LULESH 2.0 Updates and Changes

Karlin, Ian; Keasler, Jeff; Neely, J R

doi:10.2172/1090032

Cited by 211 publications

(94 citation statements)

References 5 publications

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“…Hence, beside the contribution of extending the CARM, we present the following work in the remainder of this paper: 1) we implemented a tool based on CARM methodology and the proposed improvements to automat-ically instantiate and validate the model on multi-socket systems and Knights Landing (KNL) Xeon Phi (for various memory configurations); 2) we thus validate the new model with high accuracy micro-benchmarks, for both systems; 3) We also demonstrate the model usability with synthetic benchmarks from the BLAS package; and 4) we exhibit the model ability to pinpoint data locality issues on MG from the NAS parallel benchmarks [13] and Lulesh proxy-application [14], where several data allocation policies are applied.…”

Section: Locality Aware Roofline Modelingmentioning

confidence: 82%

Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model

Denoyelle

Goglin

Ilić

et al. 2017

Lecture Notes in Computer Science

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Abstract. In order to fulfill modern applications needs, computing systems become more powerful, heterogeneous and complex. NUMA platforms and emerging high bandwidth memories offer new opportunities for performance improvements. However they also increase hardware and software complexity, thus making application performance analysis and optimization an even harder task. The Cache-Aware Roofline Model (CARM) is an insightful, yet simple model designed to address this issue. It provides feedback on potential applications bottlenecks and shows how far is the application performance from the achievable hardware upper-bounds. However, it does not encompass NUMA systems and next generation processors with heterogeneous memories. Yet, some application bottlenecks belong to those memory subsystems, and would benefit from the CARM insights. In this paper, we fill the missing requirements to scope recent large shared memory systems with the CARM. We provide the methodology to instantiate, and validate the model on a NUMA system as well as on the latest Xeon Phi processor equiped with configurable hybrid memory. Finally, we show the model ability to exhibits several bottlenecks of such systems, which were not supported by CARM.

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Section: Locality Aware Roofline Modelingmentioning

confidence: 82%

Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model

Denoyelle

Goglin

Ilić

et al. 2017

Lecture Notes in Computer Science

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“…characterized the performance of VMWare ESXi, KVM, Xen, and Linux Containers (LXC) using the PCI pass-through mode. They tested the CUDA SHOC and OpenCL SDK benchmark suites as microbenchmarks, and the LAMMPS molecular dynamics simulator [Plimpton et al 2007], GPU-LIBSVM [Athanasopoulos et al 2011], and the LULESH shock hydrodynamics simulator [Karlin et al 2013] as application benchmarks. The authors observe that KVM consistently yields nearnative performance in all benchmark programs.…”

Section: Methods Supporting a Single Vmmentioning

confidence: 99%

GPU Virtualization and Scheduling Methods

2017

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The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey paper, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.

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“…Lulesh [6] implements a solution of the Sedov blast problem for a material in three dimensions. It defines a discrete mesh that covers the region of interest and it partitions the problem into a collection of elements where hydrodynamic equations are applied.…”

Section: Benchmarksmentioning

confidence: 99%

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Vassiliadis

Chalios

Parasyris

et al. 2016

Int J Parallel Prog

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Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints.Evaluation on a dual-socket 16-core Intel platform using 9 kernels and applications shows that the proposed framework performs very close to an oracle always selecting the optimal configuration, both in terms of energy efficiency and quality of results. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

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LULESH 2.0 Updates and Changes

Cited by 211 publications

References 5 publications

Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model

Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model

GPU Virtualization and Scheduling Methods

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

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