scholarly journals PFASST-ER: combining the parallel full approximation scheme in space and time with parallelization across the method

2020 ◽  
Vol 23 (1-4) ◽  
Author(s):  
Ruth Schöbel ◽  
Robert Speck

AbstractTo extend prevailing scaling limits when solving time-dependent partial differential equations, the parallel full approximation scheme in space and time (PFASST) has been shown to be a promising parallel-in-time integrator. Similar to space–time multigrid, PFASST is able to compute multiple time-steps simultaneously and is therefore in particular suitable for large-scale applications on high performance computing systems. In this work we couple PFASST with a parallel spectral deferred correction (SDC) method, forming an unprecedented doubly time-parallel integrator. While PFASST provides global, large-scale “parallelization across the step”, the inner parallel SDC method allows integrating each individual time-step “parallel across the method” using a diagonalized local Quasi-Newton solver. This new method, which we call “PFASST with Enhanced concuRrency” (PFASST-ER), therefore exposes even more temporal concurrency. For two challenging nonlinear reaction-diffusion problems, we show that PFASST-ER works more efficiently than the classical variants of PFASST and can use more processors than time-steps.

2012 ◽  
Vol 63 (2) ◽  
pp. 365-377 ◽  
Author(s):  
Yulai Yuan ◽  
Yongwei Wu ◽  
Qiuping Wang ◽  
Guangwen Yang ◽  
Weimin Zheng

2019 ◽  
Author(s):  
Satya N. V. Arjunan ◽  
Atsushi Miyauchi ◽  
Kazunari Iwamoto ◽  
Koichi Takahashi

ABSTRACTBackgroundStudies using quantitative experimental methods have shown that intracellular spatial distribution of molecules plays a central role in many cellular systems. Spatially resolved computer simulations can integrate quantitative data from these experiments to construct physically accurate models of the systems. Although computationally expensive, microscopic resolution reaction-diffusion simulators, such as Spatiocyte can directly capture intracellular effects comprising diffusion-limited reactions and volume exclusion from crowded molecules by explicitly representing individual diffusing molecules in space. To alleviate the steep computational cost typically associated with the simulation of large or crowded intracellular compartments, we present a parallelized Spatiocyte method called pSpatiocyte.ResultsThe new high-performance method employs unique parallelization schemes on hexagonal close-packed (HCP) lattice to efficiently exploit the resources of common workstations and large distributed memory parallel computers. We introduce a coordinate system for fast accesses to HCP lattice voxels, a parallelized event scheduler, a parallelized Gillespie’s direct-method for unimolecular reactions, and a parallelized event for diffusion and bimolecular reaction processes. We verified the correctness of pSpatiocyte reaction and diffusion processes by comparison to theory. To evaluate the performance of pSpatiocyte, we performed a series of parallelized diffusion runs on the RIKEN K computer. In the case of fine lattice discretization with low voxel occupancy, pSpatiocyte exhibited 74% parallel efficiency and achieved a speedup of 7686 times with 663552 cores compared to the runtime with 64 cores. In the weak scaling performance, pSpatiocyte obtained efficiencies of at least 60% with up to 663552 cores. When executing the Michaelis-Menten benchmark model on an eight-core workstation, pSpatiocyte required 45- and 55-fold shorter runtimes than Smoldyn and the parallel version of ReaDDy, respectively. As a high-performance application example, we study the dual phosphorylation-dephosphorylation cycle of the MAPK system, a typical reaction network motif in cell signaling pathways.ConclusionspSpatiocyte demonstrates good accuracies, fast runtimes and a significant performance advantage over well-known microscopic particle simulators for large-scale simulations of intracellular reaction-diffusion systems. The source code of pSpatiocyte is available at https://spatiocyte.org.


Author(s):  
Liangxiu Han

This chapter identifies challenges and requirements for resource sharing to support high performance distributed Service-Oriented Computing (SOC) systems. The chapter draws attention to two popular and important design paradigms: Grid and Peer-to-Peer (P2P) computing systems, which are evolving as two practical solutions to supporting wide-area resource sharing over the Internet. As a fundamental task of resource sharing, the efficient resource discovery is playing an important role in the context of the SOC setting. The chapter presents the resource discovery in Grid and P2P environments through an overview of related systems, both historical and emerging. The chapter then discusses the exploitation of both technologies for facilitating the resource discovery within large-scale distributed computing systems in a flexible, scalable, fault-tolerant, interoperable and security fashion.


2020 ◽  
Vol 17 (9) ◽  
pp. 4411-4418
Author(s):  
S. Jagannatha ◽  
B. N. Tulasimala

In the world of information communication technology (ICT) the term Cloud Computing has been the buzz word. Cloud computing is changing its definition the way technocrats are using it according to the environment. Cloud computing as a definition remains very contentious. Definition is stated liable to a particular application with no unanimous definition, making it altogether elusive. In spite of this, it is this technology which is revolutionizing the traditional usage of computer hardware, software, data storage media, processing mechanism with more of benefits to the stake holders. In the past, the use of autonomous computers and the nodes that were interconnected forming the computer networks with shared software resources had minimized the cost on hardware and also on the software to certain extent. Thus evolutionary changes in computing technology over a few decades has brought in the platform and environment changes in machine architecture, operating system, network connectivity and application workload. This has made the commercial use of technology more predominant. Instead of centralized systems, parallel and distributed systems will be more preferred to solve computational problems in the business domain. These hardware are ideal to solve large-scale problems over internet. This computing model is data-intensive and networkcentric. Most of the organizations with ICT used to feel storing of huge data, maintaining, processing of the same and communication through internet for automating the entire process a challenge. In this paper we explore the growth of CC technology over several years. How high performance computing systems and high throughput computing systems enhance computational performance and also how cloud computing technology according to various experts, scientific community and also the service providers is going to be more cost effective through different dimensions of business aspects.


Author(s):  
Anne Benoit ◽  
Laurent Lefèvre ◽  
Anne-Cécile Orgerie ◽  
Issam Raïs

Large-scale distributed systems (high-performance computing centers, networks, data centers) are expected to consume huge amounts of energy. In order to address this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures: the time and energy cost of switching on and off, the power and energy consumption bounds caused by the electricity grid or the cooling system, and the availability of renewable energy. In this article, we propose models translating these various constraints into different shutdown policies that can be combined for a multiconstraint purpose. Our models and their combinations are validated through simulations on a real workload trace.


Author(s):  
Marco Atzori ◽  
Wiebke Köpp ◽  
Steven W. D. Chien ◽  
Daniele Massaro ◽  
Fermín Mallor ◽  
...  

AbstractIn situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only $$\approx 21\%$$ ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is $$\approx 99\%$$ ≈ 99 % ). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.


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