferir una metodologia de desenvolupament senzilla. El segon és més popular perquè està dissenyat per sistemes distribuïts, però està considerat difícil d'utilitzar. A part d'aquests dos, altres models de programació han sorgit per proposar noves metodologies o per suportar nous components hardware. Un d'aquests nous models és OmpSs. OmpSs és un model de programació per sistemes CAP moderns que està basat en OpenMP i StarSs. Desenvolupat pel grup de Models de Programació del Barcelona Supercomputing Center, està dissenyat per suportar la darrera generació de sistemes CAP i alhora oferir la facilitat d'us d'OpenMP. OmpSs ofereix paral·lelisme asíncron mitjançant el concepte de tasques amb dependències de dades. Aquestes tasques permeten especificar regions de codi que poden ser executades en paral·lel, mentre que les dependències especifiquen les restriccions sobre l'ordre en que aquestes tasques poden ser executades. Amb això, els programes fets amb OmpSs poden adaptar-se a sistemes amb diferents configuracions tot i ser fonamentalment programes seqüencials amb anotacions. Aquesta tesi explora els beneficis de proveir a OmpSs amb la capacitat de funcionar sobre arquitectures amb jerarquies de memòria complexes. Un exemple d'un sistema així pot ser un dels clústers de nova generació que utilitzen acceleradors per tal d'oferir més capacitat de càlcul. La jerarquia de memòria en aquestes màquines està composada per un primer nivell de memòria distribuïda formada per la memòria de cada node individual, i el segon nivell està format per la memòria privada de cada accelerador. La primera contribució d'aquesta tesi mostra la implementació del suport de clústers de multi-cores pel model de programació OmpSs. També presentem dos optimitzacions per millorar el rendiment de les aplicacions quan s'executen en sistemes clúster: una política de planificació de tasques específica i la incorporació dels missatges entre nodes esclaus. Avaluem la nostra implementació usant un conjunt d'aplicacions programades en OmpSs i també les comparem amb les mateixes aplicacions implementades usant MPI, el model de programació més estès per aquest tipus de sistemes. En la segona contribució estenem la nostra implementació inicial per tal de dotar OmpSs de suport per clústers de GPUs. Mostrem que els programes OmpSs son capaços d'obtenir un bon rendiment sobre aquests tipus de sistemes, fins i tot quan els comparem amb versions implementades usant MPI+CUDA. La tercera contribució descriu la implementació i avaluació del rendiment i de l'impacte de suportar regions de memòria no contigües. Oferir aquesta funcionalitat permet implementar fàcilment amb OmpSs aplicacions amb accessos complexes a memòria, cosa que és important de cara a ampliar l'espectre d'aplicacions que poden ser tractades pel model de programació