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At least 91 records · Page 5

Scheduling for Parallel Supercomputing: A Historical Perspective of Achievable Utilization

The NAS facility has operated parallel supercomputers for the past 11 years, including the Intel iPSC/860, Intel Paragon, Thinking Machines CM-5, IBM SP-2, and Cray Origin 2000. Across this wide variety of machine architectures, across a span of 10 years, across a large number of different users, and through thousands of minor configuration and policy changes, the utilization of these machines shows three general trends: (1) scheduling using a naive FIFO first-fit policy results in 40-60% utilization, (2) switching to the more sophisticated dynamic backfilling scheduling algorithm improves utilization by about 15 percentage points (yielding about 70% utilization), and (3) reducing the maximum allowable job size further increases utilization. Most surprising is the consistency of these trends. Over the lifetime of the NAS parallel systems, we made hundreds, perhaps thousands, of small changes to hardware, software, and policy, yet, utilization was affected little. In particular these results show that the goal of achieving near 100% utilization while supporting a real parallel supercomputing workload is unrealistic.

Jones, James Patton↗

Code IN Exhibits - Supercomputing 2000

The creation of parameter study suites has recently become a more challenging problem as the parameter studies have become multi-tiered and the computational environment has become a supercomputer grid. The parameter spaces are vast, the individual problem sizes are getting larger, and researchers are seeking to combine several successive stages of parameterization and computation. Simultaneously, grid-based computing offers immense resource opportunities but at the expense of great difficulty of use. We present ILab, an advanced graphical user interface approach to this problem. Our novel strategy stresses intuitive visual design tools for parameter study creation and complex process specification, and also offers programming-free access to grid-based supercomputer resources and process automation.

Yarrow, Maurice↗

Misleading Performance Reporting in the Supercomputing Field

In a previous humorous note, I outlined twelve ways in which performance figures for scientific supercomputers can be distorted. In this paper, the problem of potentially misleading performance reporting is discussed in detail. Included are some examples that have appeared in recent published scientific papers. This paper also includes some proposed guidelines for reporting performance, the adoption of which would raise the level of professionalism and reduce the level of confusion in the field of supercomputing.

Bailey, David H.↗

NASA Advanced Supercomputing (NAS) Division

High Performance Computing (HPC) has become an integral part of NASAs Aeronautics research and application endeavors. HPC requirements for Aeroscience applications are increasing by leaps and bounds as scientists and engineers increase the fidelity of the simulations and the size of the engineering databases needed for NASA missions. In this talk we present several such applications and describe how they have benefited from the use of supercomputing. We provide an overview of the resources available at NASA Advanced Supercomputing (NAS) facility at Ames Research Center in Silicon Valley, and also describe the progress in performance of Computational Fluid Dynamics (CFD) codes on current and future HPC technologies. We also discuss some of the challenges in efficiently exploiting the underlying computational resources for such codes.

Mehrotra, Piyush↗

An Analysis of System Balance and Architectural Trends Based on Top500 Supercomputers

Supercomputer design is a complex, multi-dimensional optimization process, wherein several subsystems need to be reconciled to meet a desired figure of merit performance for a portfolio of applications and a budget constraint. However, overall, the HPC community has been gravitating towards ever more Flops, at the expense of many other subsystems. To draw attention to overall system balance, in this paper, we analyze balance ratios and architectural trends in the world’s most powerful supercomputers. Specifically, we have collected the performance characteristics of systems between 1993 and 2019 based on the Top500 lists and then analyzed their architectures from diverse system design perspectives. Notably, our analysis studies the performance balance of the machines, across a variety of subsystems such as compute, memory, I/O, interconnect, intra-node connectivity and power. Our analysis reveals that balance ratios of the various subsystems need to be considered carefully alongside the application workload portfolio to provision the subsystem capacity and bandwidth specifications, which can help achieve optimal performance.

Khan, Awais↗

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers

Modern scientific workflows are increasing in complexity with growth in computation power, incorporation of non-traditional computation methods, and advances in technologies enabling data streaming to support on-the-fly computation. These workflows have unpredictable runtime behaviors, and a fixed, predetermined resource assignment on supercomputers can be inefficient for overall performance and throughput. Inability to change resource assignments further limits the scientists to avail of science-driven opportunities or respond to failures.We introduce DYFLOW, a flexible framework that orchestrates scientific workflows on supercomputers based on user-designed policies. DYFLOW compartmentalizes orchestration stages into simplified constructs, and end-users can program and reuse them according to their workflow requirements through an easy-to-use interface. These constructs hide the intricacies involved in runtime management from end-users, for instance, procurement of information to understand the workflow state, assessment, and supervision of the runtime changes. DYFLOW is designed to work alongside existing workflow management systems and reuse the available (static) support for workflow management. We have integrated DYFLOW with an existing workflow management tool as a demonstration. With experiments performed on use cases from three types of scientific workflows and two different parallel architectures, we show that DYFLOW achieves the desired orchestration incurring a small cost to carry out the runtime changes.

Singhal, Swati↗

HPC Molecular Simulation Tries Out a New GPU: Experiences on Early AMD Test Systems for the Frontier Supercomputer

Molecular simulation is an important tool for nu- merous efforts in physics, chemistry, and the biological sciences. Simulating molecular dynamics requires extremely rapid cal- culations to enable sufficient sampling of simulated temporal molecular processes. The Hewlett Packard Enterprise (HPE) Cray EX Frontier supercomputer installed at the Oak Ridge Leadership Computing Facility (OLCF) will provide an exascale resource for open science, and will feature graphics processing units (GPUs) from Advanced Micro Devices (AMD). The future LUMI supercomputer in Finland will be based on an HPE Cray EX platform as well. Here we test the ports of several widely used molecular dynamics packages that have each made substantial use of acceleration with NVIDIA GPUs, on Spock, the early Cray pre-Frontier testbed system at the OLCF which employs AMD GPUs. These programs are used extensively in industry for pharmaceutical and materials research, as well as academia, and are also frequently deployed on high-performance computing (HPC) systems, including national leadership HPC resources. We find that in general, performance is competitive and installation is straightforward, even at these early stages in a new GPU ecosystem. Our experiences point to an expanding arena for GPU vendors in HPC for molecular simulation.

Sedova, Ada↗

April 2020 Darshan counters from the Summit supercomputer

This dataset is the Darshan counters collected from the Summit supercomputer in a month of April 2020. 1. Description of methods used for collection/generation of data: Job submitted on Summit HPC system when completed successfully and has made I/O calls (captured by Darshan tool) writes a Darshan log file on alpine filesystem. One job can have multiple `jsrun` commands and Darshan will generate separate logs each log corresponding to an `jsrun` command, so a job can have one or more Darshan logs associated with it. 2. Methods for processing the data: To process the data, we first use `darshan-util` tool to parse the Darshan logs. Then we restructure the logs and merge data from multiple Darshan logs if they belong to the same Summit job.

97 MATHEMATICS AND COMPUTING↗

Small tensor product distributed active space (STP-DAS) framework for relativistic and non-relativistic multiconfiguration calculations: Scaling from 10 9 on a laptop to 10 12 determinants on a supercomputer

Despite the power and flexibility of configuration interaction (CI) based methods in computational chemistry, their broader application is limited by an exponential increase in both computational and storage requirements, particularly due to the substantial memory needed for excitation lists that are crucial for scalable parallel computing. Here, the objective of this work is to develop a new CI framework, namely, the small tensor product distributed active space (STP-DAS) framework, aimed at drastically reducing memory demands for extensive CI calculations on individual workstations or laptops, while simultaneously enhancing scalability for extensive parallel computing. Moreover, the STP-DAS framework can support various CI-based techniques, such as complete active space (CAS), restricted active space, generalized active space, multireference CI, and multireference perturbation theory, applicable to both relativistic (two- and four-component) and non-relativistic theories, thus extending the utility of CI methods in computational research. We conducted benchmark studies on a supercomputer to evaluate the storage needs, parallel scalability, and communication downtime using a realistic exact-two-component CASCI (X2C-CASCI) approach, covering a range of determinants from 10 9 to 10 12 . Additionally, we performed large X2C-CASCI calculations on a single laptop and examined how the STP-DAS partitioning affects performance.

Complete-active space self-consistent field↗

Collection of Disk Failure Events from Alpine, the Parallel File System for Summit Supercomputer

This dataset contains disk (HDD) failure events collected from the Alpine storage system of the Summit supercomputer, hosted at OLCF, spanning from January 4, 2019, to December 21, 2023 (a total of 4 years, 11 months, and 18 days), covering 89% of its operational lifetime. It includes 3,766 disk failure events, each recorded with its detection timestamp (in ISO 8601 format) and detailed by its location within the storage system - rack, enclosure, and drive slot number.

97 MATHEMATICS AND COMPUTING↗

Frontier (HPE Cray EX) Exascale Supercomputer at the Oak Ridge Leadership Computing Facility

Frontier is the HPE Cray EX exascale supercomputer deployed and operated by the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL). Frontier is designed for large-scale modeling, simulation, and AI workloads and is built from HPE Cray EX system architecture with AMD CPUs and AMD Instinct GPU accelerators connected by the HPE Slingshot interconnect. System composition (representative production configuration): Frontier is composed of approximately 74 cabinets with 128 compute nodes per cabinet (~9,400 compute nodes total). Each compute node contains one 64-core AMD EPYC CPU and four AMD Instinct MI250X GPUs. Nodes are connected using HPE Slingshot (Slingshot-200 class) networking with multiple NIC ports per node providing high injection bandwidth. Frontier is connected to the Orion parallel file system (multi-tier Lustre) providing a large, center-wide high-performance storage namespace. Operational context: Frontier entered public prominence as the first system to reach No. 1 on the TOP500 list in May 2022 (HPL benchmark), establishing the first widely recognized exascale-era performance milestone. The system supports DOE Office of Science mission workloads and enables leadership-class computational science and AI for open science users.

AMD EPYC↗

GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability

George Ostrouchov, Don Maxwell, Rizwan Ashraf, Mallikarjun Shankar, and James Rogers. 2020. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20). Association for Computing Machinery, New York, NY, USA. Data and code for SC20 paper about Titan GPU reliability analysis: https://github.com/olcf/TitanGPULife. Includes R code to generate graphics for paper and additional analyses. See code/README for instructions. Includes original Titan GPU reliability data on over 100,000 collective hours of operation: data/titan.gpu.history.txt - history data, data/titan.service.txt - service nodes for exclusion. Includes output data files produced by code/TitanGPUmodel.Rmd: data/gc_full.csv - cleaned up data (see paper and R code); data/gc_summary_loc.csv - one record per GPU (variables: SN, time, nlife, nloc, last, col, row, cage, slot, node, max_loc_events, time_max_loc, dbe, dbe_loc, otb, otb_loc, out, batch, days, years, dead, dead_otb, dead_dbe) (see paper and R code). Includes .Rmd analysis document as TitanGPUmode.html. Includes Python code to process data/gc_full.csv into graphics from time-between-failure analyses: See code/tbf-analyses/README for instructions.

42 ENGINEERING↗

Providing Thermal Stability for an Exascale Supercomputer: A Case Study of Frontier's Cooling System

High performance computing (HPC) systems frequently produce large dynamic power swings, even under typical operating conditions, that can present a significant challenge for their direct-liquid cooling systems. Further, the primary cooling loops that must remove this waste heat have response times measured in minutes while the underlying HPC component thermal stress is measured in seconds. The per-socket power demand for both compute processing units (CPUs) and graphic processing units ( GPUs) continues to increase with each successive generation while case temperatures are declining. New HPC systems are expected to exacerbate the challenge of these dynamic power swings and the impact on effective and timely cooling systems. This paper describes the cooling and controls system for Oak Ridge National Laboratory’s Frontier Supercomputer, the first sustained exascale system, as a case study for this situation. The cooling and control system for Frontier demonstrates specific success, but with a number of trade-offs and decisions that suggest further design and operating optimizations for the community at large to consider.

42 ENGINEERING↗

Performance Evaluation of an Intel Haswell- and Ivy Bridge-Based Supercomputer Using Scientific and Engineering Applications

We present a performance evaluation conducted on a production supercomputer of the Intel Xeon Processor E5- 2680v3, a twelve-core implementation of the fourth-generation Haswell architecture, and compare it with Intel Xeon Processor E5-2680v2, an Ivy Bridge implementation of the third-generation Sandy Bridge architecture. Several new architectural features have been incorporated in Haswell including improvements in all levels of the memory hierarchy as well as improvements to vector instructions and power management. We critically evaluate these new features of Haswell and compare with Ivy Bridge using several low-level benchmarks including subset of HPCC, HPCG and four full-scale scientific and engineering applications. We also present a model to predict the performance of HPCG and Cart3D within 5%, and Overflow within 10% accuracy.

Ivy Bridge↗

Performance of the Widely-Used CFD Code OVERFLOW on the Pleides Supercomputer

Computational performance studies were made for NASA's widely used Computational Fluid Dynamics code OVERFLOW on the Pleiades Supercomputer. Two test cases were considered: a full launch vehicle with a grid of 286 million points and a full rotorcraft model with a grid of 614 million points. Computations using up to 8000 cores were run on Sandy Bridge and Ivy Bridge nodes. Performance was monitored using times reported in the day files from the Portable Batch System utility. Results for two grid topologies are presented and compared in detail. Observations and suggestions for future work are made.

Widely Used↗