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At least 163 records · Page 9

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↗

Advanced Computing Support at NASA Advanced Supercomputing (NAS) Division

NAS (NASA Advanced Supercomputing) Division: Advanced Computing: High-End Computing Capability (HECC) Project; Quantum Artificial Intelligence Lab (QuAIL); NASA Earth Exchange (NEX); Data Analytics Infrastructure for NASA; Cloud Services for Science & Engineering Projects.

Advanced Computing↗

Dense LU Factorization on Multicore Supercomputer Nodes

Dense LU factorization is a prominent benchmark used to rank the performance of supercomputers. Many implementations, including the reference code HPL, use block-cyclic distributions of matrix blocks onto a two-dimensional process grid. The process grid dimensions drive a trade-off between communication and computation and are architecture- and implementation-sensitive. We show how the critical panel factorization steps can be made less communication-bound by overlapping asynchronous collectives for pivot identification and exchange with the computation of rank-k updates. By shifting this trade-off, a modified block-cyclic distribution can beneficially exploit more available parallelism on the critical path, and reduce panel factorization's memory hierarchy contention on now-ubiquitous multi-core architectures. The missed parallelism in traditional block-cyclic distributions arises because active panel factorization, triangular solves, and subsequent broadcasts are spread over single process columns or rows (respectively) of the process grid. Increasing one dimension of the process grid decreases the number of distinct processes in the other dimension. To increase parallelism in both dimensions, periodic 'rotation' is applied to the process grid to recover the row-parallelism lost by a tall process grid. During active panel factorization, rank-1 updates stream through memory with minimal reuse. In a column-major process grid, the performance of this access pattern degrades as too many streaming processors contend for access to memory. A block-cyclic mapping in the more popular row-major order does not encounter this problem, but consequently sacrifices node and network locality in the critical pivoting steps. We introduce 'striding' to vary between the two extremes of row- and column-major process grids. As a test-bed for further mapping experiments, we describe a dense LU implementation that allows a block distribution to be defined as a general function of block to processor. Other mappings can be tested with only small, local changes to the code.

99 GENERAL AND MISCELLANEOUS↗

PFLOTRAN: Reactive Flow & Transport Code for Use on Laptops to Leadership-Class Supercomputers

PFLOTRAN, a next-generation reactive flow and transport code for modeling subsurface processes, has been designed from the ground up to run efficiently on machines ranging from leadership-class supercomputers to laptops. Based on an object-oriented design, the code is easily extensible to incorporate additional processes. It can interface seamlessly with Fortran 9X, C and C++ codes. Domain decomposition parallelism is employed, with the PETSc parallel framework used to manage parallel solvers, data structures and communication. Features of the code include a modular input file, implementation of high-performance I/O using parallel HDF5, ability to perform multiple realization simulations with multiple processors per realization in a seamless manner, and multiple modes for multiphase flow and multicomponent geochemical transport. Chemical reactions currently implemented in the code include homogeneous aqueous complexing reactions and heterogeneous mineral precipitation/dissolution, ion exchange, surface complexation and a multirate kinetic sorption model. PFLOTRAN has demonstrated petascale performance using 2{sup 17} processor cores with over 2 billion degrees of freedom. Accomplishments achieved to date include applications to the Hanford 300 Area and modeling CO{sub 2} sequestration in deep geologic formations.

58 GEOSCIENCES↗

Introducing Mira, Argonne's Next-Generation Supercomputer

Mira, the new petascale IBM Blue Gene/Q system installed at the ALCF, will usher in a new era of scientific supercomputing. An engineering marvel, the 10-petaflops machine is capable of carrying out 10 quadrillion calculations per second.

97 MATHEMATICS AND COMPUTING↗

Supercomputing and the Search for Supernovae

Berkeley Lab's Peter Nugent discusses "Supercomputing and the search for supernovae" in this Oct. 28, 2013 talk, which is part of a Science at the Theater event entitled Eight Big Ideas.

79 ASTRONOMY AND ASTROPHYSICS↗

#AskBerkeleyLab: What's a Supercomputer?

Berkeley Lab computational scientist and Head of NERSC's Services Department, Katie Antypas, answers a question from Alameda resident, Marina, on supercomputers.

97 MATHEMATICS AND COMPUTING↗

Energy Efficient Supercomputing

Katie Anypas, Head of NERSC's Services Department discusses the Lab's research into developing increasingly powerful and energy efficient supercomputers at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.

97 MATHEMATICS AND COMPUTING↗

Supercomputer Simulations Help Develop New Approach to Fight Antibiotic Resistance

ORNL leveraged powerful supercomputing to support research led by University of Oklahoma scientists to identify chemicals that seek out and disrupt bacterial proteins called efflux pumps, known to be a major cause of antibiotic resistance. By running simulations on Titan, the team selected molecules most likely to target and potentially disable the assembly of efflux pumps found in E. coli bacteria cells.

47 OTHER INSTRUMENTATION↗