Renaissance: a modern benchmark suite for parallel applications on the JVM

AbstractHeterogeneous multiprocessors can offer high performance at low energy expenditures. However, to be able to use them in hard real-time systems, timing guarantees need to be provided, and the main challenge is to determine the worst-case schedule length (also known as makespan) of an application. Previous works that estimate the makespan focus mainly on the independent-task application model or the related multiprocessor model that limits the applicability of the makespan. On the other hand, the directed acyclic graph (DAG) application model and the unrelated multiprocessor model are general and can cover most of today’s platforms and applications. In this work, we propose a simple work-conserving scheduling method of the tasks in a DAG and two new approaches to finding the makespan. A set of representative OpenMP task-based parallel applications from the BOTS benchmark suite and synthetic DAGs are used to evaluate the proposed method. Based on the empirical results, the proposed approach calculates the makespan close to the exhaustive method and with low pessimism compared to a lower bound of the actual makespan calculation.

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Adding a Reproducible Airplane Model to the Austin RCS Benchmark Suite

2020 International Applied Computational Electromagnetics Society Symposium (ACES) ◽

10.23919/aces49320.2020.9196153 ◽

2020 ◽

Author(s):

Jon T. Kelley ◽

Andrew Maicke ◽

David A. Chamulak ◽

Clifton C. Courtney ◽

Ali E. Yilmaz

Keyword(s):

Benchmark Suite

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Matlab and Parallel Computing

Image Processing & Communications ◽

10.2478/v10248-012-0048-5 ◽

2012 ◽

Vol 17 (4) ◽

pp. 207-216 ◽

Cited By ~ 5

Author(s):

Magdalena Szymczyk ◽

Piotr Szymczyk

Keyword(s):

Image Processing ◽

Signal Processing ◽

Parallel Computing ◽

Distributed Computing ◽

Control Systems ◽

High Performance ◽

Parallel Applications ◽

Process Simulations ◽

Key Features ◽

Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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Statistical and machine learning models for optimizing energy in parallel applications

The International Journal of High Performance Computing Applications ◽

10.1177/1094342019842915 ◽

2019 ◽

Vol 33 (6) ◽

pp. 1079-1097 ◽

Cited By ~ 2

Author(s):

Mark Endrei ◽

Chao Jin ◽

Minh Ngoc Dinh ◽

David Abramson ◽

Heidi Poxon ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

High Performance ◽

Large Scale ◽

Energy Use ◽

Parallel Applications ◽

Learning Models ◽

Trade Off ◽

Time Required ◽

Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

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