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检索条件"任意字段=29th International European Conference on Parallel and Distributed Computing, Euro-Par 2023"

共 43 条记录，以下是21-30 订阅

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parareal with a Physics-Informed Neural Network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

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29th international conference on parallel and distributed computing (euro-par)

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves parareal's single-node performance. this suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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Exploring Mapping Strategies for Co-allocated HPC Applications

Exploring Mapping Strategies for Co-allocated HPC Applicatio...

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29th international conference on parallel and distributed computing (euro-par)

作者： Vardar, Ioannis Hunold, Sascha Swartvagher, Philippe Traff, Jesper Larsson TU Wien A-1040 Vienna Austria

ISBN: (纸本)9783031488023;9783031488030

In modern HPC systems with deep hierarchical architectures, large-scale applications often struggle to efficiently utilize the abundant cores due to the saturation of resources such as memory. Co-allocating multiple applications to share compute nodes can mitigate these issues and increase system throughput. However, co-allocation may harm the performance of individual applications due to resource contention. Past research suggests that topology-aware mappings can improve the performance of parallel applications that do not share resources. In this work, we implement application-oblivious, topology-aware process-to-core mappings via different core enumerations that support the co-allocation of parallel applications. We show that these mappings have a significant impact on the available memory bandwidth. We explore how these process-to-core mappings can affect the individual application duration as well as the makespan of job schedules when they are combined with co-allocation. Our main objective is to assess whether co-allocation with a topology-aware mapping can be a viable alternative to the exclusive node allocation policies that are currently common in HPC clusters.

关键词： High Performance computing parallel computing Performance Optimization Process Mapping Co-allocation

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SWSPH: A Massively parallel SPH Implementation for Hundred-Billion-particle Simulation on New Sunway Supercomputer 29th

SWSPH: A Massively Parallel SPH Implementation for Hundred-B...

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29th international conference on parallel and distributed computing (euro-par)

作者： Zhang, Ziyu Chen, Junshi Wang, Zhanming Luo, Yifan Yao, Jineng Huang, Shenghong An, Hong Univ Sci & Technol China 96 JinZhai Rd Baohe Dist Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9783031396977;9783031396984

Fluid instability plays a fundamental role in the research of astrophysics, energy power, chemical industry and new materials. the Smoothed particle Hydrodynamics (SPH) method is a useful tool for simulating interfacial flows such as multiphase flow, high-velocity impact, explosion phenomenon. However, SPH method harnesses an enormous amount of particles for accuracy, which consumes a lot of computing power. In this paper, we present a massively parallel SPH scheme on the new Sunway supercomputer, SWSPH. In order to take full advantage of large-scale heterogeneous many-core computing system, we propose a series of parallel strategies and optimization methods. Experiments show that SWSPH has the capability of handling hundred-billion-particles simulations of fluid instability phenomenon on 39 million cores with a performance of 76% parallel efficiency.

关键词： Smoothed particle Hydrodynamics Sunway Supercomputer Manycore computing Large-scale simulation

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Optimizing distributed Tensor Contractions Using Node-Aware Processor Grids 29th

Optimizing Distributed Tensor Contractions Using Node-Aware ...

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29th international conference on parallel and distributed computing (euro-par)

作者： Irmler, Andreas Kanakagiri, Raghavendra Ohlmann, Sebastian T. Solomonik, Edgar Grueneis, Andreas TU Wien Inst Theoret Phys Vienna Austria Max Planck Comp & Data Facil Garching Germany Univ Illinois Champaign IL USA

ISBN: (纸本)9783031396977;9783031396984

We propose an algorithm that aims at minimizing the internode communication volume for distributed and memory-efficient tensor contraction schemes on modern multi-core compute nodes. the key idea is to define processor grids that optimize intra-/inter-node communication volume in the employed contraction algorithms. We present an implementation of the proposed node-aware communication algorithm into the Cyclops Tensor Framework (CTF). We demonstrate that this implementation achieves a significantly improved performance for matrix-matrix-multiplication and tensor-contractions on up to several hundreds modern compute nodes compared to conventional implementations without using node-aware processor grids. Our implementation shows good performance when compared with existing state-of-the-art parallel matrix multiplication libraries (COSMA and ScaLAPACK). In addition to the discussion of the performance for matrix-matrix-multiplication, we also investigate the performance of our node-aware communication algorithm for tensor contractions as they occur in quantum chemical coupled-cluster methods. To this end we employ a modified version of CTF in combination with a coupled-cluster code (Cc4s). Our findings show that the node-aware communication algorithm is also able to improve the performance of coupled-cluster theory calculations for real-world problems running on tens to hundreds of compute nodes.

关键词： Numerical methods

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parallel Cholesky Factorization for Banded Matrices Using OpenMP Tasks 29th

Parallel Cholesky Factorization for Banded Matrices Using Op...

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29th international conference on parallel and distributed computing (euro-par)

作者： Liu, Felix Fredriksson, Albin Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden RaySearch Labs Stockholm Sweden

ISBN: (纸本)9783031396977;9783031396984

Cholesky factorization is a method for solving linear systems involving symmetric, positive-definite matrices, and can be an attractive choice in applications where a high degree of numerical stability is needed. One such application is mathematical optimization, where direct methods for solving linear systems are widely used and often a significant performance bottleneck. An example where this is the case, and the specific type of optimization problem motivating this work, is radiation therapy treatment planning, where mathematical optimization is used to create individual treatment plans for patients. To address this bottleneck, we propose a task-based multi-threaded method for Cholesky factorization of banded matrices with medium-sized bands. We implement our algorithm using OpenMP tasks and compare our performance with state-of-the-art libraries such as Intel MKL. Our performance measurements show a performance that is on par or better than Intel MKL (up to similar to 26% on a single CPU socket) for a wide range of matrix bandwidths on two different Intel CPU systems.

关键词： Cholesky factorization Task-Based parallelism Linear Solver

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parameterized Analysis of a Dynamic Programming Algorithm for a parallel Machine Scheduling Problem 29th

Parameterized Analysis of a Dynamic Programming Algorithm fo...

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29th international conference on parallel and distributed computing (euro-par)

作者： Tarhan, Istenc Carlier, Jacques Hanen, Claire Jouglet, Antoine Kordon, Alix Munier Sorbonne Univ CNRS LIP6 F-75005 Paris France UTC Heudiasyc Compiegne France Univ Paris Nanterre UPL F-92000 Nanterre France

ISBN: (纸本)9783031396977;9783031396984

We consider in this paper the scheduling problem defined by a set of dependent jobs with release times and deadlines to be processed by identical parallel machines. this problem is denoted by P|prec, ri, di|(*) in the literature. Starting from an extension of the Branch-and-Bound algorithm of Demeulemeester and Herroelen to take into account release times and deadlines, we build a state graph of which longest paths represent all active schedule. New dominance rules are also proposed. We establish that our state graph construction algorithm is fixed-parameter tractable. the two parameters are the pathwidth, which corresponds to the maximum number of overlapping jobs time windows and the maximum execution time of a job. the algorithm is experimented on random instances. these experiments show that the pathwidth is also a key factor of the practical complexity of the algorithm.

关键词： Scheduling parallel machines Release times and deadlines Branch-and-Bound Fixed-parameter tractable

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distributed k-Means with Outliers in General Metrics 29th

Distributed k-Means with Outliers in General Metrics

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29th international conference on parallel and distributed computing (euro-par)

作者： Dandolo, Enrico Pietracaprina, Andrea Pucci, Geppino Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9783031396977;9783031396984

Center-based clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is the k-means problem, which, given a set P of points from a metric space and a parameter k < |P|, requires finding a subset S subset of P of k points, dubbed centers, which minimizes the sum of all squared distances of points in P from their closest center. A more general formulation, introduced to deal with noisy datasets, features a further parameter z and allows up to z points of P (outliers) to be disregarded when computing the aforementioned sum. We present a distributed coreset-based 3-round approximation algorithm for k-means with z outliers for general metric spaces, using MapReduce as a computational model. Our distributed algorithm requires sublinear local memory per reducer, and yields a solution whose approximation ratio is an additive term O(gamma) away from the one achievable by the best known polynomial-time sequential (possibly bicriteria) approximation algorithm, where gamma can be made arbitrarily small. An important feature of our algorithm is that it obliviously adapts to the intrinsic complexity of the dataset, captured by its doubling dimension D. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance tradeoffs for general metrics.

关键词： Clustering k-means Outliers MapReduce Coreset

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A Performance Analysis of Leading Many-Core Technologies for Cellular Automata Execution

A Performance Analysis of Leading Many-Core Technologies for...

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29th international conference on parallel and distributed computing (euro-par)

作者： De Rango, Alessio D'Ambrosio, Donato Senatore, Alfonso Mendicino, Giuseppe Narasimhan, Kumudha Goli, Mehdi Burns, Rod Univ Calabria Dept Environm Engn Arcavacata Di Rende Italy Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy Codeplay Software Ltd Intel Co Edinburgh Midlothian Scotland

ISBN: (纸本)9783031506833;9783031506840

We extend the panorama of performance analyses of CUDA, OpenCL and SYCL for the execution of Cellular Automata. To this end, we apply the SciddicaT landslide model to a real event by considering two complex topographic surfaces of different granularity, thus resulting in two simulations of different computing loads. For each technology, we developed a global memory and two tiled implementations of SciddicaT by adopting the Nvidia nvcc compiler for CUDA, the Nvidia implementation of the OpenCL standard and the CUDA back-end of the Intel DPC++ compiler for SYCL. the experiments, performed on three Nvidia accelerators, point out from good to optimal performances of SYCL compared to CUDA according to the newer device's architecture. the carried-out Roofline analysis evidences high cache effects, pointing out greater advantages of tiled implementations for older architectures.

关键词： Cellular Automata Data-parallel Structured-Grid Fluid-Flow Simulation CUDA vs OpenCL vs SYCL Roofline

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Dynamic Allocations in a Hierarchical parallel Context

Dynamic Allocations in a Hierarchical Parallel Context

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international workshops held at the 29th international conference on parallel and distributed computing, euro-par 2023

作者： Millan, Aymeric Padioleau, thomas Bigot, Julien Université Paris-Saclay UVSQ CNRS CEA Maison de la Simulation Gif-sur-Yvette91191 France

ISBN: (纸本)9783031488023

this poster investigates the challenges of dynamic memory allocation in a hierarchical parallel context for the GYSELA code, a gyrokinetic simulation tool for studying plasma turbulence. Using the SYCL 2020 programming model and extensions, the study explores memory management using different parallelism paradigms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Plasma turbulence

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perun: Benchmarking Energy Consumption of High-Performance computing Applications 29th

perun: Benchmarking Energy Consumption of High-Performance C...

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29th international conference on parallel and distributed computing (euro-par)

作者： Muriedas, Juan Pedro Gutierrez Hermosillo Fluegel, Katharina Debus, Charlotte Obermaier, Holger Streit, Achim Goetz, Markus Karlsruhe Inst Technol KIT Steinbuch Ctr Comp SCC Hermann Von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Helmholtz AI Karlsruhe Germany

ISBN: (纸本)9783031396977;9783031396984

Looking closely at the Top500 list of high-performance computers (HPC) in the world, it becomes clear that computing power is not the only number that has been growing in the last three decades. the amount of power required to operate such massive computing machines has been steadily increasing, earning HPC users a higher than usual carbon footprint. While the problem is well known in academia, the exact energy requirements of hardware, software and how to optimize it are hard to quantify. To tackle this issue, we need tools to understand the software and its relationship with power consumption in today's high performance computers. With that in mind, we present perun, a Python package and command line interface to measure energy consumption based on hardware performance counters and selected physical measurement sensors. this enables accurate energy measurements on various scales of computing, from a single laptop to an MPI-distributed HPC application. We include an analysis of the discrepancies between these sensor readings and hardware performance counters, with particular focus on the power draw of the usually overlooked non-compute components such as memory. One of our major insights is their significant share of the total energy consumption. We have equally analyzed the runtime and energy overhead perun generates when monitoring common HPC applications, and found it to be minimal. Finally, an analysis on the accuracy of different measuring methodologies when applied at large scales is presented.

关键词： Energy Benchmarking High-performance computing Artificial Intelligence distributed Memory System

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