This paper examines energy management in a heterogeneous processor consisting of an integrated CPU&#x2013;GPU for high-performance computing (HPC) applications. Energy management for HPC applications is challenged by their uncompromising performance requirements and complicated by the need for coordinating energy management across distinct core types &#x2013; a new and less understood problem. We examine the intra-node CPU&#x2013;GPU frequency sensitivity of HPC applications on tightly coupled CPU&#x2013;GPU architectures as the first step in understanding power and performance optimization for a heterogeneous multi-node HPC system. The insights from this analysis form the basis of a coordinated energy management scheme, called DynaCo, for integrated CPU&#x2013;GPU architectures. We implement DynaCo on a modern heterogeneous processor and compare its performance to a state-of-the-art power- and performance-management algorithm. DynaCo improves measured average energy-delay squared (ED2) product by up to 30&#x25; with less than 2&#x25; average performance loss across several exascale and other HPC workloads.

Indrani Paul

Vignesh Ravi

Srilatha Manne

Manish Arora

Sudhakar Yalamanchili

Coordinated Energy Management in Heterogeneous Processors

The technical papers program for SC13 received 449 submissions of which 90 where selected for the program giving an acceptance rate of 20&#x25;. A rigorous peer review process, including author rebuttals and a 1.5 day face-to-face program committee meeting ensured that selected papers were the very best in our field. One of the tasks at the face-to-face meeting was also to select finalists for the best paper award, from which one is selected by a committee during the conference. To further highlight their achievement of being selected as the very top tier of all the submitted papers to SC13, the authors of these finalist papers were offered the opportunity to publish extended versions of their papers in this special issue journal; all eight authors accepted.

William Gropp

Satoshi Matsuoka

Special Issue: SC13 &#x2013; The International Conference for High Performance Computing, Networking, Storage and Analysis

Drawing parallels to the rise of general purpose graphical processing units (GPGPUs) as accelerators for specific high-performance computing (HPC) workloads, there is a rise in the use of non-volatile memory (NVM) as accelerators for I/O-intensive scientific applications. However, existing works have explored use of NVM within dedicated I/O nodes, which are distant from the compute nodes that actually need such acceleration. As NVM bandwidth begins to out-pace point-to-point network capacity, we argue for the need to break from the archetype of completely separated storage. Therefore, in this work we investigate co-location of NVM and compute by varying I/O interfaces, file systems, types of NVM, and both current and future SSD architectures, uncovering numerous bottlenecks implicit in these various levels in the I/O stack. We present novel hardware and software solutions, including the new Unified File System (UFS), to enable fuller utilization of the new compute-local NVM storage. Our experimental evaluation, which employs a real-world Out-of-Core (OoC) HPC application, demonstrates throughput increases in excess of an order of magnitude over current approaches.

Myoungsoo Jung

Ellis H. Wilson

Wonil Choi

John Shalf

Hasan Metin Aktulga

Chao Yang

Erik Saule

Umit V. Catalyurek

Mahmut Kandemir

Exploring the Future of Out-of-Core Computing with Compute-Local Non-Volatile Memory

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (218) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (40963) particle cosmological simulations, accounting for 4&#xd7;1020 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

Michael S. Warren

2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation

Co-location, where multiple jobs share compute nodes in large-scale HPC systems, has been shown to increase aggregate throughput and energy efficiency by 10&#x2013;20&#x25;. However, system operators disallow co-location due to fair-pricing concerns, i.e., a pricing mechanism that considers performance interference from co-running jobs. In the current pricing model, application execution time determines the price, which results in unfair prices paid by the minority of users whose jobs suffer from co-location. This paper presents POPPA, a runtime system that enables fair pricing by delivering precise online interference detection and facilitates the adoption of supercomputers with co-locations. POPPA leverages a novel shutter mechanism &#x2013; a cyclic, fine-grained interference sampling mechanism to accurately deduce the interference between co-runners &#x2013; to provide unbiased pricing of jobs that share nodes. POPPA is able to quantify inter-application interference within 4&#x25; mean absolute error on a variety of co-located benchmark and real scientific workloads.

Alex D. Breslow

Ananta Tiwari

Martin Schulz

Laura Carrington

Lingjia Tang

Jason Mars

Enabling Fair Pricing on High Performance Computer Systems with Node Sharing

This paper examines whether lossy compression can be used effectively in physics simulations as a possible strategy to combat the expected data-movement bottleneck in future high performance computing architectures. We show that, for the codes and simulations we tested, compression levels of 3&#x2013;5X can be applied without causing significant changes to important physical quantities. Rather than applying signal processing error metrics, we utilize physics-based metrics appropriate for each code to assess the impact of compression. We evaluate three different simulation codes: a Lagrangian shock-hydrodynamics code, an Eulerian higher-order hydrodynamics turbulence modeling code, and an Eulerian coupled laser-plasma interaction code. We compress relevant quantities after each time-step to approximate the effects of tightly coupled compression and study the compression rates to estimate memory and disk-bandwidth reduction. We find that the error characteristics of compression algorithms must be carefully considered in the context of the underlying physics being modeled.

Daniel Laney

Steven Langer

Christopher Weber

Peter Lindstrom

Al Wegener

Assessing the Effects of Data Compression in Simulations Using Physically Motivated Metrics

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

Eli Dart

Lauren Rotman

Brian Tierney

Mary Hester

Jason Zurawski

The Science DMZ: A Network Design Pattern for Data-Intensive Science

Modern interconnects offer remote direct memory access (RDMA) features. Yet, most applications rely on explicit message passing for communications albeit their unwanted overheads. The MPI-3.0 standard defines a programming interface for exploiting RDMA networks directly, however, it's scalability and practicability has to be demonstrated in practice. In this work, we develop scalable bufferless protocols that implement the MPI-3.0 specification. Our protocols support scaling to millions of cores with negligible memory consumption while providing highest performance and minimal overheads. To arm programmers, we provide a spectrum of performance models for all critical functions and demonstrate the usability of our library and models with several application studies with up to half a million processes. We show that our design is comparable to, or better than UPC and Fortran Coarrays in terms of latency, bandwidth and message rate. We also demonstrate application performance improvements with comparable programming complexity.

Robert Gerstenberger

Maciej Besta

Torsten Hoefler

Enabling Highly-Scalable Remote Memory Access Programming with MPI-3 One Sided

Domain decomposition methods are, alongside multigrid methods, one of the dominant paradigms in contemporary large-scale partial differential equation simulation. In this paper, a lightweight implementation of a theoretically and numerically scalable preconditioner is presented in the context of overlapping methods. The performance of this work is assessed by numerical simulations executed on thousands of cores, for solving various highly heterogeneous elliptic problems in both 2D and 3D with billions of degrees of freedom. Such problems arise in computational science and engineering, in solid and fluid mechanics. While focusing on overlapping domain decomposition methods might seem too restrictive, it will be shown how this work can be applied to a variety of other methods, such as non-overlapping methods and abstract deflation based preconditioners. It is also presented how multilevel preconditioners can be used to avoid communication during an iterative process such as a Krylov method.

Pierre Jolivet

Fr&#xe9;d&#xe9;ric Hecht

Fr&#xe9;d&#xe9;ric Nataf

Christophe Prud'homme

Scalable Domain Decomposition Preconditioners for Heterogeneous Elliptic Problems

About this journal

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

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Scientific Programming | Hindawi

Scientific Programming

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

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Satoshi  Matsuoka