22 research outputs found
Particle-In-Cell Simulation using Asynchronous Tasking
Recently, task-based programming models have emerged as a prominent
alternative among shared-memory parallel programming paradigms. Inherently
asynchronous, these models provide native support for dynamic load balancing
and incorporate data flow concepts to selectively synchronize the tasks.
However, tasking models are yet to be widely adopted by the HPC community and
their effective advantages when applied to non-trivial, real-world HPC
applications are still not well comprehended. In this paper, we study the
parallelization of a production electromagnetic particle-in-cell (EM-PIC) code
for kinetic plasma simulations exploring different strategies using
asynchronous task-based models. Our fully asynchronous implementation not only
significantly outperforms a conventional, synchronous approach but also
achieves near perfect scaling for 48 cores.Comment: To be published on the 27th European Conference on Parallel and
Distributed Computing (Euro-Par 2021
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
Particle-in-cell simulation using asynchronous tasking
Recently, task-based programming models have emerged as a prominent alternative among shared-memory parallel programming paradigms. Inherently asynchronous, these models provide native support for dynamic load balancing and incorporate data flow concepts to selectively synchronize the tasks. However, tasking models are yet to be widely adopted by the HPC community and their effective advantages when applied to non-trivial, real-world HPC applications are still not well comprehended. In this paper, we study the parallelization of a production electromagnetic particle-in-cell (EM-PIC) code for kinetic plasma simulations exploring different strategies using asynchronous task-based models. Our fully asynchronous implementation not only significantly outperforms a conventional, synchronous approach but also achieves near perfect scaling for 48 cores.Peer ReviewedPostprint (author's final draft
Towards an auto-tuned and task-based SpMV (LASs Library)
We present a novel approach to parallelize the SpMV kernel included in LASs (Linear Algebra routines on OmpSs) library, after a deep review and analysis of several well-known approaches. LASs is based on OmpSs, a task-based runtime that extends OpenMP directives, providing more flexibility to apply new strategies. Based on tasking and nesting, with the aim of improving the workload imbalance inherent to the SpMV operation, we present a strategy especially useful for highly imbalanced input matrices. In this approach, the number of created tasks is dynamically decided in order to maximize the use of the resources of the platform. Throughout this paper, SpMV behavior depending on the selected strategy (state of the art and proposed strategies) is deeply analyzed, setting in this way the base for a future auto-tunable code that is able to select the most suitable approach depending on the input matrix. The experiments of this work were carried out for a set of 12 matrices from the Suite Sparse Matrix Collection, all of them with different characteristics regarding their sparsity. The experiments of this work were performed on a node of Marenostrum 4 supercomputer (with two sockets Intel Xeon, 24 cores each) and on a node of Dibona cluster (using one ARM ThunderX2 socket with 32 cores). Our tests show that, for Intel Xeon, the best parallelization strategy reduces the execution time of the reference MKL multi-threaded version up to 67%. On ARM ThunderX2, the reduction is up to 56% with respect to the OmpSs parallel reference.This project has received funding from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015- 65316-P), the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Parallels (2014-SGR-1051), and the Juan de la Cierva Grant Agreement No IJCI-2017- 33511, and the Spanish Ministry of Science and Innovation under the project Heterogeneidad y especialización en la era post-Moore (RTI2018-093684-BI00). We also acknowledge the funding provided by Fujitsu under the BSC-Fujitsu joint project: Math Libraries Migration and OptimizationPeer ReviewedPostprint (author's final draft
Tasking in accelerators: performance evaluation
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this work, we analyze the implications and results of implementing dynamic parallelism, concurrent kernels and CUDA Graphs to solve task-oriented problems. As a benchmark we propose three different methods for solving DGEMM operation on tiled-matrices; which might be the most popular benchmark for performance analysis. For the algorithms that we study, we present significant differences in terms of data dependencies, synchronization and granularity. The main contribution of this work is determining which of the previous approaches work better for having multiple task running concurrently in a single GPU, as well as stating the main limitations and benefits of every technique. Using dynamic parallelism and CUDA Streams we were able to achieve up to 30% speedups and for CUDA Graph API up to 25x acceleration outperforming state of the art results.This project has received funding from the EPEEC project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801051, from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII ( TIN2015-65316-P ) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Pro-gramació i Entorns d’Execució Paral·lels (2014-SGR-1051 ). Finally, this project also received funding from the Spanish Ministry of Economy and Competitiveness under the Juan de la Cierva Grant Agreement No IJCI-2017-33511 , and from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement No. 749516 .Peer ReviewedPostprint (author's final draft
MPI+OpenMP tasking scalability for multi-morphology simulations of the human brain
The simulation of the behavior of the human brain is one of the most ambitious challenges today with a non-end of important applications. We can find many different initiatives in the USA, Europe and Japan which attempt to achieve such a challenging target. In this work, we focus on the most important European initiative (the Human Brain Project) and on one of the models developed in this project. This tool simulates the spikes triggered in a neural network by computing the voltage capacitance on the neurons’ morphology, being one of the most precise simulators today. In the present work, we have evaluated the use of MPI+OpenMP tasking on top of this framework. We prove that this approach is able to achieve a good scaling even when computing a relatively low workload (number of neurons) per node. One of our targets consists of achieving not only a highly scalable implementation, but also to develop a tool with a high degree of abstraction without losing control and performance by using MPI+OpenMP tasking. The main motivation of this work is the evaluation of this cutting-edge simulation on multi-morphology neural networks. The simulation of a high number of neurons, which are completely different among them, is an important challenge. In fact, in the multi-morphology simulations, we find an important unbalancing between the nodes, mainly due to the differences in the neurons, which causes an important under-utilization of the available resources. In this work, the authors present and evaluate mechanisms to deal with this and reduce the time of this kind of simulations considerably.We would like to appreciate the valuable feedback and help provided by the main developers of the Arbor simulator: Benjamin Cumming (ETH Zürich) and Alexander Peyser (Jülich Supercomputing Center). This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1 and SGA2), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316- P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Paral · lels (2014-SGR-1051). Finally, this project also received funding from the Spanish Ministry of Economy and Competitiveness under the Juan de la Cierva Grant Agreement No IJCI-2017-33511, and from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement No. 749516.Peer ReviewedPostprint (author's final draft)Postprint (author's final draft
General Undergraduate Catalog, 2019-2020
Marshall University Undergraduate Course Catalog for the 2019-2020 academic year.https://mds.marshall.edu/catalog_2010-2019/1000/thumbnail.jp
MPI+OpenMP tasking scalability for the simulation of the human brain
The simulation of the behavior of the Human Brain is one of the most ambitious challenges today with a non-end of important applications. We can find many different initiatives in the USA, Europe and Japan which attempt to achieve such a challenging target. In this work we focus on the most important European initiative (Human Brain Project) and on one of the tools (Arbor). This tool simulates the spikes triggered in a neuronal network by computing the voltage capacitance on the neurons' morphology, being one of the most precise simulators today. In the present work, we have evaluated the use of MPI+OpenMP tasking on top of the Arbor simulator. In this paper, we present the main characteristics of the Arbor tool and how these can be efficiently managed by using MPI+OpenMP tasking. We prove that this approach is able to achieve a good scaling even when computing a relatively low workload (number of neurons) per node using up to 32 nodes. Our target consists of achieving not only a highly scalable implementation based on MPI, but also to develop a tool with a high degree of abstraction without losing control and performance by using MPI+OpenMP tasking.We would like to apreciate the valuable feedback and help provided by Benjamin Cumming and Alexander Peyser. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1 and HBP SGA2), from the Spanish Ministry of Economy and Competitiveness under the project Computacion de Altas Prestaciones VII (TIN2015- ´ 65316-P) and the Departament d’Innovacio, Universitats i ´ Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programacio i Entorns d’Execuci ´ o Paral ´ ·lels (2014-SGR-1051). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grand agreement No.749516Peer ReviewedPostprint (author version
General Undergraduate Catalog, 2018-2019
Marshall University Undergraduate Course Catalog for the 2018-2019 academic year.https://mds.marshall.edu/catalog_2010-2019/1001/thumbnail.jp